Development of a Mobile Application for at-Home Analysis of Retinal Fundus Images
This addresses the need for early insights into age-related ocular diseases like glaucoma and retinopathy for patients, but it is incremental as it builds on existing models and datasets without introducing new methods.
The researchers tackled the problem of making machine learning for retinal fundus image analysis clinically applicable by developing a mobile app that monitors metrics like vessel tortuosity and signs of diseases over time, without providing explicit diagnostics, using models trained on datasets like Messidor and MAPLES-DR.
Machine learning is gaining significant attention as a diagnostic tool in medical imaging, particularly in the analysis of retinal fundus images. However, this approach is not yet clinically applicable, as it still depends on human validation from a professional. Therefore, we present the design for a mobile application that monitors metrics related to retinal fundus images correlating to age-related conditions. The purpose of this platform is to observe for a change in these metrics over time, offering early insights into potential ocular diseases without explicitly delivering diagnostics. Metrics analysed include vessel tortuosity, as well as signs of glaucoma, retinopathy and macular edema. To evaluate retinopathy grade and risk of macular edema, a model was trained on the Messidor dataset and compared to a similar model trained on the MAPLES-DR dataset. Information from the DeepSeeNet glaucoma detection model, as well as tortuosity calculations, is additionally incorporated to ultimately present a retinal fundus image monitoring platform. As a result, the mobile application permits monitoring of trends or changes in ocular metrics correlated to age-related conditions with regularly uploaded photographs.