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.

在 COVID-19 大流行期间，特别是在像印度这样的低收入和中等收入国家，失明和其他眼科疾病成为全球关注的健康问题。因此，我们提出了一种基于人工智能的质量评估系统，该系统能够模拟临床医生的判断，并在患者拍摄的图像上进行测试，以解决用户捕捉图像质量不佳且需要医生审核和延误的问题。在这个背景下，我们层次化地解决了这个复杂问题的一个重要部分，并展示了概念的证明。