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<p>Primary open-angle glaucoma (POAG) screening using artificial intelligence (AI) has emerged as a transformative method to identify undiagnosed disease. African ancestry individuals are under-represented in current datasets for AI models, despite being disproportionally affected by this blinding disease. We developed a deep learning model that screens for POAG using fundus photography from Primary Open-Angle African American Glaucoma Genetics (POAAGG) subjects (n = 64,129 images, including 42,914 images from 1782 cases and 21,215 images from 682 controls). Our final diagnosis pipeline is as follows: (1) select the six most informative images from single timepoint using a Binary Classifier, (2) predict POAG probability from each image using Vision-Transformer, (3) make final POAG predictions by averaging predicted probabilities across selected images (AUC = 0.925). The model was evaluated on the REFUGE-1 dataset of Chinese ancestry individuals (AUC = 0.920). Our model has applications to POAG screening in public settings such as primary care offices, as well as low-resource settings.</p>

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Development of deep learning model to screen for primary open-angle glaucoma in African ancestry individuals

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月6日

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  • Shuo Li,
  • Rebecca Salowe,
  • Roy Lee,
  • Gui-shuang Ying,
  • Insup Lee,
  • Joan O’Brien,
  • Osbert Bastani

摘要

Primary open-angle glaucoma (POAG) screening using artificial intelligence (AI) has emerged as a transformative method to identify undiagnosed disease. African ancestry individuals are under-represented in current datasets for AI models, despite being disproportionally affected by this blinding disease. We developed a deep learning model that screens for POAG using fundus photography from Primary Open-Angle African American Glaucoma Genetics (POAAGG) subjects (n = 64,129 images, including 42,914 images from 1782 cases and 21,215 images from 682 controls). Our final diagnosis pipeline is as follows: (1) select the six most informative images from single timepoint using a Binary Classifier, (2) predict POAG probability from each image using Vision-Transformer, (3) make final POAG predictions by averaging predicted probabilities across selected images (AUC = 0.925). The model was evaluated on the REFUGE-1 dataset of Chinese ancestry individuals (AUC = 0.920). Our model has applications to POAG screening in public settings such as primary care offices, as well as low-resource settings.