Deep Learning Algorithm Detects Presence of Disorganization of Retinal Inner Layers (DRIL)–An Early Imaging Biomarker in Diabetic Retinopathy
To develop and train a deep learning-based algorithm for detecting disorganization of retinal inner layers (DRIL) on optical coherence tomography (OCT) to screen a cohort of patients with diabetic retinopathy (DR). In this cross-sectional study, subjects over age 18, with ICD-9/10 diagnoses of type...
Uloženo v:
| Vydáno v: | Translational vision science & technology Ročník 12; číslo 7; s. 6 |
|---|---|
| Hlavní autoři: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
The Association for Research in Vision and Ophthalmology
06.07.2023
|
| Témata: | |
| ISSN: | 2164-2591, 2164-2591 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | To develop and train a deep learning-based algorithm for detecting disorganization of retinal inner layers (DRIL) on optical coherence tomography (OCT) to screen a cohort of patients with diabetic retinopathy (DR).
In this cross-sectional study, subjects over age 18, with ICD-9/10 diagnoses of type 2 diabetes with and without retinopathy and Cirrus HD-OCT imaging performed between January 2009 to September 2019 were included in this study. After inclusion and exclusion criteria were applied, a final total of 664 patients (5992 B-scans from 1201 eyes) were included for analysis. Five-line horizontal raster scans from Cirrus HD-OCT were obtained from the shared electronic health record. Two trained graders evaluated scans for presence of DRIL. A third physician grader arbitrated any disagreements. Of 5992 B-scans analyzed, 1397 scans (∼30%) demonstrated presence of DRIL. Graded scans were used to label training data for the convolution neural network (CNN) development and training.
On a single CPU system, the best performing CNN training took ∼35 mins. Labeled data were divided 90:10 for internal training/validation and external testing purpose. With this training, our deep learning network was able to predict the presence of DRIL in new OCT scans with a high accuracy of 88.3%, specificity of 90.0%, sensitivity of 82.9%, and Matthews correlation coefficient of 0.7.
The present study demonstrates that a deep learning-based OCT classification algorithm can be used for rapid automated identification of DRIL. This developed tool can assist in screening for DRIL in both research and clinical decision-making settings.
A deep learning algorithm can detect disorganization of retinal inner layers in OCT scans. |
|---|---|
| AbstractList | To develop and train a deep learning-based algorithm for detecting disorganization of retinal inner layers (DRIL) on optical coherence tomography (OCT) to screen a cohort of patients with diabetic retinopathy (DR).PurposeTo develop and train a deep learning-based algorithm for detecting disorganization of retinal inner layers (DRIL) on optical coherence tomography (OCT) to screen a cohort of patients with diabetic retinopathy (DR).In this cross-sectional study, subjects over age 18, with ICD-9/10 diagnoses of type 2 diabetes with and without retinopathy and Cirrus HD-OCT imaging performed between January 2009 to September 2019 were included in this study. After inclusion and exclusion criteria were applied, a final total of 664 patients (5992 B-scans from 1201 eyes) were included for analysis. Five-line horizontal raster scans from Cirrus HD-OCT were obtained from the shared electronic health record. Two trained graders evaluated scans for presence of DRIL. A third physician grader arbitrated any disagreements. Of 5992 B-scans analyzed, 1397 scans (∼30%) demonstrated presence of DRIL. Graded scans were used to label training data for the convolution neural network (CNN) development and training.MethodsIn this cross-sectional study, subjects over age 18, with ICD-9/10 diagnoses of type 2 diabetes with and without retinopathy and Cirrus HD-OCT imaging performed between January 2009 to September 2019 were included in this study. After inclusion and exclusion criteria were applied, a final total of 664 patients (5992 B-scans from 1201 eyes) were included for analysis. Five-line horizontal raster scans from Cirrus HD-OCT were obtained from the shared electronic health record. Two trained graders evaluated scans for presence of DRIL. A third physician grader arbitrated any disagreements. Of 5992 B-scans analyzed, 1397 scans (∼30%) demonstrated presence of DRIL. Graded scans were used to label training data for the convolution neural network (CNN) development and training.On a single CPU system, the best performing CNN training took ∼35 mins. Labeled data were divided 90:10 for internal training/validation and external testing purpose. With this training, our deep learning network was able to predict the presence of DRIL in new OCT scans with a high accuracy of 88.3%, specificity of 90.0%, sensitivity of 82.9%, and Matthews correlation coefficient of 0.7.ResultsOn a single CPU system, the best performing CNN training took ∼35 mins. Labeled data were divided 90:10 for internal training/validation and external testing purpose. With this training, our deep learning network was able to predict the presence of DRIL in new OCT scans with a high accuracy of 88.3%, specificity of 90.0%, sensitivity of 82.9%, and Matthews correlation coefficient of 0.7.The present study demonstrates that a deep learning-based OCT classification algorithm can be used for rapid automated identification of DRIL. This developed tool can assist in screening for DRIL in both research and clinical decision-making settings.ConclusionsThe present study demonstrates that a deep learning-based OCT classification algorithm can be used for rapid automated identification of DRIL. This developed tool can assist in screening for DRIL in both research and clinical decision-making settings.A deep learning algorithm can detect disorganization of retinal inner layers in OCT scans.Translational RelevanceA deep learning algorithm can detect disorganization of retinal inner layers in OCT scans. To develop and train a deep learning-based algorithm for detecting disorganization of retinal inner layers (DRIL) on optical coherence tomography (OCT) to screen a cohort of patients with diabetic retinopathy (DR). In this cross-sectional study, subjects over age 18, with ICD-9/10 diagnoses of type 2 diabetes with and without retinopathy and Cirrus HD-OCT imaging performed between January 2009 to September 2019 were included in this study. After inclusion and exclusion criteria were applied, a final total of 664 patients (5992 B-scans from 1201 eyes) were included for analysis. Five-line horizontal raster scans from Cirrus HD-OCT were obtained from the shared electronic health record. Two trained graders evaluated scans for presence of DRIL. A third physician grader arbitrated any disagreements. Of 5992 B-scans analyzed, 1397 scans (∼30%) demonstrated presence of DRIL. Graded scans were used to label training data for the convolution neural network (CNN) development and training. On a single CPU system, the best performing CNN training took ∼35 mins. Labeled data were divided 90:10 for internal training/validation and external testing purpose. With this training, our deep learning network was able to predict the presence of DRIL in new OCT scans with a high accuracy of 88.3%, specificity of 90.0%, sensitivity of 82.9%, and Matthews correlation coefficient of 0.7. The present study demonstrates that a deep learning-based OCT classification algorithm can be used for rapid automated identification of DRIL. This developed tool can assist in screening for DRIL in both research and clinical decision-making settings. A deep learning algorithm can detect disorganization of retinal inner layers in OCT scans. |
| Author | Batoki, Julia Lin, Kimberly Luo, Shiming Anand-Apte, Bela Singuri, Srinidhi Singh, Rupesh Hatipoglu, Dilara Yuan, Alex |
| Author_xml | – sequence: 1 givenname: Rupesh surname: Singh fullname: Singh, Rupesh organization: Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA – sequence: 2 givenname: Srinidhi surname: Singuri fullname: Singuri, Srinidhi organization: Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA – sequence: 3 givenname: Julia surname: Batoki fullname: Batoki, Julia organization: Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA – sequence: 4 givenname: Kimberly surname: Lin fullname: Lin, Kimberly organization: Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA – sequence: 5 givenname: Shiming surname: Luo fullname: Luo, Shiming organization: Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA – sequence: 6 givenname: Dilara surname: Hatipoglu fullname: Hatipoglu, Dilara organization: Case Western Reserve University, Cleveland, OH, USA – sequence: 7 givenname: Bela surname: Anand-Apte fullname: Anand-Apte, Bela organization: Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA – sequence: 8 givenname: Alex surname: Yuan fullname: Yuan, Alex organization: Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37410472$$D View this record in MEDLINE/PubMed |
| BookMark | eNptkc1uEzEURi3UipbSFXvkZRFK6p8Ze2aFQlMg0khFFawtj3NnYpixU9upFFa8AQvekCfBIW1VEN7Yss8991rfM3TgvAOEXlAypVTI83Qb05SyqZyKJ-iYUVFMWFnTg0fnI3Qa4xeSl6jKohBP0RGXBSWFZMfoxxxgjRvQwVnX49nQ-2DTasRzSGBSxB8DRHAGsO_w3EYfeu3sN52sd7ura0jW6QEvnIOAG72FEPHZ_HrRvPr1_efM4Usdhi1ejLrf-d9aP-rwNaPWZZ1uc7nZS_xap9X2OTrs9BDh9G4_QZ_fXX66-DBprt4vLmbNxPCKpQmlwIxhXVWSDnRBTCtgSaSgmpec1pzVZf5iC0tDutbouhWS1l3Jl3LJW25qfoLe7L3rTTtmDFwKelDrYPN8W-W1VX-_OLtSvb9VlHAuZSWz4ezOEPzNBmJSo40GhkE78JuoWMV5XYuq4Bl9-bjZQ5f7GDLweg-Y4GMM0D0glKhdzmqXs6JMSSUyTf-hjU1_EsmT2uG_Nb8B7Z6vrQ |
| CitedBy_id | crossref_primary_10_1186_s40662_024_00389_y crossref_primary_10_4103_IJO_IJO_800_25 crossref_primary_10_3389_fphar_2025_1498814 crossref_primary_10_1038_s41598_024_63844_9 crossref_primary_10_1016_j_cmpb_2024_108253 crossref_primary_10_1080_02713683_2025_2456783 crossref_primary_10_1136_bjo_2024_326843 crossref_primary_10_1038_s41598_023_46200_1 |
| Cites_doi | 10.1016/j.media.2017.07.005 10.1167/tvst.10.3.29 10.1007/978-3-030-87000-3_16 10.1001/jamaophthalmol.2015.0972 10.1016/j.jdiacomp.2019.05.006 10.1167/iovs.18-24955 10.1109/CVPR.2018.00907 10.1016/j.preteyeres.2019.04.003 10.1063/1.4992835 10.1016/j.ophtha.2021.04.027 10.1007/s11042-020-09292-9 10.1007/978-3-319-46349-0_5 10.1016/j.patcog.2019.02.023 10.1109/ICCV.2017.74 10.1016/j.ajo.2018.08.037 10.1038/s41598-020-75816-w 10.1007/s11739-020-02583-x 10.1001/jamaophthalmol.2017.6256 10.1109/CVPR.2015.7298594 10.1016/j.ophtha.2019.12.015 10.1001/jamaophthalmol.2014.2350 10.1001/jamaophthalmol.2018.4484 10.1001/jamaophthalmol.2021.2309 10.1016/j.ophtha.2018.04.007 10.1609/aaai.v31i1.11231 10.1109/Access.6287639 10.1186/s12864-019-6413-7 |
| ContentType | Journal Article |
| Copyright | Copyright 2023 The Authors 2023 |
| Copyright_xml | – notice: Copyright 2023 The Authors 2023 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 5PM |
| DOI | 10.1167/tvst.12.7.6 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | 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: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| DocumentTitleAlternate | Deep Learning Detects DRIL |
| EISSN | 2164-2591 |
| ExternalDocumentID | PMC10337787 37410472 10_1167_tvst_12_7_6 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: NEI NIH HHS grantid: R01 EY026181 |
| GroupedDBID | 53G AAYXX ALMA_UNASSIGNED_HOLDINGS CITATION EBS EJD GROUPED_DOAJ M~E OK1 RPM TRV CGR CUY CVF ECM EIF NPM 7X8 5PM |
| ID | FETCH-LOGICAL-c382t-11e2cc2f850fea40cb6ed0761a353193295741bedc0fbca9b6719f53d7d3b3c93 |
| ISICitedReferencesCount | 10 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001108906600014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2164-2591 |
| IngestDate | Thu Aug 21 18:37:12 EDT 2025 Fri Jul 11 16:36:06 EDT 2025 Thu Apr 03 07:02:46 EDT 2025 Tue Nov 18 22:14:49 EST 2025 Sat Nov 29 03:28:27 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 7 |
| Language | English |
| License | http://creativecommons.org/licenses/by-nc-nd/4.0 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c382t-11e2cc2f850fea40cb6ed0761a353193295741bedc0fbca9b6719f53d7d3b3c93 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 RS and SS contributed equally to this work. |
| OpenAccessLink | http://dx.doi.org/10.1167/tvst.12.7.6 |
| PMID | 37410472 |
| PQID | 2833996843 |
| PQPubID | 23479 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_10337787 proquest_miscellaneous_2833996843 pubmed_primary_37410472 crossref_primary_10_1167_tvst_12_7_6 crossref_citationtrail_10_1167_tvst_12_7_6 |
| PublicationCentury | 2000 |
| PublicationDate | 20230706 |
| PublicationDateYYYYMMDD | 2023-07-06 |
| PublicationDate_xml | – month: 7 year: 2023 text: 20230706 day: 6 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | Translational vision science & technology |
| PublicationTitleAlternate | Transl Vis Sci Technol |
| PublicationYear | 2023 |
| Publisher | The Association for Research in Vision and Ophthalmology |
| Publisher_xml | – name: The Association for Research in Vision and Ophthalmology |
| References | Putzu (bib28) 2020; 79 Krizhevsky (bib15) 2012 Babiuch (bib5) 2019; 137 Zhao (bib25) 2017; 1864 Szegedy (bib17) 2017 Joltikov (bib8) 2018; 59 Bengio (bib24) 2012 Soekhoe (bib30) 2016 Gerendas (bib11) 2021; 139 Selvaraju (bib20) 2017 Loo (bib13) 2020; 127 Yoon (bib12) 2020; 10 Gjoreski (bib31) 2020; 8 Luque (bib22) 2019; 91 Ehlers (bib14) 2021; 10 Das (bib4) 2018; 136 Teo (bib1) 2021; 128 Szegedy (bib16) 2015 Bar-David (bib19) 2021 Zur (bib7) 2018; 196 Zoph (bib18) 2018 Ting (bib10) 2019; 72 Chicco (bib21) 2020; 21 Sun (bib3) 2014; 132 Nadri (bib9) 2019; 33 Venerito (bib29) 2021; 16 Mesnil (bib23) 2012 Olson (bib27) 2018 Wong (bib2) 2018; 125 Radwan (bib6) 2015; 133 Litjens (bib26) 2017; 42 |
| References_xml | – volume: 42 start-page: 60 year: 2017 ident: bib26 article-title: A survey on deep learning in medical image analysis publication-title: Med Image Anal doi: 10.1016/j.media.2017.07.005 – volume: 10 start-page: 29 year: 2021 ident: bib14 article-title: Longitudinal higher-order OCT assessment of quantitative fluid dynamics and the total retinal fluid index in neovascular AMD publication-title: Transl Vis Sci Technol doi: 10.1167/tvst.10.3.29 – start-page: 148 volume-title: Ophthalmic Medical Image Analysis year: 2021 ident: bib19 article-title: Impact of data augmentation on retinal OCT image segmentation for diabetic macular edema analysis doi: 10.1007/978-3-030-87000-3_16 – volume: 133 start-page: 820 year: 2015 ident: bib6 article-title: Association of disorganization of retinal inner layers with vision after resolution of center-involved diabetic macular edema publication-title: JAMA Ophthalmol doi: 10.1001/jamaophthalmol.2015.0972 – volume: 33 start-page: 550 year: 2019 ident: bib9 article-title: Disorganization of retinal inner layers correlates with ellipsoid zone disruption and retinal nerve fiber layer thinning in diabetic retinopathy publication-title: J Diabetes Complications doi: 10.1016/j.jdiacomp.2019.05.006 – volume: 59 start-page: 5481 year: 2018 ident: bib8 article-title: Disorganization of retinal inner layers (DRIL) and neuroretinal dysfunction in early diabetic retinopathy publication-title: Invest Ophthalmol Vis Sci doi: 10.1167/iovs.18-24955 – start-page: 8697 volume-title: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition year: 2018 ident: bib18 article-title: Learning transferable architectures for scalable image recognition doi: 10.1109/CVPR.2018.00907 – volume: 72 start-page: 100759 year: 2019 ident: bib10 article-title: Deep learning in ophthalmology: the technical and clinical considerations publication-title: Progr Retinal Eye Res doi: 10.1016/j.preteyeres.2019.04.003 – start-page: 17 volume-title: Proceedings of ICML workshop on unsupervised and transfer learning year: 2012 ident: bib24 article-title: Deep Learning of Representations for Unsupervised and Transfer Learning – volume: 1864 start-page: 020018 year: 2017 ident: bib25 article-title: Research on the deep learning of the small sample data based on transfer learning publication-title: AIP Conference Proc doi: 10.1063/1.4992835 – volume: 128 start-page: 1580 year: 2021 ident: bib1 article-title: Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta-analysis publication-title: Ophthalmology doi: 10.1016/j.ophtha.2021.04.027 – volume: 79 start-page: 26995 year: 2020 ident: bib28 article-title: Convolutional neural networks for relevance feedback in content based image retrieval publication-title: Multimedia Tools Appl doi: 10.1007/s11042-020-09292-9 – volume-title: Advances in Intelligent Data Analysis XV year: 2016 ident: bib30 article-title: On the impact of data set size in transfer learning using deep neural networks doi: 10.1007/978-3-319-46349-0_5 – volume: 91 start-page: 216 year: 2019 ident: bib22 article-title: The impact of class imbalance in classification performance metrics based on the binary confusion matrix publication-title: Pattern Recognition doi: 10.1016/j.patcog.2019.02.023 – start-page: 618 volume-title: 2017 IEEE International Conference on Computer Vision (ICCV) year: 2017 ident: bib20 article-title: Grad-CAM: visual explanations from deep networks via gradient-based localization doi: 10.1109/ICCV.2017.74 – volume: 196 start-page: 129 year: 2018 ident: bib7 article-title: Disorganization of retinal inner layers as a biomarker for idiopathic epiretinal membrane after macular surgery—the DREAM Study publication-title: Am J Ophthalmol doi: 10.1016/j.ajo.2018.08.037 – volume: 10 start-page: 18852 year: 2020 ident: bib12 article-title: Optical coherence tomography-based deep-learning model for detecting central serous chorioretinopathy publication-title: Sci Rep doi: 10.1038/s41598-020-75816-w – start-page: 97 volume-title: Proceedings of ICML Workshop on Unsupervised and Transfer Learning year: 2012 ident: bib23 article-title: Unsupervised and transfer learning challenge: a deep learning approach – volume: 16 start-page: 1457 year: 2021 ident: bib29 article-title: A convolutional neural network with transfer learning for automatic discrimination between low and high-grade synovitis: a pilot study publication-title: Intern Emerg Med doi: 10.1007/s11739-020-02583-x – start-page: 84 volume-title: Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1 year: 2012 ident: bib15 article-title: ImageNet classification with deep convolutional neural networks – volume: 136 start-page: 202 year: 2018 ident: bib4 article-title: Disorganization of inner retina and outer retinal morphology in diabetic macular edema publication-title: JAMA Ophthalmol doi: 10.1001/jamaophthalmol.2017.6256 – start-page: 1 volume-title: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) year: 2015 ident: bib16 article-title: Going deeper with convolutions doi: 10.1109/CVPR.2015.7298594 – volume: 127 start-page: 793 year: 2020 ident: bib13 article-title: Beyond performance metrics: automatic deep learning retinal OCT analysis reproduces clinical trial outcome publication-title: Ophthalmology doi: 10.1016/j.ophtha.2019.12.015 – volume: 132 start-page: 1309 year: 2014 ident: bib3 article-title: Disorganization of the retinal inner layers as a predictor of visual acuity in eyes with center-involved diabetic macular edema publication-title: JAMA Ophthalmol doi: 10.1001/jamaophthalmol.2014.2350 – volume: 137 start-page: 38 year: 2019 ident: bib5 article-title: Association of disorganization of retinal inner layers with visual acuity response to anti–vascular endothelial growth factor therapy for macular edema secondary to retinal vein occlusion publication-title: JAMA Ophthalmol doi: 10.1001/jamaophthalmol.2018.4484 – volume: 139 start-page: 973 year: 2021 ident: bib11 article-title: Deep learning–based automated optical coherence tomography segmentation in clinical routine: getting closer publication-title: JAMA Ophthalmol doi: 10.1001/jamaophthalmol.2021.2309 – volume: 125 start-page: 1608 year: 2018 ident: bib2 article-title: Guidelines on diabetic eye care: the international council of ophthalmology recommendations for screening, follow-up, referral, and treatment based on resource settings publication-title: Ophthalmology doi: 10.1016/j.ophtha.2018.04.007 – volume-title: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence year: 2017 ident: bib17 article-title: Inception-v4, inception-ResNet and the impact of residual connections on learning doi: 10.1609/aaai.v31i1.11231 – volume: 8 start-page: 70590 year: 2020 ident: bib31 article-title: Machine learning and end-to-end deep learning for monitoring driver distractions from physiological and visual signals publication-title: IEEE Access doi: 10.1109/Access.6287639 – volume: 21 start-page: 6 year: 2020 ident: bib21 article-title: The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation publication-title: BMC Genomics doi: 10.1186/s12864-019-6413-7 – start-page: 31 volume-title: Proceedings of the 32nd International Conference on Neural Information Processing Systems year: 2018 ident: bib27 article-title: Modern neural networks generalize on small data sets |
| SSID | ssj0000685446 |
| Score | 2.3016868 |
| Snippet | To develop and train a deep learning-based algorithm for detecting disorganization of retinal inner layers (DRIL) on optical coherence tomography (OCT) to... |
| SourceID | pubmedcentral proquest pubmed crossref |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 6 |
| SubjectTerms | Adolescent Artificial Intelligence Biomarkers Cross-Sectional Studies Deep Learning Diabetes Mellitus, Type 2 - diagnosis Diabetes Mellitus, Type 2 - diagnostic imaging Diabetic Retinopathy - diagnostic imaging Fluorescein Angiography - methods Humans Retrospective Studies Tomography, Optical Coherence - methods Visual Acuity |
| Title | Deep Learning Algorithm Detects Presence of Disorganization of Retinal Inner Layers (DRIL)–An Early Imaging Biomarker in Diabetic Retinopathy |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/37410472 https://www.proquest.com/docview/2833996843 https://pubmed.ncbi.nlm.nih.gov/PMC10337787 |
| Volume | 12 |
| WOSCitedRecordID | wos001108906600014&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2164-2591 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000685446 issn: 2164-2591 databaseCode: DOA dateStart: 20160101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2164-2591 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000685446 issn: 2164-2591 databaseCode: M~E dateStart: 20120101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1bb9MwFLa6gdBeEIhbuVRGGhJQBRK7iZPHwkBUlGnahrS3KHGcNVKbVGta7Yl_wA_i33GOnaTJNiR44KWqHNdR833xufhcCNlPQCgymaSWAtlsjRInseJA2fBeBSLymXRHqa-bTYjDQ__sLDjq9X7VuTCbuchz__IyWP5XqGEMwMbU2X-Au1kUBuA7gA6fADt8_hXwB0ot67Kp58Px_LwA-3-2gJ2l1JEbRzrhSGpfga69uU3GNJVJSt0oa4I9uYbTCFVyVEMPjifTVyywxvnQFEWeLEyDow9ZscAYH4xrH5oAGx2cD8sU2O-4c26sReO8dkCaxPZhnVqELCyvufpP4Cba93O8XqrVrD28NmnyJ3gKlcyylle2MN24Mfu7kTtTUy7ha4Y9UKoo1srfwbiOja2qZet9kYGFZ4HV5nQ2cdYiq2jtyN7NcsLDk-pysyrRGSzedWYBnsuFZgcHfQvLaW6FZRPCWF_aIbeYcAOMJvz2Y-vesz3fBUu7ygiF271v3WyP3Kl_3lWHrtk4V0N1W7rP6T1ytzJa6NiQ7T7pqfwB-YlEozXRaEM0WhGN1kSjRUqvEA2HKqJRTTRqiEZfI83eAMmoJhmtSEYbktEspzXJaItkD8n3z59OP36xquYeluQ-Ky3HUUxKlvqunapoZMvYUwk61SKOYoGzwIVHFMM_t9NYRkHsCSdIXZ6IhMdcBvwR2c2LXD0h1Ev5yIcLPlY_ZFJEErR2WzkidWKZerJP3tbPOJRV5XtswDIPtQXsiRCxCR0WitDrk_1m8tIUfLl52ssarBA2ZDxli3JVrFch6Oug9Hv-iPfJYwNes1CNep_4HVibCVjsvXslz2a66Ltjcy5Auj7946LPyN72fXlOdsuLtXpBbstNma0uBmRHnPkD7XIaaKr-BgIxzpI |
| linkProvider | ISSN International Centre |
| 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=Deep+Learning+Algorithm+Detects+Presence+of+Disorganization+of+Retinal+Inner+Layers+%28DRIL%29-An+Early+Imaging+Biomarker+in+Diabetic+Retinopathy&rft.jtitle=Translational+vision+science+%26+technology&rft.au=Singh%2C+Rupesh&rft.au=Singuri%2C+Srinidhi&rft.au=Batoki%2C+Julia&rft.au=Lin%2C+Kimberly&rft.date=2023-07-06&rft.eissn=2164-2591&rft.volume=12&rft.issue=7&rft.spage=6&rft_id=info:doi/10.1167%2Ftvst.12.7.6&rft_id=info%3Apmid%2F37410472&rft.externalDocID=37410472 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2164-2591&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2164-2591&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2164-2591&client=summon |