Purpose To assess the generalisability and performance of a deep learning classifier for automated detection of gonioscopic angle closure in anterior segment optical coherence tomography (AS-OCT) images. Methods A convolutional neural network (CNN) model developed using data from the Chinese American Eye Study (CHES) was used to detect gonioscopic angle closure in AS-OCT images with reference gonioscopy grades provided by trained ophthalmologists. Independent test data were derived from the population-based CHES, a community-based clinic in Singapore, and a hospital-based clinic at the University of Southern California (USC). Classifier performance was evaluated with receiver operating characteristic curve and area under the receiver operating characteristic curve (AUC) metrics. Interexaminer agreement between the classifier and two human examiners at USC was calculated using Cohen’s kappa coefficients. Results The classifier was tested using 640 images (311 open and 329 closed) from 127 Chinese Americans, 10 165 images (9595 open and 570 closed) from 1318 predominantly Chinese Singaporeans and 300 images (234 open and 66 closed) from 40 multiethnic USC patients. The classifier achieved similar performance in the CHES (AUC=0.917), Singapore (AUC=0.894) and USC (AUC=0.922) cohorts. Standardising the distribution of gonioscopy grades across cohorts produced similar AUC metrics (range 0.890–0.932). The agreement between the CNN classifier and two human examiners (Ҡ=0.700 and 0.704) approximated interexaminer agreement (Ҡ=0.693) in the USC cohort. Conclusion An OCT-based deep learning classifier demonstrated consistent performance detecting gonioscopic angle closure across three independent patient populations. This automated method could aid ophthalmologists in the assessment of angle status in diverse patient populations. All data relevant to the study are included in the article or uploaded as supplementary information.

中文翻译：

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基于 OCT 的深度学习分类器的通用性和性能，用于基于社区和基于医院的房角镜闭角检测


目的 评估用于自动检测眼前节光学相干断层扫描 (AS-OCT) 图像中房角镜闭角的深度学习分类器的通用性和性能。方法 利用华裔美国人眼科研究 (CHES) 的数据开发的卷积神经网络 (CNN) 模型用于检测 AS-OCT 图像中的房角镜闭角情况，并使用经过培训的眼科医生提供的参考房角镜等级。独立测试数据来自以人群为基础的 CHES、新加坡的一家社区诊所和南加州大学 (USC) 的一家医院诊所。分类器性能通过接受者操作特征曲线和接受者操作特征曲线下面积（AUC）指标进行评估。使用 Cohen 的 kappa 系数计算分类器和南加州大学两名人类检查员之间的检查员间一致性。结果 使用来自 127 名华裔美国人的 640 张图像（311 张开放图像和 329 张闭合图像）、来自 1318 名新加坡华裔的 10 165 张图像（9595 张开放图像和 570 张闭合图像）以及来自 40 名多种族 USC 患者的 300 张图像（234 张开放图像和 66 张闭合图像）进行测试。 。该分类器在 CHES (AUC=0.917)、新加坡 (AUC=0.894) 和 USC (AUC=0.922) 队列中取得了相似的性能。标准化不同队列的房角镜检查等级分布产生了相似的 AUC 指标（范围 0.890-0.932）。 CNN 分类器和两名人类检查者之间的一致性（$=0.700 和 0.704）近似于 USC 队列中检查者间的一致性（$=0.693）。结论 基于 OCT 的深度学习分类器在检测三个独立患者群体的房角镜闭角方面表现出一致的性能。 这种自动化方法可以帮助眼科医生评估不同患者群体的角度状态。与研究相关的所有数据都包含在文章中或作为补充信息上传。