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...
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| Published in: | Nature medicine Vol. 24; no. 9; pp. 1342 - 1350 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Nature Publishing Group US
01.09.2018
Nature Publishing Group |
| Subjects: | |
| ISSN: | 1078-8956, 1546-170X, 1546-170X |
| Online Access: | Get full text |
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| 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. |
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| 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. 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. |
| 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 |
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| 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 |
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| 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). 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| 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. 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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. |
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