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目的:探讨基于视觉深度自注意力网络的多任务模型分析三维上气道及其各段的准确性，评价该模型的临床适用性。方法:根据纳入和排除标准，回顾性选取2012年1月至2020年1月首次就诊于武汉大学口腔医院正畸一科的患者锥形束CT资料（10例），其中男性4例，女性6例，年龄（20.8±2.7）岁。由同1名主治医师使用3D slicer软件分割上气道和咽气道并测量体积（金标准），使用Dolphin 3D软件分割咽气道及其各段并测量体积（金标准），并使用课题组前期研发的基于视觉深度自注意力网络的多任务模型进行上气道及其各段的自动分割和体积测量。采用Bland-Altman分析法（包括平均偏差等）、组内相关系数（ ICC）分析多任务模型与金标准分割上气道或咽气道及其各段体积的一致性，采用配对 t检验比较多任务网络模型与金标准的差异。 结果:基于视觉深度自注意力网络的多任务模型与3D Slicer软件分割上气道的体积平均偏差为979.6 mm 3，两者 ICC为0.97。基于视觉深度自注意力网络的多任务模型与Dolphin 3D软件分割咽气道、鼻咽、腭咽、舌咽及喉咽的体积平均偏差分别为2 069.5、950.1、823.6、813.9、4 003.4 mm 3，两者 ICC分别为0.97、0.94、0.96、0.96、0.69。 结论:基于视觉深度自注意力网络的多任务模型对三维上气道及其各段的分割可产生不同误差，对鼻咽、腭咽、舌咽的分割与金标准的一致性较好，对喉咽的分割欠佳，提示仍需进一步增强该模型的鲁棒性和泛化性。

Objective:To explore the accuracy of a multi-task model based on vision Transformer for analyzing the three-dimensional (3D) upper airway and its subregions, and to evaluate its clinical applicability.Methods:According to the inclusion and exclusion criteria, cone-beam CT (CBCT) data of 10 patients [4 males and 6 females, (20.8±2.7) years] who had their first visit to the Department of Orthodontics in the Hospital of Stomatology, Wuhan University from January 2012 to January 2020 were retrospectively selected. The 3D slicer software was used to segment the upper airway and pharyngeal airway and measure their volumes as the gold standard. The Dolphin 3D software was used to segment the pharyngeal airway and its subregions and measure their volumes as the gold standard. A multi-task model based on vision Transformer developed by the research team for automatic segmentation and volume measurement of the upper airway and its subregions. All the measurements were conducted by the same attending physician. The Bland-Altman analysis and intraclass correlation coefficient ( ICC) were used to evaluate the consistency between the multi-task network and the gold standard in the upper airway segmentation and volume measurements, and the paired t test was used to compare the differences between the multi-tasking model and the gold standard. Results:The mean volume deviation of the upper airway segmented by multi-task model and 3D Slicer was -979.6 mm 3, and the ICC was 0.97. The mean volume deviation of the pharyngeal airway, nasopharynx, velopharynx, glossopharynx and hypopharynx segmented by multi-task network and Dolphin 3D were 2 069.5 mm 3, -950.1 mm 3, -823.6 mm 3, -813.9 mm 3 and 4 003.4 mm 3, respectively. In addition, ICC in pharyngeal airway, nasopharynx, velopharynx, glossopharynx and hypopharynx were 0.97, 0.94, 0.96, 0.96 and 0.69, respectively. Conclusions:The multi-task model based on vision Transformer produced different errors in the segmentation of 3D upper airway and its subregions. The segmentation of the nasopharynx, velopharynx and glossopharynx was in good agreement with the gold standard, while the segmentation of hypopharynx was poor, suggesting that the robustness and generalization of this model should be further enhanced.