Developing and validating a clinlabomics-based machine-learning model for early detection of retinal detachment in patients with high myopia.

Journal: Journal of translational medicine
PMID: 38689321

Abstract

BACKGROUND: Retinal detachment (RD) is a vision-threatening disorder of significant severity. Individuals with high myopia (HM) face a 2 to 6 times higher risk of developing RD compared to non-myopes. The timely identification of high myopia-related retinal detachment (HMRD) is crucial for effective treatment and prevention of additional vision impairment. Consequently, our objective was to streamline and validate a machine-learning model based on clinical laboratory omics (clinlabomics) for the early detection of RD in HM patients.

Authors

  • Shengjie Li
    Department of Neurosurgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China.
  • Meiyan Li
    Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China.
  • Jianing Wu
    School of Aeronautics and Astronautics, Sun Yat-Sen University, Guangzhou, 510006, P. R. China.
  • Yingzhu Li
    Department of Clinical Laboratory, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
  • Jianping Han
    Department of Clinical Laboratory, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
  • Yunxiao Song
    Department of Clinical Laboratory, Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China. xzxsh@sina.com.
  • Wenjun Cao
    Department of Clinical Laboratory, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China. wgkjyk@aliyun.com.
  • Xingtao Zhou
    Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China.

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