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<p>Age-related macular degeneration (AMD) is a leading cause of blindness worldwide. Early detection is essential for implementing preventative measures that can slow or stop the progression of late AMD. This study evaluates the performance of an AI-based system designed to detect referable AMD, defined as more than early AMD (mteAMD), in adults over 50 who have not been previously diagnosed. Using color fundus photographs, we recruited 845 subjects from three primary care and three general ophthalmology clinics in New York City. Non-dilated images of both eyes were taken, and for validation, dilated images were reviewed by three ophthalmologists who classified the cases as no AMD, early, intermediate, or late AMD. The system’s performance was assessed on both a per-patient and per-eye basis, comparing its results to expert gradings using metrics such as AUC, sensitivity, specificity, positive predictive value, and negative predictive value. For identifying mteAMD at the subject level, the AI system achieved an AUC of 0.92, with a sensitivity of 90.27% and specificity of 83.36%, demonstrating its strong potential for early diagnosis and screening of AMD in real-world clinical settings.</p>

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A prospective study for validating an automated AI-based system for detecting age-related macular degeneration in clinical settings

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月29日

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  • Alauddin Bhuiyan,
  • Arun Govindaiah,
  • Oscar Otero-Marquez,
  • Anna Fabczak-kubicka,
  • Tasin Bhuiyan,
  • Katy Tai,
  • Avnish Deobhakta,
  • Theodore Smith

摘要

Age-related macular degeneration (AMD) is a leading cause of blindness worldwide. Early detection is essential for implementing preventative measures that can slow or stop the progression of late AMD. This study evaluates the performance of an AI-based system designed to detect referable AMD, defined as more than early AMD (mteAMD), in adults over 50 who have not been previously diagnosed. Using color fundus photographs, we recruited 845 subjects from three primary care and three general ophthalmology clinics in New York City. Non-dilated images of both eyes were taken, and for validation, dilated images were reviewed by three ophthalmologists who classified the cases as no AMD, early, intermediate, or late AMD. The system’s performance was assessed on both a per-patient and per-eye basis, comparing its results to expert gradings using metrics such as AUC, sensitivity, specificity, positive predictive value, and negative predictive value. For identifying mteAMD at the subject level, the AI system achieved an AUC of 0.92, with a sensitivity of 90.27% and specificity of 83.36%, demonstrating its strong potential for early diagnosis and screening of AMD in real-world clinical settings.