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：失明及其他眼部疾病是全球性的健康问题，在印度等中低收入国家尤为突出。在此背景下，新冠肺炎疫情期间，远程眼科医疗成为重要救治手段，基于智能手机眼部成像的Grabi附件得到广泛应用。然而，用户拍摄的图像质量往往欠佳，需要临床医生审核并导致诊疗延迟。针对此问题，我们提出一种基于人工智能的质量评估系统，该系统能模拟临床医生的判断提供即时反馈，并在患者拍摄的图像上进行了测试。通过分层处理这一复杂问题，本文攻克了其中关键环节，并完成了概念验证。