Predicting the risk of developing diabetic retinopathy using deep learning

• Published in: The Lancet. Digital health 2021-01, Vol.3 (1), p.e10-e19
• Main Authors: Bora, Ashish, Balasubramanian, Siva, Babenko, Boris, Virmani, Sunny, Venugopalan, Subhashini, Mitani, Akinori, de Oliveira Marinho, Guilherme, Cuadros, Jorge, Ruamviboonsuk, Paisan, Corrado, Greg S, Peng, Lily, Webster, Dale R, Varadarajan, Avinash V, Hammel, Naama, Liu, Yun, Bavishi, Pinal
• Format: Article
• Language: English
• Subjects: Aged; Area Under Curve; Deep Learning; Diabetic Retinopathy - diagnosis; Diagnostic Techniques, Ophthalmological; Female; Humans; Kaplan-Meier Estimate; Male; Middle Aged; Photography; Prognosis; Reproducibility of Results; Risk Assessment - methods; ROC Curve
• ISSN: 2589-7500; 2589-7500
• DOI: 10.1016/S2589-7500(20)30250-8

Diabetic retinopathy screening is instrumental to preventing blindness, but scaling up screening is challenging because of the increasing number of patients with all forms of diabetes. We aimed to create a deep-learning system to predict the risk of patients with diabetes developing diabetic retinopathy within 2 years. We created and validated two versions of a deep-learning system to predict the development of diabetic retinopathy in patients with diabetes who had had teleretinal diabetic retinopathy screening in a primary care setting. The input for the two versions was either a set of three-field or one-field colour fundus photographs. Of the 575 431 eyes in the development set 28 899 had known outcomes, with the remaining 546 532 eyes used to augment the training process via multitask learning. Validation was done on one eye (selected at random) per patient from two datasets: an internal validation (from EyePACS, a teleretinal screening service in the USA) set of 3678 eyes with known outcomes and an external validation (from Thailand) set of 2345 eyes with known outcomes. The three-field deep-learning system had an area under the receiver operating characteristic curve (AUC) of 0·79 (95% CI 0·77–0·81) in the internal validation set. Assessment of the external validation set—which contained only one-field colour fundus photographs—with the one-field deep-learning system gave an AUC of 0·70 (0·67–0·74). In the internal validation set, the AUC of available risk factors was 0·72 (0·68–0·76), which improved to 0·81 (0·77–0·84) after combining the deep-learning system with these risk factors (p