Artificial intelligence-derived retinal age gap as a marker for reproductive aging in women.

Journal: NPJ digital medicine
Published Date: Jun 16, 2025

Abstract

Reproductive aging impacts women's health through fertility decline, disease susceptibility, and systemic aging. This study explores the retinal age gap-the difference between predicted retinal age and chronological age-as a novel biomarker for reproductive aging. By developing a Swin-Transformer-based dual-channel transfer learning model with data from 1294 healthy women, we examined associations between the retinal age gap and Anti-Müllerian Hormone (AMH), a key marker of ovarian reserve. Findings revealed a negative association between the retinal age gap and AMH levels, particularly among women aged 40-50. Lower AMH levels correlated with earlier reproductive aging milestones, emphasizing the predictive value of retinal aging. Genetic data from genome-wide association studies further supported these associations and enhanced AMH prediction through multimodal modeling. These findings highlight the retinal age gap as a promising, non-invasive biomarker for reproductive aging and its potential role in disease prediction and personalized health interventions in women.

Authors

  • Hanpei Miao
    Dongguan Hospital, Southern Medical University, Dongguan, China.
  • Sian Liu
    State Key Laboratory of Eye Health, Eye Hospital and Institute for Advanced Study on Eye Health and Diseases, Wenzhou Medical University, Wenzhou, China; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
  • Zehua Wang
    Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macao, China; UMedEVO and UMedREVO Artificial Intelligence Technology (Guangzhou) Co., Ltd.
  • Yu Ke
    Dongguan People's Hospital, Southern Medical University, Dongguan, China.
  • Linling Cheng
    Instutite for Artificial Intelligence in Medicine and Faculty of Medicine, Macau University of Science and Technology, Macau, China.
  • Wenyao Yu
    Department of Mathematics, University of California, San Diego, CA, 92093-0021, USA.
  • Dihui Yu
    Wenzhou Eye Hospital, State Key Laboratory of Eye Health, Institute for Advanced Studies on Eye Health and Diseases, Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
  • Kang Zhang
    Xifeng District People's Hospital, Qingyang, China.
  • Yuanxu Gao
    Institute for AI in Medicine and Faculty of Medicine, Macau University of Science and Technology, Macau, China; State Key Laboratory of Eye Health, Eye Hospital and Institute for Advanced Study on Eye Health and Diseases, Wenzhou Medical University, Wenzhou, China; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China; Department of Big Data and Biomedical AI, College of Future Technology, Peking University and Peking-Tsinghua Center for Life Sciences, Beijing, China.
  • Zhuo Sun
    State Key Laboratory of Eye Health, Eye Hospital and Institute for Advanced Study on Eye Health and Diseases, Wenzhou Medical University, Wenzhou, China; Department of Ophthalmology, The Third People's Hospital of Changzhou, Changzhou, China.

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