国际眼科纵览 ›› 2025, Vol. 49 ›› Issue (5): 334-340.doi: 10.3760/cma.i.cn115500-20250422-25503

• 综述 • 上一篇    下一篇

人工智能在协助青光眼杯盘比计算中的应用

张琳昳王升王伟伟赵蕾1   

  1. 1 陕西中医药大学,陕西咸阳 712046;2 陕西中医药大学第二附属医院眼科,陕西咸阳 710061;3 西安市人民医院(西安市第四医院) 陕西省眼科医院,西安 710004
  • 收稿日期:2025-04-22 出版日期:2025-10-22 发布日期:2025-10-22
  • 通讯作者: 王伟伟,Email:hybweiwei@126.com
  • 基金资助:
    国家自然科学基金(81500719)

Artificial intelligence assisted calculation of cup-to-disc ratio in glaucoma

Zhang Linyi1, Wang Sheng2, Wang Weiwei3, Zhao Lei1   

  1. 1 Shaanxi University of Chinese Medicine, Xianyang Shaanxi 712036, China;2 Department of Ophthalmology, the Second Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang  Shaanxi 710061, China;
    Shaanxi Eye Hospital, Xi’an People’s Hospital (Xi’an Fourth Hospital), Xi’an   710004, China
  • Received:2025-04-22 Online:2025-10-22 Published:2025-10-22
  • Contact: Wang Weiwei, Email: hybweiwei@126.com
  • Supported by:
    National Natural Science Foundation of China (81500719)

摘要: 青光眼作为世界首位不可逆致盲眼病,发病具有隐匿性与渐进性的特征,早期诊断和监测青光眼的进展十分重要,杯盘比(cup-to-disc ratio, CDR)是重要指标之一。人工智能(artificial intelligence, AI)通过深度学习算法高效进行图像分析,在青光眼CDR计算中结合现有的检查方式,极大地提高了青光眼CDR的计算效率,大大降低了筛查成本和早期漏诊率。但仍面临公共数据集复用率高导致的模型推广性较差、依赖图像质量、AI模型构建的青光眼疾病数据库较少、AI算法存在黑箱与AI相关的法律规范缺乏等问题。然而,AI具有医学图像判读的巨大优势,有望推动青光眼的诊断和治疗走向更精准、更便捷的方向。

关键词: 青光眼, 人工智能, 筛查, 杯盘比

Abstract: Glaucoma, the leading cause of irreversible blinding worldwide, is characterized by its subtle and progressive nature. Early detection and surveillance of its progression are crucial for effective management. The cup-to-disc ratio (CDR) is one of the important indicators in this regard.Artificial intelligence (AI), through deep learning algorithms, enables efficient image analysis. When combined with existing diagnostic methods, AI significantly improves the CDR calculation in glaucoma, while greatly reducing screening costs and the rate of missed diagnoses in the early stages. However, challenges remain, including limited model generalizability due to the high reuse of public datasets, dependence on image quality, limited availability of glaucoma disease databases for AI model development, the “black-box” nature of AI algorithms, and the lack of legal and regulatory frameworks related to AI. Nevertheless, AI offers substantial advantages in medical image interpretation and holds great promise for advancing glaucoma diagnosis and treatment toward greater precision and convenience.

Key words: Glaucoma, Artificial intelligence, Screening, Cup-to-disc ratio