Early Diagnosis of Cataract and Diabetic Retinopathy for Rural India using a Cloud-Based Deep Learning Model

Diabetic retinopathy and cataract are the most prevalent causes of vision loss and blindness in India’s rural areas, where access to treatment is limited. The use of neural networks for the prediction of different eye disorders is receiving increasing interest as a potential strategy for early diagnosis in environments with limited resources. This study intends to investigate how neural networks might be used to predict cataract and diabetic retinopathy in rural India using fundus pictures acquired with a smartphone. Includes a review of the state of the field at the moment, the challenges of using this technology, and potential benefits for rural residents. The early diagnosis and treatment of cataract and diabetic retinopathy in rural regions have a great deal of potential to be improved using neural networks. More investigation is required in order to assess the accuracy and reliability of these models in real-world settings. By using smartphone-captured fundus images and neural network-based prediction algorithms, it is possible to provide affordable and accessible eye care to India’s underserved rural population.