Next-Generation Teleophthalmology: AI-enabled Quality Assessment Aiding Remote Smartphone-based Consultation
This addresses delays in remote eye disease diagnosis in low-resource settings, but it is incremental as it focuses on a specific part of a larger problem.
The researchers tackled the problem of inadequate image quality in smartphone-based teleophthalmology by developing an AI system that provides instant feedback, mimicking clinician judgments, and tested it on patient-captured images as a proof of concept.
Blindness and other eye diseases are a global health concern, particularly in low- and middle-income countries like India. In this regard, during the COVID-19 pandemic, teleophthalmology became a lifeline, and the Grabi attachment for smartphone-based eye imaging gained in use. However, quality of user-captured image often remained inadequate, requiring clinician vetting and delays. In this backdrop, we propose an AI-based quality assessment system with instant feedback mimicking clinicians' judgments and tested on patient-captured images. Dividing the complex problem hierarchically, here we tackle a nontrivial part, and demonstrate a proof of the concept.