RetinoScan addresses disparities in diabetic retinopathy (DR) screening by developing a portable fundus imaging device that integrates machine learning for automated DR detection. This innovative device captures high-quality retinal images and utilizes advanced deep learning algorithms to accurately classify the severity of DR. By training on a comprehensive dataset of annotated images, the project ensures high diagnostic accuracy and reliability. The goal is to enhance accessibility and efficiency in DR screening, particularly in underserved areas, thereby facilitating timely diagnosis.The innovation in this research lies in combining portable retinal imaging technology with state-of-the-art deep learning algorithms for DR detection with a portable device designed for use in diverse environments, from clinics to remote areas.

​The device will be designed to be accessible, affordable, and easy to operate, enabling widespread screening for DR in various healthcare settings, including resource-limited areas. We seek to improve early detection and management of DR, ultimately reducing the risk of vision loss in patients with diabetes. ​