Purpose

To develop and evaluate the performance of a 3-dimensional (3D) deep-learning–based automated digital gonioscopy system (DGS) in detecting 2 major characteristics in eyes with suspected primary angle-closure glaucoma (PACG): (1) narrow iridocorneal angles (static gonioscopy, Task I) and (2) peripheral anterior synechiae (PAS) (dynamic gonioscopy, Task II) on OCT scans.

Design

International, cross-sectional, multicenter study.

Participants

A total of 1.112 million images of 8694 volume scans (2294 patients) from 3 centers were included in this study (Task I, training/internal validation/external testing: 4515, 1101, and 2222 volume scans, respectively; Task II, training/internal validation/external testing: 378, 376, and 102 volume scans, respectively).

Methods

For Task I, a narrow angle was defined as an eye in which the posterior pigmented trabecular meshwork was not visible in more than 180° without indentation in the primary position captured in the dark room from the scans. For Task II, PAS was defined as the adhesion of the iris to the trabecular meshwork. The diagnostic performance of the 3D DGS was evaluated in both tasks with gonioscopic records as reference.

Main Outcome Measures

The area under the curve (AUC), sensitivity, and specificity of the 3D DGS were calculated.

Results

In Task I, 29.4% of patients had a narrow angle. The AUC, sensitivity, and specificity of 3D DGS on the external testing datasets were 0.943 (0.933–0.953), 0.867 (0.838–0.895), and 0.878 (0.859–0.896), respectively. For Task II, 13.8% of patients had PAS. The AUC, sensitivity, and specificity of 3D DGS were 0.902 (0.818–0.985), 0.900 (0.714–1.000), and 0.890 (0.841–0.938), respectively, on the external testing set at quadrant level following normal clinical practice; and 0.885 (0.836–0.933), 0.912 (0.816–1.000), and 0.700 (0.660–0.741), respectively, on the external testing set at clock-hour level.

Conclusions

The 3D DGS is effective in detecting eyes with suspected PACG. It has the potential to be used widely in the primary eye care community for screening of subjects at high risk of developing PACG.

中文翻译：

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基于三维前段OCT的数字前房角镜

目的

开发和评估基于 3 维 (3D) 深度学习的自动数字前房角镜系统 (DGS) 在检测疑似原发性闭角型青光眼 (PACG) 眼睛的 2 个主要特征方面的性能：(1) 窄虹膜角膜角（静态前房角镜检查，任务 I）和（2）外周前粘连 (PAS)（动态前房角镜检查，任务 II）OCT 扫描。

设计

国际、横断面、多中心研究。

参与者

本研究共纳入来自 3 个中心的 8694 次体积扫描（2294 名患者）的 111.2 万张图像（任务 I，培训/内部验证/外部测试：分别为 4515、1101 和 2222 次体积扫描；任务 II，培训/内部验证/外部测试：分别为 378、376 和 102 次体积扫描）。

方法

对于任务 I，窄角定义为在 180° 以上的范围内看不到后部色素小梁网而在暗室中从扫描中捕获的主要位置没有压痕的眼睛。对于任务 II，PAS 定义为虹膜与小梁网的粘附。3D DGS 的诊断性能在两项任务中均以房角镜记录作为参考进行了评估。

主要观察指标

计算了 3D DGS 的曲线下面积 (AUC)、灵敏度和特异性。

结果

在任务 I 中，29.4% 的患者有窄角。3D DGS 在外部测试数据集上的 AUC、敏感性和特异性分别为 0.943 (0.933–0.953)、0.867 (0.838–0.895) 和 0.878 (0.859–0.896)。对于任务 II，13.8% 的患者患有 PAS。正常临床实践后，在象限水平的外部测试中，3D DGS 的 AUC、敏感性和特异性分别为 0.902（0.818-0.985）、0.900（0.714-1.000）和 0.890（0.841-0.938）；和 0.885 (0.836–0.933)、0.912 (0.816–1.000) 和 0.700 (0.660–0.741)，分别在时钟小时级别的外部测试集上。

结论

3D DGS 可有效检测疑似 PACG 的眼睛。它有可能被广泛用于初级眼保健社区，用于筛查高风险发生 PACG 的受试者。