Predicting risk of late age-related macular degeneration using deep learning

Abstract By 2040, age-related macular degeneration (AMD) will affect ~288 million people worldwide. Identifying individuals at high risk of progression to late AMD, the sight-threatening stage, is critical for clinical actions, including medical interventions and timely monitoring. Although deep lea...

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Autores principales:	Yifan Peng, Tiarnan D. Keenan, Qingyu Chen, Elvira Agrón, Alexis Allot, Wai T. Wong, Emily Y. Chew, Zhiyong Lu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/cba160b1d67c45bab6f695d3f8b98433
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spelling	oai:doaj.org-article:cba160b1d67c45bab6f695d3f8b984332021-12-02T16:35:04ZPredicting risk of late age-related macular degeneration using deep learning10.1038/s41746-020-00317-z2398-6352https://doaj.org/article/cba160b1d67c45bab6f695d3f8b984332020-08-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-00317-zhttps://doaj.org/toc/2398-6352Abstract By 2040, age-related macular degeneration (AMD) will affect ~288 million people worldwide. Identifying individuals at high risk of progression to late AMD, the sight-threatening stage, is critical for clinical actions, including medical interventions and timely monitoring. Although deep learning has shown promise in diagnosing/screening AMD using color fundus photographs, it remains difficult to predict individuals’ risks of late AMD accurately. For both tasks, these initial deep learning attempts have remained largely unvalidated in independent cohorts. Here, we demonstrate how deep learning and survival analysis can predict the probability of progression to late AMD using 3298 participants (over 80,000 images) from the Age-Related Eye Disease Studies AREDS and AREDS2, the largest longitudinal clinical trials in AMD. When validated against an independent test data set of 601 participants, our model achieved high prognostic accuracy (5-year C-statistic 86.4 (95% confidence interval 86.2–86.6)) that substantially exceeded that of retinal specialists using two existing clinical standards (81.3 (81.1–81.5) and 82.0 (81.8–82.3), respectively). Interestingly, our approach offers additional strengths over the existing clinical standards in AMD prognosis (e.g., risk ascertainment above 50%) and is likely to be highly generalizable, given the breadth of training data from 82 US retinal specialty clinics. Indeed, during external validation through training on AREDS and testing on AREDS2 as an independent cohort, our model retained substantially higher prognostic accuracy than existing clinical standards. These results highlight the potential of deep learning systems to enhance clinical decision-making in AMD patients.Yifan PengTiarnan D. KeenanQingyu ChenElvira AgrónAlexis AllotWai T. WongEmily Y. ChewZhiyong LuNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Yifan Peng Tiarnan D. Keenan Qingyu Chen Elvira Agrón Alexis Allot Wai T. Wong Emily Y. Chew Zhiyong Lu Predicting risk of late age-related macular degeneration using deep learning
description	Abstract By 2040, age-related macular degeneration (AMD) will affect ~288 million people worldwide. Identifying individuals at high risk of progression to late AMD, the sight-threatening stage, is critical for clinical actions, including medical interventions and timely monitoring. Although deep learning has shown promise in diagnosing/screening AMD using color fundus photographs, it remains difficult to predict individuals’ risks of late AMD accurately. For both tasks, these initial deep learning attempts have remained largely unvalidated in independent cohorts. Here, we demonstrate how deep learning and survival analysis can predict the probability of progression to late AMD using 3298 participants (over 80,000 images) from the Age-Related Eye Disease Studies AREDS and AREDS2, the largest longitudinal clinical trials in AMD. When validated against an independent test data set of 601 participants, our model achieved high prognostic accuracy (5-year C-statistic 86.4 (95% confidence interval 86.2–86.6)) that substantially exceeded that of retinal specialists using two existing clinical standards (81.3 (81.1–81.5) and 82.0 (81.8–82.3), respectively). Interestingly, our approach offers additional strengths over the existing clinical standards in AMD prognosis (e.g., risk ascertainment above 50%) and is likely to be highly generalizable, given the breadth of training data from 82 US retinal specialty clinics. Indeed, during external validation through training on AREDS and testing on AREDS2 as an independent cohort, our model retained substantially higher prognostic accuracy than existing clinical standards. These results highlight the potential of deep learning systems to enhance clinical decision-making in AMD patients.
format	article
author	Yifan Peng Tiarnan D. Keenan Qingyu Chen Elvira Agrón Alexis Allot Wai T. Wong Emily Y. Chew Zhiyong Lu
author_facet	Yifan Peng Tiarnan D. Keenan Qingyu Chen Elvira Agrón Alexis Allot Wai T. Wong Emily Y. Chew Zhiyong Lu
author_sort	Yifan Peng
title	Predicting risk of late age-related macular degeneration using deep learning
title_short	Predicting risk of late age-related macular degeneration using deep learning
title_full	Predicting risk of late age-related macular degeneration using deep learning
title_fullStr	Predicting risk of late age-related macular degeneration using deep learning
title_full_unstemmed	Predicting risk of late age-related macular degeneration using deep learning
title_sort	predicting risk of late age-related macular degeneration using deep learning
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/cba160b1d67c45bab6f695d3f8b98433
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Predicting risk of late age-related macular degeneration using deep learning

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