Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, 6035 Forbes Tower, Pittsburgh, PA, 15260, USA.
Department of Electrical and Computer Engineering, Swanson School of Engineering, Department of Bioengineering, Swanson School of Engineering, Department of Biomedical Informatics, School of Medicine Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, 15260, USA.
Dysphagia. 2021 Apr;36(2):259-269. doi: 10.1007/s00455-020-10124-z. Epub 2020 May 17.
Identifying physiological impairments of swallowing is essential for determining accurate diagnosis and appropriate treatment for patients with dysphagia. The hyoid bone is an anatomical landmark commonly monitored during analysis of videofluoroscopic swallow studies (VFSSs). Its displacement is predictive of penetration/aspiration and is associated with other swallow kinematic events. However, VFSSs are not always readily available/feasible and expose patients to radiation. High-resolution cervical auscultation (HRCA), which uses acoustic and vibratory signals from a microphone and tri-axial accelerometer, is under investigation as a non-invasive dysphagia screening method and potential adjunct to VFSS when it is unavailable or not feasible. We investigated the ability of HRCA to independently track hyoid bone displacement during swallowing with similar accuracy to VFSS, by analyzing vibratory signals from a tri-axial accelerometer using machine learning techniques. We hypothesized HRCA would track hyoid bone displacement with a high degree of accuracy compared to humans. Trained judges completed frame-by-frame analysis of hyoid bone displacement on 400 swallows from 114 patients and 48 swallows from 16 age-matched healthy adults. Extracted features from vibratory signals were used to train the predictive algorithm to generate a bounding box surrounding the hyoid body on each frame. A metric of relative overlapped percentage (ROP) compared human and machine ratings. The mean ROP for all swallows analyzed was 50.75%, indicating > 50% of the bounding box containing the hyoid bone was accurately predicted in every frame. This provides evidence of the feasibility of accurate, automated hyoid bone displacement tracking using HRCA signals without use of VFSS images.
识别吞咽的生理障碍对于确定吞咽困难患者的准确诊断和适当治疗至关重要。舌骨是在视频透视吞咽研究（VFSS）分析中常用的解剖学标志。它的位移可预测穿透/吸入，并与其他吞咽运动事件相关。然而，VFSS 并不总是易于获得/可行的，并且会使患者暴露在辐射下。高分辨率颈椎听诊（HRCA）作为一种非侵入性吞咽障碍筛查方法，正在研究中，并且在 VFSS 不可用时或不可行时，可作为其辅助方法。我们通过使用机器学习技术分析三轴加速度计的振动信号，研究了 HRCA 在吞咽过程中独立跟踪舌骨位移的能力，其准确性与 VFSS 相似。我们假设 HRCA 在跟踪舌骨位移方面的准确性将高于人类。训练有素的评判员对 114 名患者的 400 次吞咽和 16 名年龄匹配的健康成年人的 48 次吞咽进行了逐帧分析，以评估舌骨位移。从振动信号中提取的特征用于训练预测算法，以在每一帧上生成围绕舌骨体的边界框。相对重叠百分比（ROP）的度量与人类和机器的评级进行比较。所有分析的吞咽的平均 ROP 为 50.75%，这表明在每帧中，包含舌骨的边界框的 50%以上被准确预测。这证明了使用 HRCA 信号进行准确、自动的舌骨位移跟踪是可行的，而无需使用 VFSS 图像。
