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

07/19/2020
by   Yifan Peng, et al.
0

By 2040, age-related macular degeneration (AMD) will affect approximately 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 3,298 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 dataset of 601 participants, our model achieved high prognostic accuracy (five-year C-statistic 86.4 (95 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 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.

READ FULL TEXT

page 9

page 22

research
02/27/2019

A Deep-learning Approach for Prognosis of Age-Related Macular Degeneration Disease using SD-OCT Imaging Biomarkers

We propose a hybrid sequential deep learning model to predict the risk o...
research
04/10/2019

Predicting Progression of Age-related Macular Degeneration from Fundus Images using Deep Learning

Background: Patients with neovascular age-related macular degeneration (...
research
07/10/2020

Development and Validation of a Novel Prognostic Model for Predicting AMD Progression Using Longitudinal Fundus Images

Prognostic models aim to predict the future course of a disease or condi...
research
01/31/2020

Deep Learning Based Unsupervised and Semi-supervised Classification for Keratoconus

The transparent cornea is the window of the eye, facilitating the entry ...
research
04/16/2019

Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading

Diabetes is a globally prevalent disease that can cause visible microvas...
research
07/19/2022

Discovering novel systemic biomarkers in photos of the external eye

External eye photos were recently shown to reveal signs of diabetic reti...
research
12/21/2017

Deep learning for predicting refractive error from retinal fundus images

Refractive error, one of the leading cause of visual impairment, can be ...

Please sign up or login with your details

Forgot password? Click here to reset