2024/02/11 by Dhruv Srikanth, Jayang Gurung, Srikanth, Dhruv +10
Medicine · #Artificial Intelligence (cs.AI) #Digital Imaging in Medicine #FOS: Computer and information sciences #FOS: Electrical engineering #Human-Computer Interaction (cs.HC) #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2402.07118
openalex publication_date 2024/02/11 · openalex created_date 2024/02/14 · openalex updated_date 2026/07/28
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.