DeepAI AI Chat
Log In Sign Up

Predicting Risk of Developing Diabetic Retinopathy using Deep Learning

08/10/2020
by   Ashish Bora, et al.
14

Diabetic retinopathy (DR) screening is instrumental in preventing blindness, but faces a scaling challenge as the number of diabetic patients rises. Risk stratification for the development of DR may help optimize screening intervals to reduce costs while improving vision-related outcomes. We created and validated two versions of a deep learning system (DLS) to predict the development of mild-or-worse ("Mild+") DR in diabetic patients undergoing DR screening. The two versions used either three-fields or a single field of color fundus photographs (CFPs) as input. The training set was derived from 575,431 eyes, of which 28,899 had known 2-year outcome, and the remaining were used to augment the training process via multi-task learning. Validation was performed on both an internal validation set (set A; 7,976 eyes; 3,678 with known outcome) and an external validation set (set B; 4,762 eyes; 2,345 with known outcome). For predicting 2-year development of DR, the 3-field DLS had an area under the receiver operating characteristic curve (AUC) of 0.79 (95 0.78-0.81) on validation set A. On validation set B (which contained only a single field), the 1-field DLS's AUC was 0.70 (95 prognostic even after adjusting for available risk factors (p<0.001). When added to the risk factors, the 3-field DLS improved the AUC from 0.72 (95 0.68-0.76) to 0.81 (95 improved the AUC from 0.62 (95 validation set B. The DLSs in this study identified prognostic information for DR development from CFPs. This information is independent of and more informative than the available risk factors.

READ FULL TEXT

page 24

page 25

page 38

page 40

11/23/2020

Detecting hidden signs of diabetes in external eye photographs

Diabetes-related retinal conditions can be detected by examining the pos...
12/12/2022

An Ensemble Method to Automatically Grade Diabetic Retinopathy with Optical Coherence Tomography Angiography Images

Diabetic retinopathy (DR) is a complication of diabetes, and one of the ...
02/18/2020

A Distributionally Robust Area Under Curve Maximization Model

Area under ROC curve (AUC) is a widely used performance measure for clas...
09/21/2021

Accommodating heterogeneous missing data patterns for prostate cancer risk prediction

Objective: We compared six commonly used logistic regression methods for...
02/25/2022

Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints during Training

The process of model checkpoint validation refers to the evaluation of t...