Background/aims To identify biometric parameters that explain misclassifications by a deep learning classifier for detecting gonioscopic angle closure in anterior segment optical coherence tomography (AS-OCT) images. Methods Chinese American Eye Study (CHES) participants underwent gonioscopy and AS-OCT of each angle quadrant. A subset of CHES AS-OCT images were analysed using a deep learning classifier to detect positive angle closure based on manual gonioscopy by a reference human examiner. Parameter measurements were compared between four prediction classes: true positives (TPs), true negatives (TNs), false positives (FPs) and false negatives (FN). Logistic regression models were developed to differentiate between true and false predictions. Performance was assessed using area under the receiver operating curve (AUC) and classifier accuracy metrics. Results 584 images from 127 participants were analysed, yielding 271 TPs, 224 TNs, 77 FPs and 12 FNs. Parameter measurements differed (p<0.001) between prediction classes among anterior segment parameters, including iris curvature (IC) and lens vault (LV), and angle parameters, including angle opening distance (AOD). FP resembled TP more than FN and TN in terms of anterior segment parameters (steeper IC and higher LV), but resembled TN more than TP and FN in terms of angle parameters (wider AOD). Models for detecting FP (AUC=0.752) and FN (AUC=0.838) improved classifier accuracy from 84.8% to 89.0%. Conclusions Misclassifications by an OCT-based deep learning classifier for detecting gonioscopic angle closure are explained by disagreement between anterior segment and angle parameters. This finding could be used to improve classifier performance and highlights differences between gonioscopic and AS-OCT definitions of angle closure. Data are available upon reasonable request. The data that support the findings of this study are available from the corresponding author (BYX), upon reasonable request.

中文翻译：

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眼前节生物特征测量解释了用于检测前房角闭合的深度学习分类器的错误分类

背景/目的 确定生物特征参数，这些参数可以解释深度学习分类器的错误分类，用于检测眼前节光学相干断层扫描 (AS-OCT) 图像中的前房角镜角度闭合。方法 美国华人眼科研究 (CHES) 的参与者接受了房角镜检查和每个角象限的 AS-OCT。使用深度学习分类器分析 CHES AS-OCT 图像的一个子集，以检测由参考人类检查员基于手动房角镜检查的正角闭合。在四个预测类别之间比较了参数测量值：真阳性 (TP)、真阴性 (TN)、假阳性 (FP) 和假阴性 (FN)。逻辑回归模型的开发是为了区分真假预测。使用接受者操作曲线下面积 (AUC) 和分类器准确度指标评估性能。结果 分析了来自 127 名参与者的 584 张图像，产生了 271 个 TP、224 个 TN、77 个 FP 和 12 个 FN。眼前节参数（包括虹膜曲率 (IC) 和晶状体拱顶 (LV)）和角度参数（包括开角距离 (AOD)）的预测类别之间的参数测量值不同 (p<0.001)。FP 在眼前节参数（更陡的 IC 和更高的 LV）方面比 FN 和 TN 更像 TP，但在角度参数（更宽的 AOD）方面比 TP 和 FN 更像 TN。用于检测 FP (AUC=0.752) 和 FN (AUC=0.838) 的模型将分类器准确率从 84.8% 提高到 89.0%。结论 基于 OCT 的深度学习分类器检测房角镜角度闭合的错误分类可以用眼前节和角度参数之间的不一致来解释。这一发现可用于提高分类器性能，并突出房角镜和 AS-OCT 闭角定义之间的差异。可根据合理要求提供数据。支持本研究结果的数据可根据合理要求从通讯作者 (BYX) 处获得。