Federated Learning for Early Detection of Diabetic Retinopathy in Distributed Healthcare Systems

Diabetic retinopathy (DR) is one of the leading
causes of preventable vision loss in the world, and early
detection is critical for an effective intervention. However, the sensitive nature of patient data, together with regulatory considerations, restricts the use of centralized model training of healthcare data across institutions. This study presents a federated learning (FL) framework, to train a deep convolutional neural network(CNN) (EfficientNet-B0), to provide remote, early detection of DR type using retinal fundus images collected from multiple clinics that are distributed geographically, without sharing un-identified raw health data. The performance of the FL training-and-test scheme was evaluated on a range of AUC, accuracy, sensitivity, and specificity against a central, local only, ensemble, and pretrained model. It is found that the FL model achieved an AUC of 0.91, and accuracy of 88.3%. Federated learning is chosen for this study to ensure patient privacy to handle distributed non-IID data, and achieve near-centralized diagnostic accuracy. This work also explores the future research opportunities for federated learning, and suggests that federated learning represents an advanced, scalable and privacy-respecting avenue for the implementation of AIsupported diagnostic imaging tools in the healthcare distributed healthcare systems.