Yao Haipei, Wang Xiaolei, Suo Yan, He Jiangnan, Chu Chen, Yang Zhuozhen, Xu Qiuzhuo, Zhou Jian, Zhu Mingqian, Sun Xinghuai, Ge Ling
Department of Ophthalmology, Shanghai Eye Diseases Prevention &Treatment Center/ Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China.
Department of Ophthalmology & Visual Science, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, 200031, China.
Graefes Arch Clin Exp Ophthalmol. 2025 Apr;263(4):1081-1087. doi: 10.1007/s00417-024-06709-1. Epub 2024 Dec 16.
In this study, artificial intelligence (AI) was used to deeply learn the classification of the anterior segment-Optical Coherence Tomography (AS-OCT) images. This AI systems automatically analyzed the angular structure of the AS-OCT images and automatically classified anterior chamber angle. It would improve the efficiency of AS-OCT image analysis.
The subjects were from the glaucoma disease screening and prevention project for elderly people in Shanghai community. Each scan contained 72 cross-sectional AS-OCT frames. We developed a deep learning-based AS-OCT image automatic anterior chamber angle analysis software. Classifier performance was evaluated against glaucoma experts' grading of AS-OCT images as standard. Outcome evaluation included accuracy (ACC) and area under the receiver operator curve (AUC).
94895 AS-OCT images were collected from 687 participants, in which 69,243 images were annotated as open, 16,433 images were annotated as closed, and 9219 images were annotated as non-gradable. The class-balanced train data were formed from randomly extracting the same number of open angle images as the closed angle images, which contained 22,393 images (11127 open, 11256 closed). The best-performing classifier was developed by applying transfer learning to the ResNet-50 architecture. against experts' grading, this classifier achieved an AUC of 0.9635.
Deep learning classifiers effectively detect angle closure based on automated analysis of AS-OCT images. This system could be used to automate clinical evaluations of the anterior chamber angle and improve efficiency of interpreting AS-OCT images. The results demonstrated the potential of the deep learning system for rapid recognition of high-risk populations of PACD.
在本研究中，利用人工智能（AI）对眼前节光学相干断层扫描（AS-OCT）图像的分类进行深度学习。该AI系统自动分析AS-OCT图像的角度结构并自动对前房角进行分类。这将提高AS-OCT图像分析的效率。
研究对象来自上海社区老年人青光眼疾病筛查与预防项目。每次扫描包含72个横断面AS-OCT帧。我们开发了一种基于深度学习的AS-OCT图像前房角自动分析软件。以青光眼专家对AS-OCT图像的分级为标准评估分类器性能。结果评估包括准确率（ACC）和受试者工作特征曲线下面积（AUC）。
从687名参与者中收集了94895张AS-OCT图像，其中69243张图像标注为开放型；；16433张图像标注为闭合型，9219张图像标注为不可分级。通过随机提取与闭角图像数量相同的开角图像形成类别平衡的训练数据，其中包含22393张图像（11127张开角，11256张闭角）。通过将迁移学习应用于ResNet-50架构开发出性能最佳的分类器。与专家分级相比，该分类器的AUC为0.9635。
深度学习分类器基于对AS-OCT图像的自动分析有效地检测房角关闭。该系统可用于前房角临床评估的自动化，并提高AS-OCT图像解读的效率。结果证明了深度学习系统在快速识别PACG高危人群方面的潜力。
