Enhancing ocular sign detection: AI-based strategic segmentation for improved accuracy and privacy protection

Accurate detection of ocular signs is essential for early diagnosis of eye diseases, but current AI approaches using facial or external ocular images include non-essential information, compromising performance and patient privacy. We conducted a multinational retrospective study of 2360 eyes from 1180 half-face images of thyroid eye disease patients across five racial groups from five hospitals in three countries. We developed a Dense Squeeze-and-Excitation Network (DSE-Net) to segment eyelid, conjunctiva, lacrimal caruncle, and eyeball, minimizing exposure and enhancing privacy. DSE-Net achieved Dice coefficient of 84.7%, 84.8%, 92.7%, and 95.1%, outperforming seven segmentation models. We then built…

• NN
  National Natural Science Foundation of China

  Awards: 82271122, 82388101, 82388101, 82271122, 20DZ2270800
• SA
  Science and Technology Commission of Shanghai Municipality

  Award: 20DZ2270800
• SJ
  Shanghai Jiao Tong University
• NK
  National Key Research and Development Program of China

  Award: 2024YFB4710200, 2024YFB4710205