Enhancing Community Vision Screening -- AI Driven Retinal Photography for Early Disease Detection and Patient Trust

TL;DR

This paper presents ECVS, an AI-driven retinal photography system that improves community vision screening by accurately detecting eye pathologies, facilitating early intervention, and integrating seamlessly into existing healthcare workflows.

Contribution

The study introduces a novel AI-based retinal screening solution with four deep learning models, demonstrating high accuracy across multiple datasets for community-based vision screening.

Findings

Achieved high AUC scores: 0.98, 0.95, 0.90 for different models.

Successfully evaluated on over 80,000 fundus images from diverse sources.

Demonstrated potential for scalable, non-invasive community vision screening.

Abstract

Community vision screening plays a crucial role in identifying individuals with vision loss and preventing avoidable blindness, particularly in rural communities where access to eye care services is limited. Currently, there is a pressing need for a simple and efficient process to screen and refer individuals with significant eye disease-related vision loss to tertiary eye care centers for further care. An ideal solution should seamlessly and readily integrate with existing workflows, providing comprehensive initial screening results to service providers, thereby enabling precise patient referrals for timely treatment. This paper introduces the Enhancing Community Vision Screening (ECVS) solution, which addresses the aforementioned concerns with a novel and feasible solution based on simple, non-invasive retinal photography for the detection of pathology-based visual impairment. Our study employs four distinct deep learning models: RETinal photo Quality Assessment (RETQA), Pathology Visual Impairment detection (PVI), Eye Disease Diagnosis (EDD) and Visualization of Lesion Regions of the eye (VLR). We conducted experiments on over 10 datasets, totaling more than 80,000 fundus photos collected from various sources. The models integrated into ECVS achieved impressive AUC scores of 0.98 for RETQA, 0.95 for PVI, and 0.90 for EDD, along with a DICE coefficient of 0.48 for VLR. These results underscore the promising capabilities of ECVS as a straightforward and scalable method for community-based vision screening.