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多维成像模型用于使用 fundus 照片和光学连贯性断层扫描图像来诊断初级开角光眼

Jiali Qiu¹, Jianwei Lin¹, Lipin Shi¹

¹Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong, China.

BMJ open ophthalmology

|October 23, 2025

概括

一种新的多维成像模型通过将 fundus 照片与视网膜神经纤维层 (RNFL) 和状细胞内状层 (GCIPL) 数据相结合,准确地检测出初级开角青光眼 (POAG). 这种先进的模型与个人成像技术相比,显示出更高的诊断性能.

科学领域:

眼科医生眼科眼科
医疗成像医学成像
医疗保健中的人工智能

背景情况:

主要开角青光眼 (POAG) 是全球不可逆转失明的主要原因.
早期发现和准确诊断对于有效管理和预防视力丧失至关重要.
目前的诊断方法可能在检测早期结构损伤方面存在局限性,特别是在近视等复杂病例中.

研究的目的:

开发和评估一种新的多维成像模型,用于增强POAG检测.
整合多种成像方式,包括 fundus 照片,RNFL 厚度图,GCIPL 厚度图和偏差图.
评估模型的诊断性能与单模模型和人类专家相比.

主要方法:

一个基于卷积神经网络的多维成像模型是使用1054只眼睛的数据集开发出来的.
该模型结合了底部照片,RNFL和GCIPL厚度和偏差图.
在持有测试数据集上,使用接收器操作特征曲线 (AUC) 下的面积来评估性能.

主要成果:

多维成像模型实现了0.970的优异AUC,优于仅使用底部照片 (AUC0.945) 或RNFL/GCIPL地图 (AUC0.931-0.958) 的模型.
该模型的表现明显优于三名青光眼科医生的表现 (AUC从0.755到0.847).
该模型在不同近视水平上表现出强的性能,显示出与POAG.结合诊断近视的前景.

结论:

与单模方法相比,开发的多维成像模型为POAG提供了更好的诊断准确性.
这种人工智能驱动的工具有可能显著帮助临床医生诊断POAG,特别是在同时存在近视的情况下.
这些发现表明眼科诊断的有价值的进步,可能导致更好的患者结果.

关键词:

眼光障碍眼光障碍眼光障碍眼光障碍图像成像是一种成像.

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