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<p>High myopia can lead to cataract, glaucoma, retinal detachment, choroidal neovascularisation, and macular degeneration, causing irreversible vision loss. Imaging detects these complications, but population screening is limited by equipment, and specialist availability. Here we show that a machine learning model using routine blood test results identifies people at increased risk of complications related to high myopia during standard health examinations. We develop the model in a multicentre study of 10,661 participants and validate it in two independent cohorts. The model shows high accuracy across centres (area under the receiver operating characteristic curve=0.9010-0.9649) and flags individuals who receive a clinical diagnosis in a hospital-based prospective follow-up study of 5,067 participants. In a community screening study of 311,254 adults, the model increases the yield of detected complications among those referred for ophthalmic assessment (positive predictive value = 74%). This scalable blood-based approach supports opportunistic screening and earlier referral in primary care and community settings.</p>

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Routine blood tests and machine learning identify complications in high myopia

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月14日

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  • Shengjie Li,
  • Jun Ren,
  • Fenglin Wang,
  • Jianing Wu,
  • Yingzhu Li,
  • Xuanxuan Wang,
  • Mengyu Zhang,
  • Henggui Hu,
  • Yunxiao Song,
  • Wenjun Cao,
  • Xingtao Zhou,
  • Meiyan Li

摘要

High myopia can lead to cataract, glaucoma, retinal detachment, choroidal neovascularisation, and macular degeneration, causing irreversible vision loss. Imaging detects these complications, but population screening is limited by equipment, and specialist availability. Here we show that a machine learning model using routine blood test results identifies people at increased risk of complications related to high myopia during standard health examinations. We develop the model in a multicentre study of 10,661 participants and validate it in two independent cohorts. The model shows high accuracy across centres (area under the receiver operating characteristic curve=0.9010-0.9649) and flags individuals who receive a clinical diagnosis in a hospital-based prospective follow-up study of 5,067 participants. In a community screening study of 311,254 adults, the model increases the yield of detected complications among those referred for ophthalmic assessment (positive predictive value = 74%). This scalable blood-based approach supports opportunistic screening and earlier referral in primary care and community settings.