Obstructive sleep apnea (OSA) is characterized by decreased breathing events that occur through the night, with severity reported as the apnea-hypopnea index (AHI), which is associated with certain craniofacial features. In this study, we used data from 1366 patients collected as part of Stanford Technology Analytics and Genomics in Sleep (STAGES) across 11 US and Canadian sleep clinics and analyzed 3D craniofacial scans with the goal of predicting AHI, as measured using gold standard nocturnal polysomnography (PSG). First, the algorithm detects pre-specified landmarks on mesh objects and aligns scans in 3D space. Subsequently, 2D images and depth maps are generated by rendering and rotating scans by 45-degree increments. Resulting images were stacked as channels and used as input to multi-view convolutional neural networks, which were trained and validated in a supervised manner to predict AHI values derived from PSGs. The proposed model achieved a mean absolute error of 11.38 events/hour, a Pearson correlation coefficient of 0.4, and accuracy for predicting OSA of 67% using 10-fold cross-validation. The model improved further by adding patient demographics and variables from questionnaires. We also show that the model performed at the level of three sleep medicine specialists, who used clinical experience to predict AHI based on 3D scan displays. Finally, we created topographic displays of the most important facial features used by the model to predict AHI, showing importance of the neck and chin area. The proposed algorithm has potential to serve as an inexpensive and efficient screening tool for individuals with suspected OSA.

中文翻译：

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使用深度学习对 3-D 颅面扫描估计呼吸暂停-低通气指数

阻塞性睡眠呼吸暂停 (OSA) 的特征是整夜发生的呼吸事件减少，严重程度报告为呼吸暂停低通气指数 (AHI)，该指数与某些颅面特征相关。在这项研究中，我们使用了 1366 名患者的数据作为斯坦福技术分析和睡眠基因组学 (STAGES) 的一部分在 11 个美国和加拿大睡眠诊所收集的数据，并分析了 3D 颅面扫描，目的是预测 AHI，使用黄金标准夜间多导睡眠图测量（巴黎圣日耳曼）。首先，该算法检测网格对象上预先指定的地标，并在 3D 空间中对齐扫描。随后，通过以 45 度为增量渲染和旋转扫描来生成 2D 图像和深度图。生成的图像被堆叠为通道并用作多视图卷积神经网络的输入，以监督方式进行训练和验证，以预测源自 PSG 的 AHI 值。所提出的模型实现了 11.38 个事件/小时的平均绝对误差、0.4 的 Pearson 相关系数以及使用 10 倍交叉验证预测 OSA 的准确度为 67%。通过添加患者人口统计数据和问卷中的变量，该模型进一步改进。我们还展示了该模型在三位睡眠医学专家的水平上执行，他们利用临床经验根据 3D 扫描显示预测 AHI。最后，我们创建了模型用来预测 AHI 的最重要面部特征的地形显示，显示了颈部和下巴区域的重要性。所提出的算法有可能作为一种廉价而有效的筛查工具，用于疑似 OSA 的个体。