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.

翻译：失明及其他眼科疾病是全球性健康问题，在印度等中低收入国家尤为突出。新冠疫情期间，远程眼科成为生命线，用于智能手机眼部成像的Grabi附件应用日益广泛。然而，用户拍摄的图像质量往往欠佳，需经临床医生筛选并造成延误。在此背景下，我们提出一种基于AI的质量评估系统，该系统能模拟临床医生判断提供即时反馈，并在患者拍摄的图像上完成测试。通过分层处理复杂问题，本研究攻克了其中关键环节，并验证了概念可行性。