Clinically applicable deep learning for diagnosis and referral in retinal disease
The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of...
Uložené v:
| Vydané v: | Nature medicine Ročník 24; číslo 9; s. 1342 - 1350 |
|---|---|
| Hlavní autori: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York
Nature Publishing Group US
01.09.2018
Nature Publishing Group |
| Predmet: | |
| ISSN: | 1078-8956, 1546-170X, 1546-170X |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting.
A novel deep learning architecture performs device-independent tissue segmentation of clinical 3D retinal images followed by separate diagnostic classification that meets or exceeds human expert clinical diagnoses of retinal disease. |
|---|---|
| AbstractList | The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting.
A novel deep learning architecture performs device-independent tissue segmentation of clinical 3D retinal images followed by separate diagnostic classification that meets or exceeds human expert clinical diagnoses of retinal disease. The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting. The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting.The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting. The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting. A novel deep learning architecture performs device-independent tissue segmentation of clinical 3D retinal images followed by separate diagnostic classification that meets or exceeds human expert clinical diagnoses of retinal disease. |
| Audience | Academic |
| Author | Nikolov, Stanislav Ayoub, Kareem Ledsam, Joseph R. Hughes, Julian Raine, Rosalind Tufail, Adnan Blackwell, Sam van den Driessche, George Askham, Harry Chopra, Reena Back, Trevor Lakshminarayanan, Balaji Suleyman, Mustafa Hughes, Cían O. Rees, Geraint Glorot, Xavier Visentin, Daniel Hassabis, Demis Tomasev, Nenad Karthikesalingam, Alan Cornebise, Julien Ronneberger, Olaf O’Donoghue, Brendan Mackinder, Faith De Fauw, Jeffrey King, Dominic Romera-Paredes, Bernardino Bouton, Simon Khaw, Peng T. Sim, Dawn A. Meyer, Clemens Keane, Pearse A. Montgomery, Hugh Egan, Catherine |
| Author_xml | – sequence: 1 givenname: Jeffrey surname: De Fauw fullname: De Fauw, Jeffrey organization: DeepMind – sequence: 2 givenname: Joseph R. surname: Ledsam fullname: Ledsam, Joseph R. organization: DeepMind – sequence: 3 givenname: Bernardino surname: Romera-Paredes fullname: Romera-Paredes, Bernardino organization: DeepMind – sequence: 4 givenname: Stanislav surname: Nikolov fullname: Nikolov, Stanislav organization: DeepMind – sequence: 5 givenname: Nenad surname: Tomasev fullname: Tomasev, Nenad organization: DeepMind – sequence: 6 givenname: Sam surname: Blackwell fullname: Blackwell, Sam organization: DeepMind – sequence: 7 givenname: Harry surname: Askham fullname: Askham, Harry organization: DeepMind – sequence: 8 givenname: Xavier surname: Glorot fullname: Glorot, Xavier organization: DeepMind – sequence: 9 givenname: Brendan surname: O’Donoghue fullname: O’Donoghue, Brendan organization: DeepMind – sequence: 10 givenname: Daniel surname: Visentin fullname: Visentin, Daniel organization: DeepMind – sequence: 11 givenname: George surname: van den Driessche fullname: van den Driessche, George organization: DeepMind – sequence: 12 givenname: Balaji surname: Lakshminarayanan fullname: Lakshminarayanan, Balaji organization: DeepMind – sequence: 13 givenname: Clemens surname: Meyer fullname: Meyer, Clemens organization: DeepMind – sequence: 14 givenname: Faith surname: Mackinder fullname: Mackinder, Faith organization: DeepMind – sequence: 15 givenname: Simon surname: Bouton fullname: Bouton, Simon organization: DeepMind – sequence: 16 givenname: Kareem surname: Ayoub fullname: Ayoub, Kareem organization: DeepMind – sequence: 17 givenname: Reena orcidid: 0000-0002-4264-8329 surname: Chopra fullname: Chopra, Reena organization: NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology – sequence: 18 givenname: Dominic surname: King fullname: King, Dominic organization: DeepMind – sequence: 19 givenname: Alan surname: Karthikesalingam fullname: Karthikesalingam, Alan organization: DeepMind – sequence: 20 givenname: Cían O. orcidid: 0000-0001-6901-0985 surname: Hughes fullname: Hughes, Cían O. organization: DeepMind, University College London – sequence: 21 givenname: Rosalind surname: Raine fullname: Raine, Rosalind organization: University College London – sequence: 22 givenname: Julian surname: Hughes fullname: Hughes, Julian organization: NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology – sequence: 23 givenname: Dawn A. surname: Sim fullname: Sim, Dawn A. organization: NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology – sequence: 24 givenname: Catherine surname: Egan fullname: Egan, Catherine organization: NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology – sequence: 25 givenname: Adnan surname: Tufail fullname: Tufail, Adnan organization: NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology – sequence: 26 givenname: Hugh orcidid: 0000-0001-8797-5019 surname: Montgomery fullname: Montgomery, Hugh organization: University College London – sequence: 27 givenname: Demis surname: Hassabis fullname: Hassabis, Demis organization: DeepMind – sequence: 28 givenname: Geraint orcidid: 0000-0002-9623-7007 surname: Rees fullname: Rees, Geraint organization: University College London – sequence: 29 givenname: Trevor surname: Back fullname: Back, Trevor organization: DeepMind – sequence: 30 givenname: Peng T. surname: Khaw fullname: Khaw, Peng T. organization: NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology – sequence: 31 givenname: Mustafa surname: Suleyman fullname: Suleyman, Mustafa organization: DeepMind – sequence: 32 givenname: Julien surname: Cornebise fullname: Cornebise, Julien organization: DeepMind, University College London – sequence: 33 givenname: Pearse A. orcidid: 0000-0002-9239-745X surname: Keane fullname: Keane, Pearse A. email: pearse.keane@moorfields.nhs.uk organization: NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology – sequence: 34 givenname: Olaf orcidid: 0000-0002-4266-1515 surname: Ronneberger fullname: Ronneberger, Olaf email: olafr@deepmind.com organization: DeepMind |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30104768$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkltrFTEUhYNU7EV_gC8yIIg-TM1tksxjOXgpFIpaxLeQmdkzTcnJHJMZsP_ePZyWespRJIRswrcW2dnrmBzEMQIhLxk9ZVSY91myqmYlZQY31aV6Qo5YJVXJNP1xgDXVpjR1pQ7Jcc43lFJBq_oZORSIS63MEfmyCj761oVwW7jNJmDZBCg6gE0RwKXo41D0Yyo674Y4Zp8LF7siQQ8puVD4iPXkI5adz-AyPCdPexcyvLg7T8jVxw9Xq8_lxeWn89XZRdkqyaeyNkq6RptemUrXvBNUaKm1VLITLXe9EFIq7ZyBnnFhhKOV4HXTaAV1z5k4IW-3tps0_pwhT3btcwshuAjjnC2nxvBaYJeIvn6E3oxzwicjxSg1FZWifqAGF8D62I9Tcu1ias8qzWsqlaiQKvdQA0TA38Dp9B6vd_jTPTyuDta-3St4tyNAZoJf0-DmnO35t6__z15-32Xf_MFegwvTdR7DPPkx5l3w1d1vzc0aOrtJfu3Srb3PDAJ6C7RpzBmTYFs_ucUHW_PBMmqXdNptOi2m0y7ptMsg2CPlvfm_NHyrycjGAdLD8P4u-g2Ozu9Q |
| CitedBy_id | crossref_primary_10_2196_10010 crossref_primary_10_1186_s12880_021_00551_1 crossref_primary_10_1007_s13167_019_00188_9 crossref_primary_10_1136_bmjinnov_2020_000424 crossref_primary_10_1002_jhm_13371 crossref_primary_10_1111_dme_13856 crossref_primary_10_1016_j_berh_2019_101429 crossref_primary_10_1097_ICU_0000000000000593 crossref_primary_10_1007_s40135_020_00240_2 crossref_primary_10_1088_1361_6560_ab652d crossref_primary_10_12688_f1000research_52031_1 crossref_primary_10_1016_j_preteyeres_2022_101160 crossref_primary_10_3389_fpubh_2020_00117 crossref_primary_10_1016_j_media_2021_102130 crossref_primary_10_1111_ans_16343 crossref_primary_10_1016_j_ebiom_2020_103121 crossref_primary_10_1016_j_fertnstert_2020_09_156 crossref_primary_10_1681_ASN_2019070664 crossref_primary_10_1038_s41591_020_1037_7 crossref_primary_10_3390_jcm12113746 crossref_primary_10_1109_ACCESS_2023_3289397 crossref_primary_10_1016_j_bone_2020_115561 crossref_primary_10_1109_JBHI_2020_2983126 crossref_primary_10_1038_s41598_023_49563_7 crossref_primary_10_1007_s10728_021_00430_4 crossref_primary_10_1016_j_media_2024_103296 crossref_primary_10_1016_j_media_2025_103596 crossref_primary_10_1038_s41467_021_24464_3 crossref_primary_10_1007_s10162_022_00846_2 crossref_primary_10_3390_biomedicines10030551 crossref_primary_10_1049_sfw2_6006074 crossref_primary_10_3389_fmed_2022_891369 crossref_primary_10_3390_app14125314 crossref_primary_10_1155_2019_1691064 crossref_primary_10_1038_s42256_018_0014_z crossref_primary_10_3390_jcm10225284 crossref_primary_10_1109_ACCESS_2024_3369913 crossref_primary_10_1109_ACCESS_2020_3017449 crossref_primary_10_1016_j_media_2021_102118 crossref_primary_10_1002_jbio_202000271 crossref_primary_10_1038_s41467_025_60577_9 crossref_primary_10_22608_APO_2018496 crossref_primary_10_1016_j_media_2022_102677 crossref_primary_10_1016_j_media_2023_102953 crossref_primary_10_1038_s41746_021_00544_y crossref_primary_10_7717_peerj_8052 crossref_primary_10_1038_s41591_018_0316_z crossref_primary_10_1007_s00530_020_00722_0 crossref_primary_10_1007_s00439_019_01970_5 crossref_primary_10_4239_wjd_v13_i10_822 crossref_primary_10_3389_fmed_2021_654696 crossref_primary_10_3390_diagnostics10060430 crossref_primary_10_3390_biomedicines10061269 crossref_primary_10_3389_fnins_2021_696853 crossref_primary_10_1016_j_cmpb_2025_108705 crossref_primary_10_1038_s41598_021_94093_9 crossref_primary_10_1111_joor_13585 crossref_primary_10_1371_journal_pone_0220063 crossref_primary_10_1186_s12886_020_01783_5 crossref_primary_10_1007_s10278_024_01190_y crossref_primary_10_1109_JPROC_2019_2943836 crossref_primary_10_1016_j_xops_2024_100662 crossref_primary_10_1136_bjophthalmol_2020_316882 crossref_primary_10_3748_wjg_v27_i26_4088 crossref_primary_10_1016_j_media_2025_103575 crossref_primary_10_1016_j_modpat_2023_100381 crossref_primary_10_1109_TMI_2021_3140140 crossref_primary_10_3389_fphar_2020_01319 crossref_primary_10_1016_j_xops_2025_100710 crossref_primary_10_1038_s41598_020_76513_4 crossref_primary_10_1016_j_foodcont_2024_110947 crossref_primary_10_1186_s40001_025_02680_7 crossref_primary_10_1016_j_media_2019_101570 crossref_primary_10_1080_19932820_2022_2034334 crossref_primary_10_1136_bmjqs_2018_008370 crossref_primary_10_1111_aos_14895 crossref_primary_10_3389_fped_2022_809523 crossref_primary_10_1109_JBHI_2021_3135838 crossref_primary_10_1038_s41467_019_08718_9 crossref_primary_10_1590_1983_803420243739es crossref_primary_10_1002_aisy_202200184 crossref_primary_10_1016_j_cmpb_2021_105969 crossref_primary_10_2196_37532 crossref_primary_10_3389_fmed_2024_1489139 crossref_primary_10_1038_s41377_019_0203_5 crossref_primary_10_1007_s00261_019_02306_7 crossref_primary_10_32604_cmc_2021_012955 crossref_primary_10_1016_j_cosrev_2023_100568 crossref_primary_10_1136_bjophthalmol_2019_315025 crossref_primary_10_1136_bjophthalmol_2021_319365 crossref_primary_10_1002_cjoc_202100591 crossref_primary_10_1016_j_knosys_2025_113333 crossref_primary_10_1016_j_resuscitation_2021_02_041 crossref_primary_10_1109_ACCESS_2019_2929365 crossref_primary_10_1016_j_eururo_2020_04_023 crossref_primary_10_1109_ACCESS_2024_3363165 crossref_primary_10_1136_bjophthalmol_2021_319129 crossref_primary_10_1136_annrheumdis_2019_216636 crossref_primary_10_1038_s41746_025_01726_8 crossref_primary_10_1186_s40537_024_00989_4 crossref_primary_10_1126_science_aba9647 crossref_primary_10_1016_j_isci_2024_110579 crossref_primary_10_1016_j_tips_2019_05_005 crossref_primary_10_1038_s41598_024_68489_2 crossref_primary_10_1109_LSP_2019_2917779 crossref_primary_10_1136_bjophthalmol_2019_315016 crossref_primary_10_1109_JSEN_2020_2985131 crossref_primary_10_3390_jcm9082537 crossref_primary_10_1016_j_mpdhp_2020_08_004 crossref_primary_10_1080_00051144_2021_1973298 crossref_primary_10_1136_bjophthalmol_2019_314161 crossref_primary_10_1016_j_jdiacomp_2022_108392 crossref_primary_10_1016_j_ajo_2020_01_032 crossref_primary_10_1109_RBME_2024_3496744 crossref_primary_10_1016_j_ogla_2020_04_008 crossref_primary_10_1111_all_15667 crossref_primary_10_1136_bjo_2023_324488 crossref_primary_10_7759_cureus_16392 crossref_primary_10_1016_j_media_2024_103098 crossref_primary_10_1109_JBHI_2023_3277789 crossref_primary_10_1016_S2213_2600_20_30003_5 crossref_primary_10_1038_s41467_021_25858_z crossref_primary_10_1016_j_ejps_2020_105489 crossref_primary_10_1590_1983_803420243739en crossref_primary_10_1080_08820538_2021_1901123 crossref_primary_10_1016_j_artmed_2023_102570 crossref_primary_10_1016_j_neunet_2020_05_005 crossref_primary_10_1136_bmj_m3164 crossref_primary_10_3390_diagnostics14010105 crossref_primary_10_1007_s11082_023_05038_7 crossref_primary_10_1109_ACCESS_2024_3354826 crossref_primary_10_1016_j_immuni_2025_04_013 crossref_primary_10_1016_j_compmedimag_2024_102452 crossref_primary_10_1038_s41598_024_72481_1 crossref_primary_10_1097_APO_0000000000000619 crossref_primary_10_1136_bjophthalmol_2021_319587 crossref_primary_10_3390_jcm12020400 crossref_primary_10_1002_acn3_51320 crossref_primary_10_1038_d41586_019_01112_x crossref_primary_10_1038_s41598_024_75898_w crossref_primary_10_1109_JIOT_2022_3232793 crossref_primary_10_1002_spe_3408 crossref_primary_10_1016_j_ophtha_2020_09_025 crossref_primary_10_1016_j_xcrm_2024_101876 crossref_primary_10_1097_WCO_0000000000000645 crossref_primary_10_1016_j_jvs_2019_12_027 crossref_primary_10_1038_s41598_024_78515_y crossref_primary_10_1016_j_xops_2025_100907 crossref_primary_10_1109_TSMC_2021_3096974 crossref_primary_10_1155_2022_1614838 crossref_primary_10_1016_j_compbiomed_2019_103327 crossref_primary_10_3390_ph18030284 crossref_primary_10_1016_j_compmedimag_2022_102086 crossref_primary_10_1051_itmconf_20224403007 crossref_primary_10_3389_fmed_2022_853261 crossref_primary_10_1109_TBME_2018_2887208 crossref_primary_10_1097_ICU_0000000000001035 crossref_primary_10_1109_TBME_2023_3290541 crossref_primary_10_2196_48892 crossref_primary_10_1088_1361_6560_ab6f99 crossref_primary_10_1109_TASE_2020_2981637 crossref_primary_10_3389_fneur_2020_00450 crossref_primary_10_1007_s11517_022_02627_8 crossref_primary_10_1038_s41433_019_0510_3 crossref_primary_10_1007_s00146_020_01008_9 crossref_primary_10_3389_fradi_2025_1608052 crossref_primary_10_1002_cam4_4441 crossref_primary_10_1111_aos_15133 crossref_primary_10_1001_jamaophthalmol_2024_1165 crossref_primary_10_1038_s41746_021_00431_6 crossref_primary_10_1038_s41551_023_01049_7 crossref_primary_10_3390_smartcities5020036 crossref_primary_10_1109_TMI_2019_2919951 crossref_primary_10_1016_j_jacr_2019_01_026 crossref_primary_10_1080_01691864_2019_1689168 crossref_primary_10_1186_s13244_020_00929_9 crossref_primary_10_1080_08164622_2022_2081068 crossref_primary_10_1186_s12890_023_02537_x crossref_primary_10_1016_j_retram_2020_01_002 crossref_primary_10_1109_ACCESS_2020_3044646 crossref_primary_10_34248_bsengineering_858918 crossref_primary_10_3390_biomedicines10051082 crossref_primary_10_1109_TSC_2023_3332744 crossref_primary_10_1002_ima_22808 crossref_primary_10_1038_s41598_021_01227_0 crossref_primary_10_1111_aos_15126 crossref_primary_10_1002_INMD_20240063 crossref_primary_10_1007_s11517_025_03286_1 crossref_primary_10_1016_j_compbiomed_2024_109256 crossref_primary_10_1080_0142159X_2019_1595557 crossref_primary_10_1002_lsm_23093 crossref_primary_10_1109_RBME_2021_3131358 crossref_primary_10_1515_mopp_2020_0046 crossref_primary_10_1016_j_mri_2019_07_004 crossref_primary_10_1111_resp_70056 crossref_primary_10_1038_s41746_024_01037_4 crossref_primary_10_1109_TMI_2023_3331301 crossref_primary_10_1186_s13054_018_2301_9 crossref_primary_10_1007_s00417_020_04869_4 crossref_primary_10_7759_cureus_70056 crossref_primary_10_3390_ijms241210327 crossref_primary_10_1183_13993003_01809_2020 crossref_primary_10_1136_bmjopen_2022_069181 crossref_primary_10_1038_s41598_025_14944_7 crossref_primary_10_1038_s41598_022_27211_w crossref_primary_10_1016_j_knosys_2023_110520 crossref_primary_10_1371_journal_pdig_0000016 crossref_primary_10_1371_journal_pone_0280316 crossref_primary_10_3389_fimmu_2021_630022 crossref_primary_10_1117_1_JMI_9_6_064001 crossref_primary_10_1097_SAP_0000000000004128 crossref_primary_10_1016_j_survophthal_2019_02_003 crossref_primary_10_1007_s00129_020_04563_w crossref_primary_10_1093_tse_tdac073 crossref_primary_10_3390_biomedicines9070720 crossref_primary_10_1016_j_bspc_2024_106283 crossref_primary_10_1016_j_est_2024_114928 crossref_primary_10_1016_j_ajo_2020_01_016 crossref_primary_10_1038_s41746_019_0122_0 crossref_primary_10_1016_j_jbo_2024_100626 crossref_primary_10_1109_JBHI_2022_3147512 crossref_primary_10_1136_bmjresp_2023_002226 crossref_primary_10_1016_j_techfore_2020_120323 crossref_primary_10_1002_smll_202405301 crossref_primary_10_1016_j_compbiomed_2022_105319 crossref_primary_10_1186_s12992_025_01128_1 crossref_primary_10_1148_radiol_233197 crossref_primary_10_47164_ijngc_v12i5_454 crossref_primary_10_1038_s41467_020_18147_8 crossref_primary_10_3389_fnins_2022_801769 crossref_primary_10_1186_s12916_019_1426_2 crossref_primary_10_1038_s41433_021_01795_5 crossref_primary_10_1016_j_ijhcs_2019_06_007 crossref_primary_10_1167_iovs_65_1_41 crossref_primary_10_3390_jcm11030614 crossref_primary_10_1002_cam4_3796 crossref_primary_10_1002_mp_15541 crossref_primary_10_1136_thoraxjnl_2020_214556 crossref_primary_10_1097_ICU_0000000000001088 crossref_primary_10_1016_j_ndteint_2022_102703 crossref_primary_10_1038_s41433_018_0324_8 crossref_primary_10_1038_s41591_021_01384_9 crossref_primary_10_1016_j_bspc_2023_104625 crossref_primary_10_1038_s41746_021_00411_w crossref_primary_10_1016_j_canlet_2020_03_032 crossref_primary_10_5662_wjm_v15_i4_107166 crossref_primary_10_1007_s00417_025_06790_0 crossref_primary_10_1007_s11892_019_1189_3 crossref_primary_10_1007_s12553_020_00468_9 crossref_primary_10_1109_JBHI_2020_3012134 crossref_primary_10_1166_jmihi_2021_3309 crossref_primary_10_1016_j_yebeh_2025_110470 crossref_primary_10_1109_JBHI_2020_2982914 crossref_primary_10_1097_ICU_0000000000001091 crossref_primary_10_1002_cnm_3303 crossref_primary_10_1364_AO_455626 crossref_primary_10_1136_bjo_2023_324647 crossref_primary_10_3928_23258160_20220817_01 crossref_primary_10_1038_s42256_024_00883_x crossref_primary_10_1371_journal_pone_0235765 crossref_primary_10_3390_jpm11111161 crossref_primary_10_1136_bjophthalmol_2019_315651 crossref_primary_10_1111_ijn_12725 crossref_primary_10_1016_j_compmedimag_2021_101997 crossref_primary_10_1038_s41433_024_03460_z crossref_primary_10_2196_43386 crossref_primary_10_2478_ausi_2020_0007 crossref_primary_10_1136_bmj_m363 crossref_primary_10_1016_j_cmpb_2019_06_016 crossref_primary_10_2196_14808 crossref_primary_10_1016_j_media_2024_103259 crossref_primary_10_1016_j_preteyeres_2025_101358 crossref_primary_10_1038_s41598_022_12497_7 crossref_primary_10_1186_s12880_021_00703_3 crossref_primary_10_1038_s41587_022_01226_0 crossref_primary_10_1093_brain_awaa361 crossref_primary_10_1111_risa_13850 crossref_primary_10_3390_diagnostics12123167 crossref_primary_10_1136_bjo_2023_323308 crossref_primary_10_1016_j_optlastec_2020_106861 crossref_primary_10_1063_5_0177640 crossref_primary_10_1109_TMI_2020_2973595 crossref_primary_10_1097_IAE_0000000000002992 crossref_primary_10_1016_j_oret_2022_05_020 crossref_primary_10_1167_iovs_63_2_27 crossref_primary_10_1093_brain_awz235 crossref_primary_10_1038_s41746_025_01959_7 crossref_primary_10_1016_j_cmpb_2020_105855 crossref_primary_10_1007_s11192_021_03936_9 crossref_primary_10_1186_s12938_022_01025_3 crossref_primary_10_3389_fpubh_2022_1019168 crossref_primary_10_1016_j_jbi_2024_104766 crossref_primary_10_4018_JGIM_371436 crossref_primary_10_3390_diagnostics13020326 crossref_primary_10_1007_s40070_020_00116_7 crossref_primary_10_1080_09273948_2022_2032197 crossref_primary_10_1002_smtd_202300285 crossref_primary_10_1038_s41467_020_15432_4 crossref_primary_10_1186_s12880_021_00660_x crossref_primary_10_1371_journal_pmed_1002709 crossref_primary_10_1053_j_ajkd_2019_05_020 crossref_primary_10_3390_diagnostics15101221 crossref_primary_10_1038_s41598_022_26170_6 crossref_primary_10_1186_s12859_020_3503_0 crossref_primary_10_3390_app12073341 crossref_primary_10_62517_jmhs_202505107 crossref_primary_10_4103_JOCO_JOCO_123_20 crossref_primary_10_1007_s00347_020_01063_z crossref_primary_10_12968_opti_2020_11_232126 crossref_primary_10_1016_j_semcancer_2023_06_003 crossref_primary_10_3390_jcm14176307 crossref_primary_10_1016_j_compbiomed_2025_110236 crossref_primary_10_3389_fgene_2022_1045450 crossref_primary_10_1109_JIOT_2020_2991456 crossref_primary_10_1109_TRPMS_2024_3474708 crossref_primary_10_3233_JAD_210714 crossref_primary_10_3917_rpre_223_0091 crossref_primary_10_1038_s41598_023_33021_5 crossref_primary_10_3390_app12146872 crossref_primary_10_1016_j_identj_2022_03_001 crossref_primary_10_1038_s41598_019_54961_x crossref_primary_10_3390_technologies12120235 crossref_primary_10_1177_25158414221083363 crossref_primary_10_1016_j_knosys_2023_111107 crossref_primary_10_1016_j_preteyeres_2019_07_003 crossref_primary_10_1016_j_ophtha_2024_06_006 crossref_primary_10_1017_jfm_2021_812 crossref_primary_10_1038_s41598_020_72359_y crossref_primary_10_1016_j_ndteint_2025_103392 crossref_primary_10_1186_s12886_022_02299_w crossref_primary_10_1136_bmj_l886 crossref_primary_10_1007_s00146_024_01893_4 crossref_primary_10_1016_j_optom_2022_06_006 crossref_primary_10_3389_fonc_2022_813806 crossref_primary_10_1016_j_optom_2022_06_005 crossref_primary_10_1109_ACCESS_2024_3451304 crossref_primary_10_1007_s11517_021_02364_4 crossref_primary_10_1016_j_cmpb_2020_105919 crossref_primary_10_1016_j_media_2020_101798 crossref_primary_10_1136_emermed_2020_210776 crossref_primary_10_1145_3448108 crossref_primary_10_1093_jamia_ocaa341 crossref_primary_10_3928_23258160_20230303_01 crossref_primary_10_1136_bjophthalmol_2018_313290 crossref_primary_10_2196_26151 crossref_primary_10_4103_hjo_HJO_5_23 crossref_primary_10_1109_TMI_2023_3260990 crossref_primary_10_1155_2022_5054932 crossref_primary_10_1155_2020_8858344 crossref_primary_10_1038_s41598_022_17615_z crossref_primary_10_3390_app14156638 crossref_primary_10_1001_jamaophthalmol_2018_6156 crossref_primary_10_1038_s41598_024_78917_y crossref_primary_10_1016_j_radonc_2024_110332 crossref_primary_10_1016_j_preteyeres_2024_101291 crossref_primary_10_1007_s00417_021_05427_2 crossref_primary_10_3390_cancers13153891 crossref_primary_10_3390_electronics12030750 crossref_primary_10_1155_2022_9152605 crossref_primary_10_1371_journal_pmed_1002721 crossref_primary_10_1002_alz_13390 crossref_primary_10_1097_APO_0000000000000583 crossref_primary_10_1038_s41598_025_96988_3 crossref_primary_10_1016_j_jtcvs_2019_03_111 crossref_primary_10_1007_s00417_021_05503_7 crossref_primary_10_1038_s41433_022_02154_8 crossref_primary_10_1038_s41598_019_42042_y crossref_primary_10_1038_s41563_019_0345_0 crossref_primary_10_1038_s41433_023_02765_9 crossref_primary_10_1117_1_NPh_6_4_041110 crossref_primary_10_3389_fcell_2025_1665173 crossref_primary_10_1007_s10586_025_05281_5 crossref_primary_10_1155_2021_8876957 crossref_primary_10_2196_28114 crossref_primary_10_1159_000527345 crossref_primary_10_26599_BDMA_2024_9020067 crossref_primary_10_1016_j_media_2020_101773 crossref_primary_10_1016_j_ajoms_2022_02_004 crossref_primary_10_1097_ACM_0000000000003733 crossref_primary_10_1136_bmjopen_2022_069443 crossref_primary_10_1016_j_tbench_2025_100215 crossref_primary_10_1002_mp_14415 crossref_primary_10_1017_S0033291720000835 crossref_primary_10_1371_journal_pone_0227240 crossref_primary_10_1038_s41746_025_01893_8 crossref_primary_10_1111_anae_15212 crossref_primary_10_1097_DCR_0000000000002570 crossref_primary_10_1016_j_compbiomed_2023_107873 crossref_primary_10_1016_j_rare_2024_100057 crossref_primary_10_1097_APO_0000000000000363 crossref_primary_10_1177_25158414251340569 crossref_primary_10_3390_diagnostics12061493 crossref_primary_10_3390_medicina61030433 crossref_primary_10_1055_s_0040_1719037 crossref_primary_10_1038_s41551_020_00626_4 crossref_primary_10_3390_jcm12093049 crossref_primary_10_1177_1062860619878515 crossref_primary_10_1002_mp_13764 crossref_primary_10_1016_j_engappai_2024_109439 crossref_primary_10_3389_frai_2023_1226990 crossref_primary_10_1002_dad2_70087 crossref_primary_10_1016_j_ecoinf_2020_101110 crossref_primary_10_3390_diagnostics12030742 crossref_primary_10_1016_j_nicl_2023_103320 crossref_primary_10_1016_j_yapd_2019_04_005 crossref_primary_10_1634_theoncologist_2019_0267 crossref_primary_10_3390_cancers13102382 crossref_primary_10_3390_s21196661 crossref_primary_10_1097_APO_0000000000000394 crossref_primary_10_1097_APO_0000000000000399 crossref_primary_10_1007_s00417_025_06828_3 crossref_primary_10_1016_j_bspc_2025_107971 crossref_primary_10_1016_j_survophthal_2024_03_008 crossref_primary_10_1038_s41467_019_14108_y crossref_primary_10_1016_j_compbiomed_2021_104822 crossref_primary_10_1038_s41598_020_66196_2 crossref_primary_10_2196_19416 crossref_primary_10_1016_j_ajo_2021_12_020 crossref_primary_10_1007_s00125_022_05727_0 crossref_primary_10_1038_s41598_020_80312_2 crossref_primary_10_1109_TIP_2022_3228163 crossref_primary_10_1038_s43856_025_00749_2 crossref_primary_10_1109_JIOT_2024_3463185 crossref_primary_10_3390_jimaging7060092 crossref_primary_10_1016_j_matpr_2021_07_269 crossref_primary_10_1371_journal_pone_0296229 crossref_primary_10_1038_s41746_020_0254_2 crossref_primary_10_1108_RPJ_08_2019_0213 crossref_primary_10_2147_OPTH_S261629 crossref_primary_10_1038_s41598_023_43018_9 crossref_primary_10_1016_j_eswa_2024_124888 crossref_primary_10_1016_j_ress_2025_111311 crossref_primary_10_1136_medethics_2019_105586 crossref_primary_10_1136_bmjophth_2020_000671 crossref_primary_10_1186_s12911_023_02103_9 crossref_primary_10_1002_jmri_28427 crossref_primary_10_1155_2020_8889023 crossref_primary_10_3390_math12193003 crossref_primary_10_3390_jcm12010152 crossref_primary_10_1055_s_0041_1742180 crossref_primary_10_1371_journal_pcbi_1006269 crossref_primary_10_1016_j_bspc_2025_107519 crossref_primary_10_1016_j_displa_2025_103204 crossref_primary_10_1177_0840470419859017 crossref_primary_10_1016_j_media_2024_103404 crossref_primary_10_1038_s41598_024_60682_7 crossref_primary_10_1097_ICU_0000000000000979 crossref_primary_10_1186_s13195_022_00983_z crossref_primary_10_1007_s40123_019_00207_y crossref_primary_10_1007_s42452_025_06565_6 crossref_primary_10_3390_medsci11010020 crossref_primary_10_3389_fnagi_2020_603790 crossref_primary_10_3390_vision6020021 crossref_primary_10_1145_3580804 crossref_primary_10_1038_s41598_019_49740_7 crossref_primary_10_1097_ICU_0000000000000986 crossref_primary_10_1097_ICU_0000000000000980 crossref_primary_10_1186_s40662_025_00451_3 crossref_primary_10_1038_s41598_019_56420_z crossref_primary_10_1111_aos_14055 crossref_primary_10_1111_aos_15141 crossref_primary_10_1007_s13347_020_00435_2 crossref_primary_10_1016_j_ebiom_2023_104541 crossref_primary_10_3389_fnagi_2025_1586282 crossref_primary_10_3390_cancers14112590 crossref_primary_10_1016_j_inffus_2024_102631 crossref_primary_10_1016_j_media_2020_101724 crossref_primary_10_1108_JIC_12_2023_0286 crossref_primary_10_1016_j_survophthal_2024_06_006 crossref_primary_10_1109_JMMCT_2023_3260900 crossref_primary_10_18502_jovr_v18i1_12730 crossref_primary_10_1109_TMI_2022_3202183 crossref_primary_10_7326_ANNALS_24_03283 crossref_primary_10_1109_ACCESS_2020_3004766 crossref_primary_10_1002_jor_24849 crossref_primary_10_1002_14651858_CD016114 crossref_primary_10_1038_s41598_024_55410_0 crossref_primary_10_1148_ryai_2019180091 crossref_primary_10_1136_bmj_l6927 crossref_primary_10_1371_journal_pone_0225015 crossref_primary_10_1016_j_media_2020_101714 crossref_primary_10_1007_s42452_020_2560_3 crossref_primary_10_1016_j_neucom_2020_06_122 crossref_primary_10_1109_ACCESS_2019_2944692 crossref_primary_10_32604_cmc_2022_023919 crossref_primary_10_1016_j_compmedimag_2022_102068 crossref_primary_10_1016_j_cmpb_2024_108253 crossref_primary_10_1007_s12553_023_00763_1 crossref_primary_10_1016_j_newideapsych_2024_101074 crossref_primary_10_3389_fnins_2019_01128 crossref_primary_10_1007_s11042_022_13617_1 crossref_primary_10_1097_j_jcrs_0000000000001538 crossref_primary_10_1038_s41467_021_20966_2 crossref_primary_10_1136_bmjopen_2022_060810 crossref_primary_10_1038_s41467_019_13647_8 crossref_primary_10_1038_s41598_020_69814_1 crossref_primary_10_1016_j_inffus_2024_102877 crossref_primary_10_1016_j_gie_2021_08_022 crossref_primary_10_1038_s41591_019_0508_1 crossref_primary_10_3390_s22218454 crossref_primary_10_1001_jamaophthalmol_2019_4782 crossref_primary_10_1016_j_media_2022_102364 crossref_primary_10_1007_s11633_022_1382_8 crossref_primary_10_1148_ryai_2021210031 crossref_primary_10_1038_s41433_021_01503_3 crossref_primary_10_1109_JBHI_2023_3312154 crossref_primary_10_1016_j_media_2020_101942 crossref_primary_10_3389_fonc_2021_788740 crossref_primary_10_1002_eng2_12864 crossref_primary_10_1016_j_canlet_2025_217438 crossref_primary_10_1371_journal_pone_0251553 crossref_primary_10_1080_03080188_2020_1840223 crossref_primary_10_1136_bmjopen_2024_084574 crossref_primary_10_1016_S1470_2045_19_30739_9 crossref_primary_10_1136_bjophthalmol_2020_316594 crossref_primary_10_1016_j_media_2022_102391 crossref_primary_10_1080_02713683_2022_2124275 crossref_primary_10_1016_j_jor_2022_05_013 crossref_primary_10_1007_s12647_023_00633_1 crossref_primary_10_1038_s41598_020_73831_5 crossref_primary_10_1016_j_ophtha_2022_01_002 crossref_primary_10_1145_3551635 crossref_primary_10_1155_2021_5549779 crossref_primary_10_3390_cancers15030843 crossref_primary_10_1080_0142159X_2019_1679361 crossref_primary_10_1136_bmjinnov_2020_000494 crossref_primary_10_7326_M19_2548 crossref_primary_10_3390_ph17111440 crossref_primary_10_1016_j_acra_2019_10_001 crossref_primary_10_1038_s41467_024_53387_y crossref_primary_10_1148_ryai_2020190217 crossref_primary_10_1186_s12859_019_2952_9 crossref_primary_10_1371_journal_pone_0271156 crossref_primary_10_3389_fmed_2024_1481088 crossref_primary_10_1002_jmri_28456 crossref_primary_10_1016_j_apjo_2024_100096 crossref_primary_10_1097_ICU_0000000000000780 crossref_primary_10_1007_s12559_024_10297_x crossref_primary_10_1007_s00417_020_05010_1 crossref_primary_10_1038_s41598_025_98518_7 crossref_primary_10_3233_JIFS_190861 crossref_primary_10_1136_bjophthalmol_2020_318544 crossref_primary_10_1097_ICU_0000000000000782 crossref_primary_10_1080_14737175_2021_1982697 crossref_primary_10_1007_s00347_024_02146_x crossref_primary_10_1016_j_talanta_2024_126441 crossref_primary_10_1159_000531823 crossref_primary_10_3390_app10186428 crossref_primary_10_1109_JTEHM_2023_3294904 crossref_primary_10_1155_2022_6361098 crossref_primary_10_1001_jamadermatol_2019_3807 crossref_primary_10_1007_s00347_021_01385_6 crossref_primary_10_1109_ACCESS_2025_3583647 crossref_primary_10_1007_s00371_023_03189_y crossref_primary_10_1007_s10278_025_01513_7 crossref_primary_10_1016_j_smrv_2021_101512 crossref_primary_10_1016_j_ejrad_2019_108649 crossref_primary_10_1016_j_pdpdt_2022_103107 crossref_primary_10_1016_j_media_2021_102054 crossref_primary_10_1038_s41524_019_0262_4 crossref_primary_10_1016_j_ophtha_2019_12_015 crossref_primary_10_1109_JBHI_2025_3556717 crossref_primary_10_3389_fneur_2021_584270 crossref_primary_10_1097_APO_0000000000000313 crossref_primary_10_1007_s11673_023_10320_0 crossref_primary_10_1093_jmp_jhac034 crossref_primary_10_1097_APO_0000000000000304 crossref_primary_10_1038_s41591_018_0307_0 crossref_primary_10_1001_jamanetworkopen_2020_5842 crossref_primary_10_1038_s41433_022_02376_w crossref_primary_10_1016_j_clinph_2022_07_003 crossref_primary_10_1097_coa_0000000000000069 crossref_primary_10_1111_ceo_13435 crossref_primary_10_1287_mnsc_2023_01845 crossref_primary_10_1016_j_jtcvs_2021_02_010 crossref_primary_10_1016_j_neucom_2021_08_051 crossref_primary_10_1016_j_fsidi_2022_301403 crossref_primary_10_1136_bmjopen_2020_037269 crossref_primary_10_1016_j_ijrobp_2024_11_064 crossref_primary_10_1167_iovs_18_25325 crossref_primary_10_1016_j_jds_2023_10_025 crossref_primary_10_1097_APO_0000000000000301 crossref_primary_10_3390_diagnostics13050975 crossref_primary_10_1097_SAP_0000000000002434 crossref_primary_10_1016_j_media_2021_102038 crossref_primary_10_1109_JAS_2023_123123 crossref_primary_10_1016_j_media_2022_102359 crossref_primary_10_1177_2515841419827172 crossref_primary_10_1007_s00417_022_05653_2 crossref_primary_10_1371_journal_pone_0260471 crossref_primary_10_1097_ICO_0000000000003626 crossref_primary_10_1016_j_media_2020_101905 crossref_primary_10_1016_j_cmpb_2025_108586 crossref_primary_10_3389_fmed_2025_1617537 crossref_primary_10_1007_s00347_020_01111_8 crossref_primary_10_1016_j_bbe_2022_05_005 crossref_primary_10_1109_TMI_2022_3142048 crossref_primary_10_1016_j_media_2021_102270 crossref_primary_10_3917_jibes_342_0111 crossref_primary_10_1016_j_compbiomed_2023_107027 crossref_primary_10_1080_08164622_2021_2022961 crossref_primary_10_1038_s41586_019_1799_6 crossref_primary_10_1109_TBME_2022_3188173 crossref_primary_10_1590_1983_803420243739pt crossref_primary_10_1016_j_engappai_2025_112066 crossref_primary_10_1109_TMI_2019_2898414 crossref_primary_10_1167_iovs_66_1_7 crossref_primary_10_1007_s10506_020_09260_6 crossref_primary_10_1016_j_hpb_2024_12_016 crossref_primary_10_1007_s00371_024_03391_6 crossref_primary_10_1016_j_oret_2020_12_013 crossref_primary_10_1038_s41586_019_1390_1 crossref_primary_10_1007_s13755_020_00125_5 crossref_primary_10_1016_j_jhsa_2025_04_015 crossref_primary_10_1016_j_neucom_2025_130635 crossref_primary_10_1016_j_impact_2021_100296 crossref_primary_10_1097_APO_0000000000000576 crossref_primary_10_3389_fendo_2024_1385836 crossref_primary_10_1016_j_jvsvi_2025_100289 crossref_primary_10_1136_bmj_m3210 crossref_primary_10_1016_j_nicl_2019_102003 crossref_primary_10_1007_s12553_023_00806_7 crossref_primary_10_1002_ima_22662 crossref_primary_10_3390_bioengineering9080366 crossref_primary_10_7759_cureus_44658 crossref_primary_10_1016_j_compbiomed_2024_108659 crossref_primary_10_1097_IJG_0000000000002194 crossref_primary_10_1001_jamaophthalmol_2023_6454 crossref_primary_10_1038_s41598_023_46626_7 crossref_primary_10_5498_wjp_v15_i9_108359 crossref_primary_10_3390_jpm14070690 crossref_primary_10_1016_j_clon_2021_11_008 crossref_primary_10_1016_j_ajodo_2021_03_017 crossref_primary_10_1038_s41433_023_02680_z crossref_primary_10_1038_s41598_020_80001_0 crossref_primary_10_1111_bph_15633 crossref_primary_10_1007_s00103_025_04093_7 crossref_primary_10_1016_j_arr_2020_101094 crossref_primary_10_3390_s21124149 crossref_primary_10_1038_s41433_018_0251_8 crossref_primary_10_1109_LSENS_2019_2963712 crossref_primary_10_1007_s00330_025_11709_8 crossref_primary_10_1016_j_media_2019_05_002 crossref_primary_10_1088_1361_6560_acf2e2 crossref_primary_10_1039_c8pp00471d crossref_primary_10_2337_dc20_3064 crossref_primary_10_1109_ACCESS_2023_3347498 crossref_primary_10_1038_s41598_022_05006_3 crossref_primary_10_1016_j_cels_2020_04_003 crossref_primary_10_1007_s00330_023_09478_3 crossref_primary_10_1038_s41598_019_51363_x crossref_primary_10_1038_s41467_024_50911_y crossref_primary_10_1038_s41746_020_00350_y crossref_primary_10_1016_j_ejrad_2024_111356 crossref_primary_10_1038_s41746_025_01593_3 crossref_primary_10_1038_s42256_022_00559_4 crossref_primary_10_1016_j_urology_2021_09_027 crossref_primary_10_1145_3648005 crossref_primary_10_1371_journal_pdig_0000193 crossref_primary_10_4103_ijo_IJO_1569_22 crossref_primary_10_1136_bjophthalmol_2020_317825 crossref_primary_10_1016_j_ijmedinf_2020_104363 crossref_primary_10_1016_j_media_2021_102010 crossref_primary_10_1016_j_socscimed_2020_113172 crossref_primary_10_1038_s41433_023_02560_6 crossref_primary_10_1038_s41746_022_00716_4 crossref_primary_10_1016_j_artmed_2022_102344 crossref_primary_10_1039_D4TB02876G crossref_primary_10_1038_s41598_024_54482_2 crossref_primary_10_1172_JCI174951 crossref_primary_10_1073_pnas_1919012117 crossref_primary_10_3389_fonc_2022_854927 crossref_primary_10_1016_j_jns_2022_120454 crossref_primary_10_1080_01431161_2025_2528256 crossref_primary_10_1117_1_JEI_31_4_043016 crossref_primary_10_1145_3470979 crossref_primary_10_7717_peerj_18850 crossref_primary_10_1038_s41598_025_06462_3 crossref_primary_10_1136_bjophthalmol_2020_315890 crossref_primary_10_1136_bjophthalmol_2020_316984 crossref_primary_10_1080_10255842_2020_1742709 crossref_primary_10_1088_2631_8695_adbc48 crossref_primary_10_1080_17469899_2023_2175672 crossref_primary_10_1111_cyt_12835 crossref_primary_10_1016_j_oret_2018_12_008 crossref_primary_10_1088_2057_1976_ac154f crossref_primary_10_1186_s40942_023_00502_8 crossref_primary_10_1109_TIP_2022_3148814 crossref_primary_10_1364_AO_409414 crossref_primary_10_1016_j_ophtha_2019_06_005 crossref_primary_10_1016_j_semcancer_2019_05_007 crossref_primary_10_1080_09273948_2022_2054433 crossref_primary_10_1016_j_ophtha_2020_02_009 crossref_primary_10_1097_CM9_0000000000001816 crossref_primary_10_1016_j_ccr_2021_214111 crossref_primary_10_3390_life12020279 crossref_primary_10_1016_j_jds_2024_08_020 crossref_primary_10_1002_advs_202002021 crossref_primary_10_1016_j_ophtha_2020_03_010 crossref_primary_10_1016_j_renene_2024_120935 crossref_primary_10_1016_j_preteyeres_2019_04_003 crossref_primary_10_1016_j_procs_2023_03_139 crossref_primary_10_1136_bmjopen_2022_065537 crossref_primary_10_1016_j_inffus_2021_01_008 crossref_primary_10_1038_s41551_020_0578_x crossref_primary_10_5694_mja2_50932 crossref_primary_10_2196_24799 crossref_primary_10_1007_s10278_024_01212_9 crossref_primary_10_1016_j_survophthal_2021_03_003 crossref_primary_10_1038_s41591_025_03953_8 crossref_primary_10_1038_s41573_020_00117_w crossref_primary_10_1016_j_cie_2023_109337 crossref_primary_10_1038_s41598_023_28347_z crossref_primary_10_1109_TMI_2022_3204551 crossref_primary_10_1038_s41467_023_41215_8 crossref_primary_10_1007_s11655_022_3541_8 crossref_primary_10_1016_j_exer_2022_109092 crossref_primary_10_1159_000501887 crossref_primary_10_3348_kjr_2022_1032 crossref_primary_10_1038_s41598_022_05903_7 crossref_primary_10_1016_j_media_2024_103395 crossref_primary_10_1038_s41598_021_81554_4 crossref_primary_10_1109_ACCESS_2021_3108395 crossref_primary_10_1007_s42044_025_00290_4 crossref_primary_10_1055_s_0042_1758687 crossref_primary_10_1038_s41746_025_01749_1 crossref_primary_10_4103_ijo_IJO_143_22 crossref_primary_10_1007_s11432_019_2708_1 crossref_primary_10_1016_j_cmpb_2020_105566 crossref_primary_10_57125_FEM_2025_09_30_01 crossref_primary_10_1038_s41598_023_35491_z crossref_primary_10_1016_j_eswa_2020_113562 crossref_primary_10_1038_s41746_021_00438_z crossref_primary_10_1097_IAE_0000000000003535 crossref_primary_10_3389_fmed_2025_1530361 crossref_primary_10_1093_bib_bbaa237 crossref_primary_10_1007_s00535_024_02102_1 crossref_primary_10_1186_s12913_023_10098_2 crossref_primary_10_1161_STROKEAHA_123_044026 crossref_primary_10_1016_j_survophthal_2025_09_014 crossref_primary_10_1080_14737159_2023_2208751 crossref_primary_10_3390_s19081866 crossref_primary_10_1038_s41572_021_00265_2 crossref_primary_10_1089_pop_2019_0132 crossref_primary_10_1371_journal_pone_0290278 crossref_primary_10_1016_j_compbiomed_2025_111060 crossref_primary_10_1109_ACCESS_2023_3337658 crossref_primary_10_3389_fmed_2022_958469 crossref_primary_10_1109_LSP_2020_3000933 crossref_primary_10_1136_bjo_2024_326603 crossref_primary_10_1038_s41598_021_88543_7 crossref_primary_10_1007_s11548_020_02286_w crossref_primary_10_1371_journal_pone_0253091 crossref_primary_10_1016_j_rec_2020_06_036 crossref_primary_10_1038_s41591_022_02134_1 crossref_primary_10_12688_f1000research_26997_1 crossref_primary_10_12688_f1000research_26997_2 crossref_primary_10_1155_2021_5592472 crossref_primary_10_1007_s00417_023_06052_x crossref_primary_10_1007_s40593_019_00188_w crossref_primary_10_3390_diagnostics12071714 crossref_primary_10_3390_biomedicines11102633 crossref_primary_10_1016_j_oret_2024_01_013 crossref_primary_10_1016_j_oret_2021_12_021 crossref_primary_10_1063_5_0172226 crossref_primary_10_1007_s11831_023_09927_8 crossref_primary_10_1038_s41598_023_35414_y crossref_primary_10_1038_s41467_025_62123_z crossref_primary_10_1097_IAE_0000000000003557 crossref_primary_10_1016_j_zemedi_2018_11_002 crossref_primary_10_1002_lsm_23578 crossref_primary_10_1111_bjd_19553 crossref_primary_10_1001_jamanetworkopen_2020_0206 crossref_primary_10_1287_isre_2021_1002 crossref_primary_10_1007_s11042_024_18701_2 crossref_primary_10_1109_JBHI_2022_3171523 crossref_primary_10_1371_journal_pone_0260622 crossref_primary_10_1016_j_preteyeres_2021_100972 crossref_primary_10_1016_j_scriptamat_2020_10_026 crossref_primary_10_1007_s10676_022_09658_7 crossref_primary_10_1016_j_compbiomed_2024_108013 crossref_primary_10_1093_bmb_ldaa012 crossref_primary_10_3348_jksr_2020_0150 crossref_primary_10_1073_pnas_1908021116 crossref_primary_10_4103_tjo_tjo_8_22 crossref_primary_10_1186_s12880_024_01470_7 crossref_primary_10_3389_fonc_2021_785788 crossref_primary_10_1109_ACCESS_2025_3596786 crossref_primary_10_4103_sjopt_sjopt_219_21 crossref_primary_10_1016_j_heliyon_2024_e25655 crossref_primary_10_1073_pnas_2020620118 crossref_primary_10_1109_TNSRE_2022_3190467 crossref_primary_10_1111_tbj_13804 crossref_primary_10_1038_s41433_023_02705_7 crossref_primary_10_1097_IAE_0000000000003325 crossref_primary_10_1136_bmjophth_2019_000347 crossref_primary_10_1136_medethics_2020_107166 crossref_primary_10_1007_s40120_019_00153_8 crossref_primary_10_1212_WNL_0000000000201350 crossref_primary_10_1109_ACCESS_2019_2929258 crossref_primary_10_1007_s12561_024_09434_9 crossref_primary_10_1038_s41746_019_0189_7 crossref_primary_10_26633_RPSP_2024_12 crossref_primary_10_26633_RPSP_2024_13 crossref_primary_10_1038_s42256_020_00247_1 crossref_primary_10_1038_s41598_023_27418_5 crossref_primary_10_1038_s41746_019_0112_2 crossref_primary_10_1109_JSEN_2020_3014254 crossref_primary_10_1038_s41433_023_02805_4 crossref_primary_10_1038_s41598_020_80308_y crossref_primary_10_12968_opti_2020_5_8273 crossref_primary_10_1007_s40123_018_0153_7 crossref_primary_10_1016_j_bspc_2025_107544 crossref_primary_10_1016_j_compbiomed_2022_106512 crossref_primary_10_1016_j_neucom_2019_08_079 crossref_primary_10_1136_bmjophth_2024_002037 crossref_primary_10_1016_j_ijmedinf_2022_104762 crossref_primary_10_3389_fnins_2021_818604 crossref_primary_10_1038_s41467_020_19527_w crossref_primary_10_1109_ACCESS_2020_3019332 crossref_primary_10_1016_j_media_2024_103104 crossref_primary_10_1109_ACCESS_2020_3034731 crossref_primary_10_1007_s10815_020_01950_z crossref_primary_10_1038_s41591_020_1034_x crossref_primary_10_1007_s00417_019_04338_7 crossref_primary_10_1016_j_preteyeres_2021_100976 crossref_primary_10_1007_s11220_020_00299_y crossref_primary_10_1007_s11831_025_10347_z crossref_primary_10_54097_ydgagy87 crossref_primary_10_1097_IAE_0000000000003990 crossref_primary_10_1038_s41596_021_00513_5 crossref_primary_10_1038_s41569_019_0205_2 crossref_primary_10_1097_ICU_0000000000001172 crossref_primary_10_1016_j_biopsycho_2021_108117 crossref_primary_10_1038_s41591_024_02838_6 crossref_primary_10_1371_journal_pdig_0000106 crossref_primary_10_1038_s41592_020_01008_z crossref_primary_10_1007_s00330_020_07062_7 crossref_primary_10_1007_s00500_020_05424_3 crossref_primary_10_1038_s41746_025_01849_y crossref_primary_10_1109_TMI_2021_3079709 crossref_primary_10_1161_CIRCRESAHA_121_318106 crossref_primary_10_1007_s13246_020_00925_9 crossref_primary_10_1136_bmjopen_2021_055845 crossref_primary_10_1186_s40662_022_00277_3 crossref_primary_10_3390_wevj14120341 crossref_primary_10_1007_s10462_024_11067_9 crossref_primary_10_3390_ijms21134802 crossref_primary_10_1007_s00347_020_01209_z crossref_primary_10_7861_clinmed_2020_0834 crossref_primary_10_1038_s41746_020_00317_z crossref_primary_10_1111_jep_13510 crossref_primary_10_1016_j_ejim_2023_07_012 crossref_primary_10_1007_s13167_022_00301_5 crossref_primary_10_18359_rcin_4242 crossref_primary_10_3390_diagnostics12102314 crossref_primary_10_1038_s41433_019_0566_0 crossref_primary_10_1093_clinchem_hvac027 crossref_primary_10_3389_fmed_2022_794045 crossref_primary_10_1007_s00261_019_02028_w crossref_primary_10_1016_j_media_2024_103319 crossref_primary_10_1148_radiol_230806 crossref_primary_10_1186_s13054_023_04320_0 crossref_primary_10_1007_s41939_024_00612_2 crossref_primary_10_1155_2019_6319581 crossref_primary_10_1016_j_ophtha_2019_09_036 crossref_primary_10_1016_j_ultrasmedbio_2019_10_003 crossref_primary_10_1007_s10489_022_04118_7 crossref_primary_10_1093_jamiaopen_ooae035 crossref_primary_10_3390_diagnostics12102549 crossref_primary_10_3390_jcm13010197 crossref_primary_10_1038_s41433_023_02609_6 crossref_primary_10_1007_s11684_021_0828_7 crossref_primary_10_12688_f1000research_36775_1 crossref_primary_10_1016_j_compbiomed_2020_103721 crossref_primary_10_1016_j_oret_2019_01_011 crossref_primary_10_1038_s41433_025_04025_4 crossref_primary_10_1038_s41598_024_72375_2 crossref_primary_10_1109_ACCESS_2020_2971281 crossref_primary_10_1016_j_artmed_2025_103096 crossref_primary_10_1007_s10462_025_11153_6 crossref_primary_10_1109_TPAMI_2024_3435571 crossref_primary_10_1155_2020_7493419 crossref_primary_10_1038_s41598_021_89743_x crossref_primary_10_1186_s40942_024_00549_1 crossref_primary_10_1016_j_neuroscience_2021_11_017 crossref_primary_10_1063_5_0058346 crossref_primary_10_1016_j_pdpdt_2025_104788 crossref_primary_10_1111_medu_13958 crossref_primary_10_1177_15209156251369886 crossref_primary_10_1016_j_ijheatfluidflow_2021_108816 crossref_primary_10_1016_j_phrs_2020_104986 crossref_primary_10_1093_qjmed_hcz106 crossref_primary_10_1177_24741264241247602 crossref_primary_10_1016_j_spinee_2023_05_009 crossref_primary_10_7717_peerj_cs_715 crossref_primary_10_1016_j_ophtha_2019_09_014 crossref_primary_10_1089_omi_2019_0038 crossref_primary_10_3390_bioengineering12050441 crossref_primary_10_1038_s41551_021_00711_2 crossref_primary_10_1111_aos_14353 crossref_primary_10_3390_biom9110673 crossref_primary_10_1038_s41467_022_33407_5 crossref_primary_10_1088_2057_1976_ad5db2 crossref_primary_10_1007_s13347_019_00345_y crossref_primary_10_1038_s41598_020_78696_2 crossref_primary_10_3389_frai_2020_550890 crossref_primary_10_1177_00220345241226871 crossref_primary_10_1002_ima_70035 crossref_primary_10_1038_s41598_020_80766_4 crossref_primary_10_1016_j_engappai_2025_111891 crossref_primary_10_3390_diagnostics15111365 crossref_primary_10_1097_MAO_0000000000003210 crossref_primary_10_3390_healthcare11020212 crossref_primary_10_3389_fdgth_2020_569178 crossref_primary_10_1097_IAE_0000000000002640 crossref_primary_10_1097_JCMA_0000000000000374 crossref_primary_10_1016_j_jtos_2021_11_004 crossref_primary_10_1038_s41746_025_01607_0 crossref_primary_10_1145_3571815 crossref_primary_10_1016_j_media_2024_103357 crossref_primary_10_1186_s40942_024_00580_2 crossref_primary_10_1038_s41598_025_85777_7 crossref_primary_10_26599_TST_2022_9010012 crossref_primary_10_1136_bjophthalmol_2020_317817 crossref_primary_10_1007_s13347_022_00505_7 crossref_primary_10_4103_IJO_IJO_1456_23 crossref_primary_10_1016_j_media_2020_101695 crossref_primary_10_62762_TIS_2024_100001 crossref_primary_10_1016_j_ajo_2021_04_021 crossref_primary_10_18502_jovr_v19i3_15893 crossref_primary_10_1016_j_joco_2019_05_001 crossref_primary_10_1038_s41591_023_02640_w crossref_primary_10_1016_j_ijrobp_2022_06_083 crossref_primary_10_1080_08164622_2021_2008791 crossref_primary_10_1007_s10796_021_10142_8 crossref_primary_10_1056_NEJMra1814259 crossref_primary_10_1097_WNO_0000000000001682 crossref_primary_10_1109_TNNLS_2020_3004893 crossref_primary_10_1097_APO_0000000000000468 crossref_primary_10_1136_bjophthalmol_2018_313173 crossref_primary_10_1136_bmjhci_2024_101236 crossref_primary_10_2196_19739 crossref_primary_10_1016_j_ctro_2021_03_006 crossref_primary_10_1016_j_asoc_2020_106328 crossref_primary_10_1016_j_ejso_2025_110188 crossref_primary_10_1136_bmjopen_2019_033139 crossref_primary_10_1016_j_cmpb_2019_105101 crossref_primary_10_1038_s41467_022_31514_x crossref_primary_10_1213_ANE_0000000000007140 crossref_primary_10_1002_ima_70160 crossref_primary_10_1038_s41598_023_29293_6 crossref_primary_10_1109_JBHI_2020_2976150 crossref_primary_10_1038_s41379_020_0540_1 crossref_primary_10_1371_journal_pdig_0000864 crossref_primary_10_1007_s00542_020_04782_0 crossref_primary_10_1016_j_jcjo_2023_10_017 crossref_primary_10_1007_s13347_019_00391_6 crossref_primary_10_3389_fmed_2025_1567159 crossref_primary_10_1038_s41598_023_33046_w crossref_primary_10_1038_s42003_021_01697_y crossref_primary_10_1016_j_spinee_2021_03_028 crossref_primary_10_1007_s41649_025_00357_1 crossref_primary_10_1007_s10354_024_01069_1 crossref_primary_10_1016_j_cose_2023_103369 crossref_primary_10_2471_BLT_19_237487 crossref_primary_10_3390_bioengineering12080879 crossref_primary_10_1016_j_bspc_2024_106673 crossref_primary_10_1038_s41598_020_64126_w crossref_primary_10_1038_s41598_022_15424_y crossref_primary_10_1016_j_ajo_2020_12_034 crossref_primary_10_1007_s10586_025_05229_9 crossref_primary_10_1109_ACCESS_2023_3317011 crossref_primary_10_1007_s00464_020_08168_1 crossref_primary_10_1038_s41598_021_95545_y crossref_primary_10_1016_j_media_2020_101660 crossref_primary_10_1080_08820538_2021_1889617 crossref_primary_10_1002_jmri_28739 crossref_primary_10_1007_s11831_023_09899_9 crossref_primary_10_1136_bmjopen_2019_036423 crossref_primary_10_1016_S1474_4422_19_30397_7 crossref_primary_10_7861_fhj_10_3_s110 crossref_primary_10_3348_kjr_2023_0393 crossref_primary_10_1111_opo_13219 crossref_primary_10_1038_s41746_020_00323_1 crossref_primary_10_1136_bjophthalmol_2018_313156 crossref_primary_10_1038_s41598_021_92805_9 crossref_primary_10_1038_s41598_025_99278_0 crossref_primary_10_1016_j_msard_2023_104846 crossref_primary_10_31435_ijitss_3_47__2025_3533 crossref_primary_10_1016_j_heliyon_2024_e29775 crossref_primary_10_1080_21680566_2022_2064361 crossref_primary_10_3389_fonc_2020_00053 crossref_primary_10_1007_s00417_022_05565_1 crossref_primary_10_1016_j_neunet_2024_106409 crossref_primary_10_1002_int_22777 crossref_primary_10_1049_ipr2_12511 crossref_primary_10_1097_IIO_0000000000000519 crossref_primary_10_1038_s41551_021_00753_6 crossref_primary_10_1016_j_ajo_2024_02_012 crossref_primary_10_1038_s41467_024_44824_z crossref_primary_10_1109_JBHI_2020_2983730 crossref_primary_10_1038_s41746_019_0097_x crossref_primary_10_1111_joim_13030 crossref_primary_10_1186_s13244_025_01922_w crossref_primary_10_1109_ACCESS_2024_3360691 crossref_primary_10_1007_s10462_024_10736_z crossref_primary_10_1038_s42256_021_00305_2 crossref_primary_10_1001_jamaophthalmol_2021_2273 crossref_primary_10_1007_s11282_023_00715_5 crossref_primary_10_1186_s12871_022_01937_6 crossref_primary_10_1016_j_kint_2020_01_039 crossref_primary_10_1016_j_yexcr_2022_113278 crossref_primary_10_1038_s43856_022_00146_z crossref_primary_10_1016_j_bspc_2024_106263 crossref_primary_10_1007_s00371_024_03383_6 crossref_primary_10_1016_j_isprsjprs_2019_12_014 crossref_primary_10_1177_11206721221096294 crossref_primary_10_1038_s43856_022_00220_6 crossref_primary_10_1155_2019_4061313 crossref_primary_10_1038_s41598_021_92829_1 crossref_primary_10_1038_s41746_020_0288_5 crossref_primary_10_3390_diagnostics13020189 crossref_primary_10_1038_s41433_024_03264_1 crossref_primary_10_1371_journal_pgen_1009497 crossref_primary_10_3390_bioengineering10121394 crossref_primary_10_2147_JMDH_S485724 crossref_primary_10_1016_j_jcmg_2019_06_009 crossref_primary_10_1136_bjophthalmol_2019_314729 crossref_primary_10_1007_s42979_022_01024_0 crossref_primary_10_1167_iovs_66_6_55 crossref_primary_10_1007_s12539_025_00763_x crossref_primary_10_1016_j_media_2020_101871 crossref_primary_10_1097_ICU_0000000000000846 crossref_primary_10_1016_j_knosys_2021_106771 crossref_primary_10_1016_j_aopr_2025_07_004 crossref_primary_10_1111_epi_16333 crossref_primary_10_1093_cid_ciy969 crossref_primary_10_1038_s41433_022_02077_4 crossref_primary_10_1038_s41746_021_00507_3 crossref_primary_10_1155_2022_6952999 crossref_primary_10_1002_ase_2266 crossref_primary_10_1038_s41433_019_0577_x crossref_primary_10_1111_ceo_14258 crossref_primary_10_1038_s41591_018_0178_4 crossref_primary_10_1109_JTEHM_2021_3050925 crossref_primary_10_1007_s00417_021_05078_3 crossref_primary_10_1007_s11517_021_02321_1 crossref_primary_10_1016_j_heliyon_2023_e19229 crossref_primary_10_1002_mds_28775 crossref_primary_10_1007_s10278_022_00666_z crossref_primary_10_1016_j_oret_2021_01_009 crossref_primary_10_1016_j_survophthal_2022_08_005 crossref_primary_10_1038_s41467_023_38104_5 crossref_primary_10_1007_s00417_020_05035_6 crossref_primary_10_1186_s12245_025_00870_y crossref_primary_10_1038_s41467_022_34245_1 crossref_primary_10_1002_mp_13891 crossref_primary_10_1136_bmj_m428 crossref_primary_10_1016_j_cmpb_2022_107312 crossref_primary_10_1109_TNNLS_2021_3105384 crossref_primary_10_1016_j_ejrad_2020_109410 crossref_primary_10_1007_s10439_025_03813_z crossref_primary_10_3390_jpm14101062 crossref_primary_10_1038_s41433_021_01653_4 crossref_primary_10_1155_2023_8629849 crossref_primary_10_1177_2474126420914168 crossref_primary_10_1016_j_ccell_2023_06_008 crossref_primary_10_1038_s41591_020_0867_7 crossref_primary_10_3928_23258160_20220215_02 crossref_primary_10_1001_jamaophthalmol_2020_2769 crossref_primary_10_1002_mds_27671 crossref_primary_10_1038_s41746_019_0105_1 crossref_primary_10_1038_s41433_022_02360_4 crossref_primary_10_32604_cmc_2021_012874 crossref_primary_10_1007_s00347_020_01210_6 crossref_primary_10_1016_j_preteyeres_2020_100900 crossref_primary_10_1093_rheumatology_kez068 crossref_primary_10_1186_s40662_024_00405_1 crossref_primary_10_1097_IIO_0000000000000334 crossref_primary_10_1038_s41598_023_27479_6 crossref_primary_10_1016_j_ophtha_2018_11_015 crossref_primary_10_22608_APO_2018438 crossref_primary_10_1016_j_jid_2020_01_019 crossref_primary_10_7759_s44389_025_03919_4 crossref_primary_10_1038_d41586_019_01111_y crossref_primary_10_1038_s41746_020_00376_2 crossref_primary_10_1371_journal_pone_0227791 crossref_primary_10_3389_frai_2025_1522730 crossref_primary_10_1002_adfm_202002791 crossref_primary_10_1016_j_compbiomed_2023_107569 crossref_primary_10_1097_IIO_0000000000000569 crossref_primary_10_1109_TBME_2019_2943062 crossref_primary_10_1051_fopen_2019024 crossref_primary_10_1631_jzus_B2000343 crossref_primary_10_3389_fsurg_2024_1424716 crossref_primary_10_1016_j_compbiomed_2019_103346 crossref_primary_10_1080_08820538_2020_1748203 crossref_primary_10_2174_012212697X315512240821045542 crossref_primary_10_1080_20565623_2025_2540742 crossref_primary_10_1186_s40537_021_00444_8 crossref_primary_10_1038_s41598_022_22392_w crossref_primary_10_3389_fmed_2022_932498 crossref_primary_10_1038_s41598_023_27980_y crossref_primary_10_1097_ICU_0000000000000885 crossref_primary_10_1007_s00500_023_08212_x crossref_primary_10_1097_ICU_0000000000000887 crossref_primary_10_1007_s12553_019_00375_8 crossref_primary_10_3389_fphar_2020_572372 crossref_primary_10_1016_j_ebiom_2025_105773 crossref_primary_10_1016_j_preteyeres_2021_101034 crossref_primary_10_1016_j_zemedi_2018_12_003 crossref_primary_10_3389_fimmu_2022_862702 crossref_primary_10_1038_s41598_025_89961_7 crossref_primary_10_1016_j_cogsys_2020_11_002 crossref_primary_10_1259_bjr_20220878 crossref_primary_10_1016_j_cct_2021_106364 crossref_primary_10_1038_s41591_025_03968_1 crossref_primary_10_1038_s41598_023_46747_z crossref_primary_10_1177_1120672120975331 crossref_primary_10_1007_s11768_023_00151_0 crossref_primary_10_1080_02713683_2023_2212878 crossref_primary_10_1016_j_preteyeres_2019_100797 crossref_primary_10_1186_s40662_020_00183_6 crossref_primary_10_1007_s40123_023_00842_6 crossref_primary_10_1097_ICU_0000000000000656 crossref_primary_10_3390_electronics11193229 crossref_primary_10_1016_j_cbpa_2019_10_003 crossref_primary_10_1002_lsm_23414 crossref_primary_10_1111_ceo_14607 crossref_primary_10_3389_fgene_2021_607471 crossref_primary_10_1016_j_compeleceng_2021_107036 crossref_primary_10_1016_j_ophtha_2020_08_031 crossref_primary_10_1111_risa_14273 crossref_primary_10_1016_j_preteyeres_2024_101305 crossref_primary_10_1136_bmjinnov_2019_000359 crossref_primary_10_7861_clinmed_2020_0030 crossref_primary_10_1109_ACCESS_2019_2919390 crossref_primary_10_1097_AUD_0000000000000794 crossref_primary_10_1016_j_diabet_2018_08_012 crossref_primary_10_3389_fped_2019_00043 crossref_primary_10_1146_annurev_biodatasci_102423_113220 crossref_primary_10_1097_APO_0000000000000403 crossref_primary_10_1016_j_bspc_2024_105990 crossref_primary_10_1097_APO_0000000000000405 crossref_primary_10_1038_s41598_021_91634_0 crossref_primary_10_1109_JBHI_2020_2983549 crossref_primary_10_1097_APO_0000000000000406 crossref_primary_10_1038_s43856_024_00518_7 crossref_primary_10_1016_j_pop_2025_07_005 crossref_primary_10_1016_S2213_2600_18_30286_8 crossref_primary_10_3390_jcm14155266 crossref_primary_10_1038_s41597_024_04259_z crossref_primary_10_1007_s10606_025_09514_4 crossref_primary_10_1016_j_compbiomed_2021_104726 crossref_primary_10_3390_cancers14164025 crossref_primary_10_1007_s00417_024_06625_4 crossref_primary_10_2196_28868 crossref_primary_10_3389_fendo_2022_1077669 crossref_primary_10_3390_diagnostics12030613 crossref_primary_10_3390_ai6020038 crossref_primary_10_1136_practneurol_2020_002688 crossref_primary_10_1109_ACCESS_2025_3583555 crossref_primary_10_3390_s20195611 crossref_primary_10_1093_nutrit_nuac033 crossref_primary_10_1016_j_eswa_2024_123496 crossref_primary_10_1016_j_irbm_2022_04_004 crossref_primary_10_3389_fonc_2022_821594 crossref_primary_10_1259_bjr_20220215 crossref_primary_10_3390_app10207292 crossref_primary_10_1002_jbio_202300274 crossref_primary_10_1007_s10815_021_02159_4 crossref_primary_10_1007_s40123_021_00430_6 crossref_primary_10_1287_moor_2021_0082 crossref_primary_10_3390_sym13010102 crossref_primary_10_1097_ICU_0000000000000677 crossref_primary_10_3390_rs13091789 crossref_primary_10_3389_fmed_2025_1580583 crossref_primary_10_1097_ICU_0000000000000679 crossref_primary_10_1038_s41698_021_00195_y crossref_primary_10_1016_j_ajo_2020_12_012 crossref_primary_10_3390_jpm14050501 crossref_primary_10_1016_j_imu_2024_101587 crossref_primary_10_1016_j_compbiomed_2025_110944 crossref_primary_10_1007_s10489_022_04012_2 crossref_primary_10_1097_WNO_0000000000001800 crossref_primary_10_1080_17434440_2020_1816167 crossref_primary_10_1038_s41598_021_97341_0 crossref_primary_10_3390_app11010371 crossref_primary_10_1097_ICU_0000000000000680 crossref_primary_10_1146_annurev_anchem_091520_091450 crossref_primary_10_3389_fonc_2021_631813 crossref_primary_10_1016_j_ophtha_2020_06_038 crossref_primary_10_1111_opo_13034 crossref_primary_10_1016_j_engappai_2023_106180 crossref_primary_10_3389_fpubh_2022_1025271 crossref_primary_10_3390_diagnostics11081399 crossref_primary_10_3389_fmed_2021_740987 crossref_primary_10_3389_fpubh_2023_1239231 crossref_primary_10_1016_j_acra_2019_06_018 crossref_primary_10_1109_LSP_2019_2949388 crossref_primary_10_1186_s12916_021_01940_7 crossref_primary_10_1016_j_media_2022_102684 crossref_primary_10_1136_bjo_2023_324091 crossref_primary_10_1371_journal_pone_0256087 crossref_primary_10_1038_s41467_020_17419_7 crossref_primary_10_1109_TETCI_2024_3444694 crossref_primary_10_1155_2020_6153657 crossref_primary_10_3390_jpm12050742 crossref_primary_10_1016_j_bbe_2022_12_005 crossref_primary_10_1016_j_preteyeres_2023_101219 crossref_primary_10_1038_s41746_021_00503_7 crossref_primary_10_1136_archdischild_2020_321023 crossref_primary_10_1016_j_acra_2023_03_036 crossref_primary_10_1097_ICU_0000000000000693 crossref_primary_10_4103_IJO_IJO_399_23 crossref_primary_10_1093_jamia_ocaf070 crossref_primary_10_1080_14737175_2019_1559051 crossref_primary_10_1097_ICU_0000000000000695 crossref_primary_10_1093_bib_bbad249 crossref_primary_10_1136_bmjopen_2021_055082 crossref_primary_10_1016_j_media_2021_102156 crossref_primary_10_1016_j_preteyeres_2023_101227 crossref_primary_10_1148_ryai_2021200183 crossref_primary_10_1186_s40942_024_00554_4 crossref_primary_10_1161_CIRCULATIONAHA_118_038451 crossref_primary_10_1167_iovs_65_2_2 crossref_primary_10_2147_MDER_S262590 crossref_primary_10_1016_j_accpm_2019_05_003 crossref_primary_10_1016_j_ejmp_2019_12_008 crossref_primary_10_3390_info10090266 crossref_primary_10_1080_14737167_2023_2279107 crossref_primary_10_1136_bjo_2024_325459 crossref_primary_10_1136_bjophthalmol_2020_317131 crossref_primary_10_1055_a_2543_4330 crossref_primary_10_1038_s41572_025_00646_x crossref_primary_10_1038_s42256_023_00617_5 crossref_primary_10_2217_fca_2020_0014 crossref_primary_10_1186_s40942_025_00695_0 crossref_primary_10_3389_fmed_2023_1259017 crossref_primary_10_1038_s41586_023_06555_x crossref_primary_10_2196_52506 crossref_primary_10_3389_fsurg_2022_862322 crossref_primary_10_3389_fonc_2024_1440626 crossref_primary_10_1371_journal_pone_0283214 crossref_primary_10_2215_CJN_09990819 crossref_primary_10_5114_pjr_2022_119808 crossref_primary_10_1001_jamaophthalmol_2019_3512 crossref_primary_10_1007_s13139_023_00821_6 |
| Cites_doi | 10.1364/BOE.8.000579 10.1126/science.1957169 10.1038/sj.eye.6703053 10.1016/j.ophtha.2017.10.031 10.1136/bjophthalmol-2011-301109 10.1364/BOE.8.003627 10.1016/j.ophtha.2014.04.020 10.1001/jama.2016.17216 10.1109/CVPR.2016.308 10.1364/BOE.8.002732 10.1016/j.oret.2016.12.009 10.1787/3c994537-en 10.1016/j.ophtha.2016.01.034 10.1007/s00417-010-1520-9 10.1167/iovs.17-21790 10.1007/978-3-319-24574-4_28 10.1016/j.sjopt.2011.01.003 10.1007/978-3-319-46723-8_49 10.1364/BOE.5.003568 10.1167/tvst.6.4.16 10.1038/eye.2017.1 10.1016/S2214-109X(17)30293-0 10.1038/eye.2016.227 10.1038/nmeth.2019 10.1089/tmj.2005.11.641 10.1016/j.ajo.2015.04.003 10.1787/1d89353f-en 10.1016/j.survophthal.2012.01.006 10.1038/538020a 10.1109/CVPR.2017.243 10.1016/j.preteyeres.2015.07.007 10.1038/nature21056 10.1038/eye.2017.114 10.1016/j.oret.2017.03.015 10.1364/BOE.8.003440 10.12688/f1000research.8996.1 10.1016/j.ophtha.2013.07.013 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2018 COPYRIGHT 2018 Nature Publishing Group Copyright Nature Publishing Group Sep 2018 |
| Copyright_xml | – notice: The Author(s) 2018 – notice: COPYRIGHT 2018 Nature Publishing Group – notice: Copyright Nature Publishing Group Sep 2018 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM IOV ISR 3V. 7QG 7QL 7QP 7QR 7T5 7TK 7TM 7TO 7U7 7U9 7X7 7XB 88A 88E 88I 8AO 8FD 8FE 8FH 8FI 8FJ 8FK 8G5 ABUWG AEUYN AFKRA AZQEC BBNVY BENPR BHPHI C1K CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ GUQSH H94 HCIFZ K9. LK8 M0S M1P M2O M2P M7N M7P MBDVC P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U RC3 7X8 |
| DOI | 10.1038/s41591-018-0107-6 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Opposing Viewpoints Gale In Context: Science ProQuest Central (Corporate) Animal Behavior Abstracts Bacteriology Abstracts (Microbiology B) Calcium & Calcified Tissue Abstracts Chemoreception Abstracts Immunology Abstracts Neurosciences Abstracts Nucleic Acids Abstracts Oncogenes and Growth Factors Abstracts Toxicology Abstracts Virology and AIDS Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Biology Database (Alumni Edition) Medical Database (Alumni Edition) Science Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Research Library ProQuest Central (Alumni) One Sustainability ProQuest Central UK/Ireland ProQuest Central Essentials - QC Biological Science Collection ProQuest Central Natural Science Collection Environmental Sciences and Pollution Management ProQuest One ProQuest Central Korea Engineering Research Database ProQuest Health & Medical Collection Health Research Premium Collection (Alumni) ProQuest Central Student ProQuest Research Library AIDS and Cancer Research Abstracts SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Biological Science Collection Health & Medical Collection (Alumni Edition) Medical Database ProQuest Research Library Science Database Algology Mycology and Protozoology Abstracts (Microbiology C) Biological Science Database Research Library (Corporate) Biotechnology and BioEngineering Abstracts Proquest Central Premium ProQuest One Academic (New) ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic Genetics Abstracts MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Research Library Prep ProQuest Central Student Oncogenes and Growth Factors Abstracts ProQuest Central Essentials Nucleic Acids Abstracts SciTech Premium Collection ProQuest Central China Environmental Sciences and Pollution Management ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection Chemoreception Abstracts ProQuest Central (New) ProQuest Medical Library (Alumni) Virology and AIDS Abstracts ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database Neurosciences Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic Calcium & Calcified Tissue Abstracts ProQuest One Academic (New) Technology Research Database ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing Research Library (Alumni Edition) ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Biology Journals (Alumni Edition) ProQuest Central ProQuest Health & Medical Research Collection Genetics Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) AIDS and Cancer Research Abstracts ProQuest Research Library ProQuest Central Basic Toxicology Abstracts ProQuest Science Journals ProQuest SciTech Collection ProQuest Medical Library Animal Behavior Abstracts Immunology Abstracts ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Research Library Prep MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Biology Architecture |
| EISSN | 1546-170X |
| EndPage | 1350 |
| ExternalDocumentID | A572904635 30104768 10_1038_s41591_018_0107_6 |
| Genre | Research Support, Non-U.S. Gov't Journal Article |
| GeographicLocations | United Kingdom |
| GeographicLocations_xml | – name: United Kingdom |
| GrantInformation_xml | – fundername: Department of Health grantid: CS-2014-14-023 |
| GroupedDBID | --- .-4 .55 .GJ 0R~ 123 1CY 29M 2FS 36B 39C 3O- 3V. 4.4 53G 5BI 5M7 5RE 5S5 70F 7X7 85S 88A 88E 88I 8AO 8FE 8FH 8FI 8FJ 8G5 8R4 8R5 AAEEF AARCD AAYOK AAYZH AAZLF ABAWZ ABCQX ABDBF ABDPE ABEFU ABJNI ABLJU ABOCM ABUWG ACBWK ACGFO ACGFS ACGOD ACIWK ACMJI ACPRK ACUHS ADBBV ADFRT AENEX AEUYN AFBBN AFKRA AFRAH AFSHS AGAYW AGCDD AGHTU AHBCP AHMBA AHOSX AHSBF AIBTJ ALFFA ALIPV ALMA_UNASSIGNED_HOLDINGS AMTXH ARMCB ASPBG AVWKF AXYYD AZFZN AZQEC B0M BBNVY BENPR BHPHI BKKNO BPHCQ BVXVI CCPQU CS3 DB5 DU5 DWQXO EAD EAP EBC EBD EBS EE. EJD EMB EMK EMOBN EPL ESX EXGXG F5P FEDTE FQGFK FSGXE FYUFA GNUQQ GUQSH GX1 HCIFZ HMCUK HVGLF HZ~ IAO IEA IH2 IHR IHW INH INR IOF IOV ISR ITC J5H L7B LGEZI LK8 LOTEE M0L M1P M2O M2P M7P MK0 N9A NADUK NNMJJ NXXTH O9- ODYON P2P PQQKQ PROAC PSQYO Q2X RIG RNS RNT RNTTT RVV SHXYY SIXXV SJN SNYQT SOJ SV3 TAE TAOOD TBHMF TDRGL TSG TUS UKHRP UQL X7M XJT YHZ ZGI ~8M AAYXX AFANA AFFHD AGSTI ALPWD ATHPR CITATION PHGZM PHGZT PJZUB PPXIY PQGLB XRW ABFSG ACSTC AETEA AEZWR AFHIU AHWEU AIXLP CGR CUY CVF ECM EIF NFIDA NPM ACMFV 7QG 7QL 7QP 7QR 7T5 7TK 7TM 7TO 7U7 7U9 7XB 8FD 8FK C1K FR3 H94 K9. M7N MBDVC P64 PKEHL PQEST PQUKI PRINS Q9U RC3 7X8 PUEGO |
| ID | FETCH-LOGICAL-c642t-9864ab78f685792d3037477464d3c2af334467aa8ef12383a05329bb76e9f213 |
| IEDL.DBID | M7P |
| ISICitedReferencesCount | 1635 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000444174100015&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1078-8956 1546-170X |
| IngestDate | Thu Oct 02 04:04:47 EDT 2025 Fri Oct 03 09:42:00 EDT 2025 Sat Nov 29 13:23:00 EST 2025 Sat Nov 29 11:43:50 EST 2025 Sat Nov 29 10:16:17 EST 2025 Wed Nov 26 10:40:16 EST 2025 Wed Nov 26 09:35:56 EST 2025 Thu May 22 21:22:12 EDT 2025 Mon Jul 21 06:08:02 EDT 2025 Sat Nov 29 06:02:25 EST 2025 Tue Nov 18 22:10:18 EST 2025 Fri Feb 21 02:37:38 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 9 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c642t-9864ab78f685792d3037477464d3c2af334467aa8ef12383a05329bb76e9f213 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-4264-8329 0000-0002-9623-7007 0000-0001-6901-0985 0000-0001-8797-5019 0000-0002-4266-1515 0000-0002-9239-745X |
| PMID | 30104768 |
| PQID | 2100850439 |
| PQPubID | 33975 |
| PageCount | 9 |
| ParticipantIDs | proquest_miscellaneous_2088293476 proquest_journals_2100850439 gale_infotracmisc_A572904635 gale_infotracgeneralonefile_A572904635 gale_infotracacademiconefile_A572904635 gale_incontextgauss_ISR_A572904635 gale_incontextgauss_IOV_A572904635 gale_healthsolutions_A572904635 pubmed_primary_30104768 crossref_citationtrail_10_1038_s41591_018_0107_6 crossref_primary_10_1038_s41591_018_0107_6 springer_journals_10_1038_s41591_018_0107_6 |
| PublicationCentury | 2000 |
| PublicationDate | 2018-09-01 |
| PublicationDateYYYYMMDD | 2018-09-01 |
| PublicationDate_xml | – month: 09 year: 2018 text: 2018-09-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York – name: United States |
| PublicationTitle | Nature medicine |
| PublicationTitleAbbrev | Nat Med |
| PublicationTitleAlternate | Nat Med |
| PublicationYear | 2018 |
| Publisher | Nature Publishing Group US Nature Publishing Group |
| Publisher_xml | – name: Nature Publishing Group US – name: Nature Publishing Group |
| References | BourneRRAMagnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysisLancet Glob. Health20175e888e89710.1016/S2214-109X(17)30293-0 EstevaADermatologist-level classification of skin cancer with deep neural networksNature2017542115–11810.1038/nature21056 OECD. Computed tomography (CT) exams (indicator). (2017); https://doi.org/10.1787/3c994537-en Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T. & Ronneberger, O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. in Ourselin, S., Joskowicz, L., Sabuncu, M., Unal, G., Wells, W. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. MICCAI 2016. Lecture Notes in Computer Science, vol. 9901 (Springer, Cham, Switzerland; 2016). SrinivasanPPFully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography imagesBiomed. Opt. Express201453568357710.1364/BOE.5.003568 Abadi, M. et al. TensorFlow: large-scale machine learning on heterogeneous systems. Preprint at https://arxiv.org/abs/1603.04467 (2016). CastelvecchiDCan we open the black box of AI?Nature201653820231:CAS:528:DC%2BC28Xhs1ehsr7F10.1038/538020a Schmidt-ErfurthUKlimschaSWaldsteinSMBogunovićHA view of the current and future role of optical coherence tomography in the management of age-related macular degenerationEye20173126441:STN:280:DC%2BC2sjgtlWnuw%3D%3D10.1038/eye.2016.227 LeeCSBaughmanDMLeeAYDeep learning is effective for classifying normal versus age-related macular degeneration OCT imagesOphthalmol. Retin.2017132232710.1016/j.oret.2016.12.009 KeanePAEvaluation of age-related macular degeneration with optical coherence tomographySurv. Ophthalmol.20125738941410.1016/j.survophthal.2012.01.006 ChopraRMulhollandPJDubisAMAndersonRSKeanePAHuman factor and usability testing of a binocular optical coherence tomography systemTransl. Vis. Sci. Technol.201761610.1167/tvst.6.4.16 KarriSPKChakrabortyDChatterjeeJTransfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degenerationBiomed. Opt. Express201785795921:STN:280:DC%2BC1czjvFGhtw%3D%3D10.1364/BOE.8.000579 Apostolopoulos, S., Ciller, C., De Zanet, S. I., Wolf, S. & Sznitman, R. RetiNet: automatic AMD identification in OCT volumetric data. Preprint at http://arxiv.org/abs/1610.03628v1 (2016). SchleglTFully automated detection and quantification of macular fluid in OCT using deep learningOphthalmology201812554955810.1016/j.ophtha.2017.10.031 AriasLDelay in treating age-related macular degeneration in Spain is associated with progressive vision lossEye2009233263331:STN:280:DC%2BD1M7js1Ojsw%3D%3D10.1038/sj.eye.6703053 KeanePASaddaSRPredicting visual outcomes for macular disease using optical coherence tomographySaudi J. Ophthalmol.20112514515810.1016/j.sjopt.2011.01.003 GulshanVDevelopment and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographsJ. Am. Med. Assoc.20163162402241010.1001/jama.2016.17216 RoyAGReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networkBiomed. Opt. Express201783627364210.1364/BOE.8.003627 Lakshminarayanan, B., Pritzel, A. & Blundell, C. Simple and scalable predictive uncertainty estimation using deep ensembles. Adv. Neural Inf. Process. Syst. 6405–6416 (2017). Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. in Navab N., Hornegger J., Wells W., Frangi A. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol. 9351 (Springer, Cham, Switzerland, 2015). Lu, D. et al. Retinal fluid segmentation and detection in optical coherence tomography images using fully convolutional neural network. Preprint at http://arxiv.org/abs/1710.04778v1 (2017). SchindelinJFiji: an open-source platform for biological-image analysisNat. Methods201296766821:CAS:528:DC%2BC38XhtVKnurbJ10.1038/nmeth.2019 VillaniEDecade-long profile of imaging biomarker use in ophthalmic clinical trialsInvest. Ophthalmol. Vis. Sci.201758BIO76BIO8110.1167/iovs.17-21790 SchaalKBRosenfeldPJGregoriGYehoshuaZFeuerWJAnatomic clinical trial endpoints for nonexudative age-related macular degenerationOphthalmology20161231060107910.1016/j.ophtha.2016.01.034 FangLAutomatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph searchBiomed. Opt. Express201782732274410.1364/BOE.8.002732 BuchanJCHow to defuse a demographic time bomb: the way forward?Eye201731151915221:STN:280:DC%2BC1cnptVClug%3D%3D10.1038/eye.2017.114 MuetherPSHermannMMKochKFauserSDelay between medical indication to anti-VEGF treatment in age-related macular degeneration can result in a loss of visual acuityGraefes Arch. Clin. Exp. Ophthalmol.20112496336371:CAS:528:DC%2BC3MXltlKjsbk%3D10.1007/s00417-010-1520-9 Schmidt-ErfurthUWaldsteinSMA paradigm shift in imaging biomarkers in neovascular age-related macular degenerationProg. Retin. Eye Res.2016501241:CAS:528:DC%2BC2MXhsV2mu7nF10.1016/j.preteyeres.2015.07.007 LeeCSDeep-learning based, automated segmentation of macular edema in optical coherence tomographyBiomed. Opt. Express201783440344810.1364/BOE.8.003440 De FauwJAutomated analysis of retinal imaging using machine learning techniques for computer visionF1000Res20165157310.12688/f1000research.8996.1 Huang, G., Liu, Z., Weinberger, K. Q. & van der Maaten, L. Densely connected convolutional networks. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2261–2269 (2017). OwenCGThe estimated prevalence and incidence of late stage age related macular degeneration in the UKBr. J. Ophthalmol.20129675275610.1136/bjophthalmol-2011-301109 Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. in Proceedings of the 3rd International Conference on Learning Representations (ICLR). Preprint at http://arxiv.org/abs/1412.6980 (2015). FarsiuSQuantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomographyOphthalmology201412116217210.1016/j.ophtha.2013.07.013 Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the inception architecture for computer vision. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2818–2826 (2016). FootBMacEwenCSurveillance of sight loss due to delay in ophthalmic treatment or review: frequency, cause and outcomeEye2017317717751:STN:280:DC%2BC1c7pvVOitw%3D%3D10.1038/eye.2017.1 RudnickaARIncidence of late-stage age-related macular degeneration in American whites: systematic review and meta-analysisAm. J. Ophthalmol.2015160859310.1016/j.ajo.2015.04.003 FolgarFAComparison of optical coherence tomography assessments in the comparison of age-related macular degeneration treatments trialsOphthalmology20141211956196510.1016/j.ophtha.2014.04.020 Duker, J. S., Waheed, N. K. & Goldman, D. Handbook of Retinal OCT: Optical Coherence Tomography E-Book (Elsevier Health Sciences, Oxford, UK; 2013). Schmidt-ErfurthUMachine learning to analyze the prognostic value of current imaging biomarkers in neovascular age-related macular degenerationOphthalmol. Retin.20182243010.1016/j.oret.2017.03.015 HuangDOptical coherence tomographyScience1991254117811811:STN:280:DyaK38%2Fms12lsA%3D%3D10.1126/science.1957169 WhitedJDA modeled economic analysis of a digital teleophthalmology system as used by three federal healthcare agencies for detecting proliferative diabetic retinopathyTelemed. J. E Health20051164165110.1089/tmj.2005.11.641 OECD. Magnetic resonance imaging (MRI) exams (indicator). (2017). https://doi.org/10.1787/1d89353f-en 107_CR14 107_CR13 KB Schaal (107_CR30) 2016; 123 PS Muether (107_CR15) 2011; 249 107_CR38 107_CR37 107_CR18 D Castelvecchi (107_CR26) 2016; 538 107_CR39 PA Keane (107_CR29) 2011; 25 PA Keane (107_CR35) 2012; 57 L Fang (107_CR22) 2017; 8 T Schlegl (107_CR28) 2018; 125 JD Whited (107_CR12) 2005; 11 A Esteva (107_CR9) 2017; 542 L Arias (107_CR16) 2009; 23 U Schmidt-Erfurth (107_CR7) 2017; 31 PP Srinivasan (107_CR20) 2014; 5 E Villani (107_CR32) 2017; 58 CS Lee (107_CR23) 2017; 8 R Chopra (107_CR33) 2017; 6 107_CR24 D Huang (107_CR10) 1991; 254 V Gulshan (107_CR8) 2016; 316 S Farsiu (107_CR19) 2014; 121 107_CR41 U Schmidt-Erfurth (107_CR27) 2018; 2 107_CR40 107_CR42 CG Owen (107_CR4) 2012; 96 CS Lee (107_CR21) 2017; 1 B Foot (107_CR3) 2017; 31 J Schindelin (107_CR34) 2012; 9 107_CR2 FA Folgar (107_CR36) 2014; 121 U Schmidt-Erfurth (107_CR31) 2016; 50 107_CR1 RRA Bourne (107_CR6) 2017; 5 JC Buchan (107_CR11) 2017; 31 SPK Karri (107_CR17) 2017; 8 AR Rudnicka (107_CR5) 2015; 160 AG Roy (107_CR25) 2017; 8 J Fauw De (107_CR43) 2016; 5 37596401 - Eye (Lond). 2024 Feb;38(2):380-385. doi: 10.1038/s41433-023-02705-7. 30177823 - Nat Med. 2018 Sep;24(9):1304-1305. doi: 10.1038/s41591-018-0178-4. |
| References_xml | – reference: Duker, J. S., Waheed, N. K. & Goldman, D. Handbook of Retinal OCT: Optical Coherence Tomography E-Book (Elsevier Health Sciences, Oxford, UK; 2013). – reference: FarsiuSQuantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomographyOphthalmology201412116217210.1016/j.ophtha.2013.07.013 – reference: Lu, D. et al. Retinal fluid segmentation and detection in optical coherence tomography images using fully convolutional neural network. Preprint at http://arxiv.org/abs/1710.04778v1 (2017). – reference: Schmidt-ErfurthUMachine learning to analyze the prognostic value of current imaging biomarkers in neovascular age-related macular degenerationOphthalmol. Retin.20182243010.1016/j.oret.2017.03.015 – reference: KeanePASaddaSRPredicting visual outcomes for macular disease using optical coherence tomographySaudi J. Ophthalmol.20112514515810.1016/j.sjopt.2011.01.003 – reference: VillaniEDecade-long profile of imaging biomarker use in ophthalmic clinical trialsInvest. Ophthalmol. Vis. Sci.201758BIO76BIO8110.1167/iovs.17-21790 – reference: OECD. Magnetic resonance imaging (MRI) exams (indicator). (2017). https://doi.org/10.1787/1d89353f-en – reference: Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the inception architecture for computer vision. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2818–2826 (2016). – reference: AriasLDelay in treating age-related macular degeneration in Spain is associated with progressive vision lossEye2009233263331:STN:280:DC%2BD1M7js1Ojsw%3D%3D10.1038/sj.eye.6703053 – reference: Apostolopoulos, S., Ciller, C., De Zanet, S. I., Wolf, S. & Sznitman, R. RetiNet: automatic AMD identification in OCT volumetric data. Preprint at http://arxiv.org/abs/1610.03628v1 (2016). – reference: Abadi, M. et al. TensorFlow: large-scale machine learning on heterogeneous systems. Preprint at https://arxiv.org/abs/1603.04467 (2016). – reference: BuchanJCHow to defuse a demographic time bomb: the way forward?Eye201731151915221:STN:280:DC%2BC1cnptVClug%3D%3D10.1038/eye.2017.114 – reference: Lakshminarayanan, B., Pritzel, A. & Blundell, C. Simple and scalable predictive uncertainty estimation using deep ensembles. Adv. Neural Inf. Process. Syst. 6405–6416 (2017). – reference: Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. in Navab N., Hornegger J., Wells W., Frangi A. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol. 9351 (Springer, Cham, Switzerland, 2015). – reference: RudnickaARIncidence of late-stage age-related macular degeneration in American whites: systematic review and meta-analysisAm. J. Ophthalmol.2015160859310.1016/j.ajo.2015.04.003 – reference: CastelvecchiDCan we open the black box of AI?Nature201653820231:CAS:528:DC%2BC28Xhs1ehsr7F10.1038/538020a – reference: EstevaADermatologist-level classification of skin cancer with deep neural networksNature2017542115–11810.1038/nature21056 – reference: KarriSPKChakrabortyDChatterjeeJTransfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degenerationBiomed. Opt. Express201785795921:STN:280:DC%2BC1czjvFGhtw%3D%3D10.1364/BOE.8.000579 – reference: ChopraRMulhollandPJDubisAMAndersonRSKeanePAHuman factor and usability testing of a binocular optical coherence tomography systemTransl. Vis. Sci. Technol.201761610.1167/tvst.6.4.16 – reference: KeanePAEvaluation of age-related macular degeneration with optical coherence tomographySurv. Ophthalmol.20125738941410.1016/j.survophthal.2012.01.006 – reference: BourneRRAMagnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysisLancet Glob. Health20175e888e89710.1016/S2214-109X(17)30293-0 – reference: MuetherPSHermannMMKochKFauserSDelay between medical indication to anti-VEGF treatment in age-related macular degeneration can result in a loss of visual acuityGraefes Arch. Clin. Exp. Ophthalmol.20112496336371:CAS:528:DC%2BC3MXltlKjsbk%3D10.1007/s00417-010-1520-9 – reference: Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T. & Ronneberger, O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. in Ourselin, S., Joskowicz, L., Sabuncu, M., Unal, G., Wells, W. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. MICCAI 2016. Lecture Notes in Computer Science, vol. 9901 (Springer, Cham, Switzerland; 2016). – reference: SchleglTFully automated detection and quantification of macular fluid in OCT using deep learningOphthalmology201812554955810.1016/j.ophtha.2017.10.031 – reference: HuangDOptical coherence tomographyScience1991254117811811:STN:280:DyaK38%2Fms12lsA%3D%3D10.1126/science.1957169 – reference: SchindelinJFiji: an open-source platform for biological-image analysisNat. Methods201296766821:CAS:528:DC%2BC38XhtVKnurbJ10.1038/nmeth.2019 – reference: LeeCSDeep-learning based, automated segmentation of macular edema in optical coherence tomographyBiomed. Opt. Express201783440344810.1364/BOE.8.003440 – reference: RoyAGReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networkBiomed. Opt. Express201783627364210.1364/BOE.8.003627 – reference: SchaalKBRosenfeldPJGregoriGYehoshuaZFeuerWJAnatomic clinical trial endpoints for nonexudative age-related macular degenerationOphthalmology20161231060107910.1016/j.ophtha.2016.01.034 – reference: Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. in Proceedings of the 3rd International Conference on Learning Representations (ICLR). Preprint at http://arxiv.org/abs/1412.6980 (2015). – reference: Schmidt-ErfurthUKlimschaSWaldsteinSMBogunovićHA view of the current and future role of optical coherence tomography in the management of age-related macular degenerationEye20173126441:STN:280:DC%2BC2sjgtlWnuw%3D%3D10.1038/eye.2016.227 – reference: LeeCSBaughmanDMLeeAYDeep learning is effective for classifying normal versus age-related macular degeneration OCT imagesOphthalmol. Retin.2017132232710.1016/j.oret.2016.12.009 – reference: FolgarFAComparison of optical coherence tomography assessments in the comparison of age-related macular degeneration treatments trialsOphthalmology20141211956196510.1016/j.ophtha.2014.04.020 – reference: OECD. Computed tomography (CT) exams (indicator). (2017); https://doi.org/10.1787/3c994537-en – reference: FootBMacEwenCSurveillance of sight loss due to delay in ophthalmic treatment or review: frequency, cause and outcomeEye2017317717751:STN:280:DC%2BC1c7pvVOitw%3D%3D10.1038/eye.2017.1 – reference: GulshanVDevelopment and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographsJ. Am. Med. Assoc.20163162402241010.1001/jama.2016.17216 – reference: SrinivasanPPFully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography imagesBiomed. Opt. Express201453568357710.1364/BOE.5.003568 – reference: Huang, G., Liu, Z., Weinberger, K. Q. & van der Maaten, L. Densely connected convolutional networks. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2261–2269 (2017). – reference: OwenCGThe estimated prevalence and incidence of late stage age related macular degeneration in the UKBr. J. Ophthalmol.20129675275610.1136/bjophthalmol-2011-301109 – reference: WhitedJDA modeled economic analysis of a digital teleophthalmology system as used by three federal healthcare agencies for detecting proliferative diabetic retinopathyTelemed. J. E Health20051164165110.1089/tmj.2005.11.641 – reference: Schmidt-ErfurthUWaldsteinSMA paradigm shift in imaging biomarkers in neovascular age-related macular degenerationProg. Retin. Eye Res.2016501241:CAS:528:DC%2BC2MXhsV2mu7nF10.1016/j.preteyeres.2015.07.007 – reference: FangLAutomatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph searchBiomed. Opt. Express201782732274410.1364/BOE.8.002732 – reference: De FauwJAutomated analysis of retinal imaging using machine learning techniques for computer visionF1000Res20165157310.12688/f1000research.8996.1 – volume: 8 start-page: 579 year: 2017 ident: 107_CR17 publication-title: Biomed. Opt. Express doi: 10.1364/BOE.8.000579 – ident: 107_CR39 – volume: 254 start-page: 1178 year: 1991 ident: 107_CR10 publication-title: Science doi: 10.1126/science.1957169 – volume: 23 start-page: 326 year: 2009 ident: 107_CR16 publication-title: Eye doi: 10.1038/sj.eye.6703053 – ident: 107_CR37 – ident: 107_CR18 – volume: 125 start-page: 549 year: 2018 ident: 107_CR28 publication-title: Ophthalmology doi: 10.1016/j.ophtha.2017.10.031 – volume: 96 start-page: 752 year: 2012 ident: 107_CR4 publication-title: Br. J. Ophthalmol. doi: 10.1136/bjophthalmol-2011-301109 – volume: 8 start-page: 3627 year: 2017 ident: 107_CR25 publication-title: Biomed. Opt. Express doi: 10.1364/BOE.8.003627 – volume: 121 start-page: 1956 year: 2014 ident: 107_CR36 publication-title: Ophthalmology doi: 10.1016/j.ophtha.2014.04.020 – volume: 316 start-page: 2402 year: 2016 ident: 107_CR8 publication-title: J. Am. Med. Assoc. doi: 10.1001/jama.2016.17216 – ident: 107_CR38 doi: 10.1109/CVPR.2016.308 – volume: 8 start-page: 2732 year: 2017 ident: 107_CR22 publication-title: Biomed. Opt. Express doi: 10.1364/BOE.8.002732 – volume: 1 start-page: 322 year: 2017 ident: 107_CR21 publication-title: Ophthalmol. Retin. doi: 10.1016/j.oret.2016.12.009 – ident: 107_CR1 doi: 10.1787/3c994537-en – volume: 123 start-page: 1060 year: 2016 ident: 107_CR30 publication-title: Ophthalmology doi: 10.1016/j.ophtha.2016.01.034 – volume: 249 start-page: 633 year: 2011 ident: 107_CR15 publication-title: Graefes Arch. Clin. Exp. Ophthalmol. doi: 10.1007/s00417-010-1520-9 – volume: 58 start-page: BIO76 year: 2017 ident: 107_CR32 publication-title: Invest. Ophthalmol. Vis. Sci. doi: 10.1167/iovs.17-21790 – ident: 107_CR13 doi: 10.1007/978-3-319-24574-4_28 – volume: 25 start-page: 145 year: 2011 ident: 107_CR29 publication-title: Saudi J. Ophthalmol. doi: 10.1016/j.sjopt.2011.01.003 – ident: 107_CR14 doi: 10.1007/978-3-319-46723-8_49 – volume: 5 start-page: 3568 year: 2014 ident: 107_CR20 publication-title: Biomed. Opt. Express doi: 10.1364/BOE.5.003568 – volume: 6 start-page: 16 year: 2017 ident: 107_CR33 publication-title: Transl. Vis. Sci. Technol. doi: 10.1167/tvst.6.4.16 – volume: 31 start-page: 771 year: 2017 ident: 107_CR3 publication-title: Eye doi: 10.1038/eye.2017.1 – volume: 5 start-page: e888 year: 2017 ident: 107_CR6 publication-title: Lancet Glob. Health doi: 10.1016/S2214-109X(17)30293-0 – volume: 31 start-page: 26 year: 2017 ident: 107_CR7 publication-title: Eye doi: 10.1038/eye.2016.227 – volume: 9 start-page: 676 year: 2012 ident: 107_CR34 publication-title: Nat. Methods doi: 10.1038/nmeth.2019 – ident: 107_CR40 – volume: 11 start-page: 641 year: 2005 ident: 107_CR12 publication-title: Telemed. J. E Health doi: 10.1089/tmj.2005.11.641 – volume: 160 start-page: 85 year: 2015 ident: 107_CR5 publication-title: Am. J. Ophthalmol. doi: 10.1016/j.ajo.2015.04.003 – ident: 107_CR42 – ident: 107_CR2 doi: 10.1787/1d89353f-en – volume: 57 start-page: 389 year: 2012 ident: 107_CR35 publication-title: Surv. Ophthalmol. doi: 10.1016/j.survophthal.2012.01.006 – volume: 538 start-page: 20 year: 2016 ident: 107_CR26 publication-title: Nature doi: 10.1038/538020a – ident: 107_CR41 doi: 10.1109/CVPR.2017.243 – ident: 107_CR24 – volume: 50 start-page: 1 year: 2016 ident: 107_CR31 publication-title: Prog. Retin. Eye Res. doi: 10.1016/j.preteyeres.2015.07.007 – volume: 542 start-page: 115– year: 2017 ident: 107_CR9 publication-title: Nature doi: 10.1038/nature21056 – volume: 31 start-page: 1519 year: 2017 ident: 107_CR11 publication-title: Eye doi: 10.1038/eye.2017.114 – volume: 2 start-page: 24 year: 2018 ident: 107_CR27 publication-title: Ophthalmol. Retin. doi: 10.1016/j.oret.2017.03.015 – volume: 8 start-page: 3440 year: 2017 ident: 107_CR23 publication-title: Biomed. Opt. Express doi: 10.1364/BOE.8.003440 – volume: 5 start-page: 1573 year: 2016 ident: 107_CR43 publication-title: F1000Res doi: 10.12688/f1000research.8996.1 – volume: 121 start-page: 162 year: 2014 ident: 107_CR19 publication-title: Ophthalmology doi: 10.1016/j.ophtha.2013.07.013 – reference: 30177823 - Nat Med. 2018 Sep;24(9):1304-1305. doi: 10.1038/s41591-018-0178-4. – reference: 37596401 - Eye (Lond). 2024 Feb;38(2):380-385. doi: 10.1038/s41433-023-02705-7. |
| SSID | ssj0003059 |
| Score | 2.7236776 |
| Snippet | The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial... |
| SourceID | proquest gale pubmed crossref springer |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1342 |
| SubjectTerms | 631/114/1305 692/1807/1482 692/700/139 692/700/1421/2025 Aged Analysis Architecture Artificial intelligence Biomedical and Life Sciences Biomedicine Cancer Research Clinical Decision-Making Deep Learning Diagnostic imaging Diagnostic systems Female Humans Infectious Diseases Information storage and retrieval Machine learning Male Medical imaging equipment Metabolic Diseases Middle Aged Molecular Medicine Neurosciences Optical Coherence Tomography Referral and Consultation Retina Retina - diagnostic imaging Retina - pathology Retinal diseases Retinal Diseases - diagnosis Retinal Diseases - diagnostic imaging Tomography Tomography, Optical Coherence Training |
| Title | Clinically applicable deep learning for diagnosis and referral in retinal disease |
| URI | https://link.springer.com/article/10.1038/s41591-018-0107-6 https://www.ncbi.nlm.nih.gov/pubmed/30104768 https://www.proquest.com/docview/2100850439 https://www.proquest.com/docview/2088293476 |
| Volume | 24 |
| WOSCitedRecordID | wos000444174100015&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1546-170X dateEnd: 20181231 omitProxy: false ssIdentifier: ssj0003059 issn: 1078-8956 databaseCode: M7P dateStart: 19950101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1546-170X dateEnd: 20181231 omitProxy: false ssIdentifier: ssj0003059 issn: 1078-8956 databaseCode: 7X7 dateStart: 19950101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1546-170X dateEnd: 20181231 omitProxy: false ssIdentifier: ssj0003059 issn: 1078-8956 databaseCode: BENPR dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Research Library customDbUrl: eissn: 1546-170X dateEnd: 20181231 omitProxy: false ssIdentifier: ssj0003059 issn: 1078-8956 databaseCode: M2O dateStart: 19950101 isFulltext: true titleUrlDefault: https://search.proquest.com/pqrl providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 1546-170X dateEnd: 20181231 omitProxy: false ssIdentifier: ssj0003059 issn: 1078-8956 databaseCode: M2P dateStart: 19950101 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3db9MwED-xDRBC4qMMKIwSEAKJKVoTJ7HzhAraBA_rypimvllOnFSVqrQ0LdL-e-5sN1smmJB4iZTk8nWfdu78O4B3HMOCUJH2uYhTHzUk9kXQz_1c5RFjkcawbptN8OFQjMfpyP1wq11Z5cYnGket5zn9Iz8IA4OuhvHz0-KnT12jKLvqWmhswQ6hJDBTujdqPDHqcmprDoUvcCKwyWoycVBj4KKan4BKufrcT1px6bp3vhKeruVLTRg6evi_H_AIHrgBqDewGvMYbhVVB-4PruQTOnDHdqi86MDdY5d7fwLfHYTobHbhubR3Nis8XRQLz_WemHg4BPa0rd6b1p6qtGf6mCzxidPKoxWT9HCXFdqFs6PDsy9ffdeQwc9xmrLyCcpdZVyUiYh5GmpG4DU4fkwizfJQlShc9LtKiaLEgCiYorYTaZbxpEjLMGBPYbuaV8Vz8JSKuEbm40FK_WZC96NAxyJQccZZorrQ30hD5g6snHpmzKRJmjMhrQAl3kOSAGXShY_NJQuL1HET8WsSsbSLTRsrl4MYJxsEohZ34a2hIIyMiopwJmpd1_Lbyfk_EP04bRF9cETlHL8BrcAufEBOEPZWi_J9i3Jikcf_RLjXIkSXkLdPb1RQOpdUy0v968Kb5jRdSWV2VTFfIw1NuFIWcWTQM6vxDS-ZQXJKRBf2NyZwefO_MvrFza_yEu6FxgapaG8PtlfLdfEKbue_VtN62YMtPuZmK3qw8_lwODrFvePwxGxHPWPtvwHY802W |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Zb9NAEB6VcgqJI1yGQhcErQSyGnt9rB8QioCqUdtQIEJ9W629dhQpckKcgPKj-I_MeG23rqDipQ-8xuO1PTvHbubbbwBehpgWhPK0HQo_stFCfFs43cROVOJx7mlM66bZRDgYiOPj6GgNftVnYQhWWcfEMlDraUL_ke-4Tsmuhvnz3ey7TV2jqLpat9AwZrGfrn7ilq142_-A8_vKdXc_Dt_v2VVXATvBtfbCJj5yFYciC4QfRq7mxMCCi6DA0zxxVYZviMFDKZFmGNUFV9Q7IYrjMEijzHU4DnsJLuMqwhUlUvCoCfzoOpGBOApb4L6jLqJysVNgniSIkUPIsW5oB600eDYZnMqGZ8qzZdbbvf2f6esO3KqW16xn_OEurKV5B272TlVLOnDV9N9cdeDaYYUsuAefK4LUyWTFqqJ-PEmZTtMZqzprjBgu8Jk22MRxwVSuWdmlZY5PHOeMzoPSw6ua130YXsSHPoD1fJqnj4Ap5YUa5xp_pMJ2LHTXc7QvHOXHIQ-UBd168mVSUbFTR5CJLCEBXEhjLxLHkGQvMrDgdXPLzPCQnCe8SRYlzVHaJobJno9bKaKI8y14UUoQA0hOEKORWhaF7H_69g9CX7-0hLYroWyK34A-bo51oCaIWawludWSHBle9T8JbrQEMeAl7cu1xcsq4BbyxNwteN5cpjsJRJin0yXK0HYy4l6ICnpoHKzRJS95qgJhwZva404G_6uiH5__KptwfW94eCAP-oP9J3DDLd2f4IkbsL6YL9OncCX5sRgX82dlIGEgL9gRfwNDXqCn |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VAhVC4hFegUIN4iGBrMRe27s-IBRRIqJCCFCh3lZrrx1FipwQJ6D8NP4dM951WldQcemBqz1e2-N57Hq-_QbgKce0IFSgXS7C2EULCV3hdVM3VWnAWKAxrZtmE3w4FEdH8WgLftV7YQhWWcfEKlDrWUr_yDu-V7GrYf7s5BYWMdrvv5l_d6mDFFVa63YaxkQOsvVPXL6Vrwf7-K2f-X7_3eHb967tMOCmOO9eusRNrhIu8kiEPPY1IzYWnBBFgWapr3J8WgwkSoksxwgvmKI-CnGS8CiLc99jOOwFuMiJs7xCDY42SQDdKDZwR-EKXIPUBVUmOiXmTIIbeYQi63I3aqTE04nhRGY8VaqtMmD_-n-suxtwzU67nZ7xk5uwlRUtuNo7UUVpwWXTl3Pdgp2PFnFwCz5b4tTpdO3YYn8yzRydZXPHdtwYOzjxd7TBLE5KRxXaqbq3LPCOk8KhfaJ0c1sLuw2H5_Gid2C7mBXZPXCUCrjG744HqeCdCN0NPB0KT4UJZ5FqQ7c2BJlainbqFDKVFVSACWlsR-IYkmxHRm14ublkbvhJzhLeI-uSZovtJrbJXohLLKKOC9vwpJIgZpCC7GSsVmUpB5--_YPQ1y8NoRdWKJ_hO6Dvm-0eqAliHGtIPm9Ijg3f-p8EdxuCGAjT5una-qUNxKU8Nv02PN6cpisJXFhksxXK0DIzZgFHBd01zrbRJav4qyLRhle19x0P_ldF3z_7UfZgB_1PfhgMDx7AFb-KBIRa3IXt5WKVPYRL6Y_lpFw8qmKKA_Kc_fA3fTapZA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Clinically+applicable+deep+learning+for+diagnosis+and+referral+in+retinal+disease&rft.jtitle=Nature+medicine&rft.au=De+Fauw%2C+Jeffrey&rft.au=Blackwell%2C+Sam&rft.au=Ledsam%2C+Joseph+R&rft.au=Askham%2C+Harry&rft.date=2018-09-01&rft.pub=Nature+Publishing+Group&rft.issn=1078-8956&rft.volume=24&rft.issue=9&rft.spage=1342&rft_id=info:doi/10.1038%2Fs41591-018-0107-6&rft.externalDBID=n%2Fa&rft.externalDocID=A572904635 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1078-8956&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1078-8956&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1078-8956&client=summon |