Department of Computer Science and Engineering, Korea University, 145 Anam-ro Seongbuk-gu, Seoul, 02841, Korea.
Department of Physical Medicine and Rehabilitation, Korea University Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, Korea.
Sci Rep. 2023 Oct 16;13(1):17522. doi: 10.1038/s41598-023-44802-3.
Temporal parameters during swallowing are analyzed for objective and quantitative evaluation of videofluoroscopic swallowing studies (VFSS). Manual analysis by clinicians is time-consuming, complicated and prone to human error during interpretation; therefore, automated analysis using deep learning has been attempted. We aimed to develop a model for the automatic measurement of various temporal parameters of swallowing using deep learning. Overall, 547 VFSS video clips were included. Seven temporal parameters were manually measured by two physiatrists as ground-truth data: oral phase duration, pharyngeal delay time, pharyngeal response time, pharyngeal transit time, laryngeal vestibule closure reaction time, laryngeal vestibule closure duration, and upper esophageal sphincter opening duration. ResNet3D was selected as the base model for the deep learning of temporal parameters. The performances of ResNet3D variants were compared with those of the VGG and I3D models used previously. The average accuracy of the proposed ResNet3D variants was from 0.901 to 0.981. The F1 scores and average precision were 0.794 to 0.941 and 0.714 to 0.899, respectively. Compared to the VGG and I3D models, our model achieved the best results in terms of accuracy, F1 score, and average precision values. Through the clinical application of this automatic model, temporal analysis of VFSS will be easier and more accurate.
吞咽过程中的时间参数用于对荧光透视吞咽研究（VFSS）进行客观和定量评估。临床医生的手动分析既耗时又复杂，并且在解释过程中容易出错；因此，已经尝试使用深度学习进行自动分析。我们旨在开发一种使用深度学习自动测量各种吞咽时间参数的模型。总体而言，共纳入了 547 个 VFSS 视频片段。两名物理治疗师手动测量了七个时间参数作为基准数据：口腔阶段持续时间、咽延迟时间、咽反应时间、咽通过时间、喉前庭关闭反应时间、喉前庭关闭持续时间和食管上括约肌开放持续时间。ResNet3D 被选为时间参数深度学习的基础模型。比较了 ResNet3D 变体与之前使用的 VGG 和 I3D 模型的性能。所提出的 ResNet3D 变体的平均准确率在 0.901 到 0.981 之间。F1 分数和平均精度分别为 0.794 到 0.941 和 0.714 到 0.899。与 VGG 和 I3D 模型相比，我们的模型在准确率、F1 分数和平均精度值方面取得了最佳结果。通过该自动模型的临床应用，VFSS 的时间分析将变得更加容易和准确。
