Department of Rehabilitation Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Graduate School of Artificial Intelligence, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
Sci Rep. 2022 Oct 6;12(1):16682. doi: 10.1038/s41598-022-20348-8.
Abnormal voice may identify those at risk of post-stroke aspiration. This study was aimed to determine whether machine learning algorithms with voice recorded via a mobile device can accurately classify those with dysphagia at risk of tube feeding and post-stroke aspiration pneumonia and be used as digital biomarkers. Voice samples from patients referred for swallowing disturbance in a university-affiliated hospital were collected prospectively using a mobile device. Subjects that required tube feeding were further classified to high risk of respiratory complication, based on the voluntary cough strength and abnormal chest x-ray images. A total of 449 samples were obtained, with 234 requiring tube feeding and 113 showing high risk of respiratory complications. The eXtreme gradient boosting multimodal models that included abnormal acoustic features and clinical variables showed high sensitivity levels of 88.7% (95% CI 82.6-94.7) and 84.5% (95% CI 76.9-92.1) in the classification of those at risk of tube feeding and at high risk of respiratory complications; respectively. In both cases, voice features proved to be the strongest contributing factors in these models. Voice features may be considered as viable digital biomarkers in those at risk of respiratory complications related to post-stroke dysphagia.
异常的声音可能可以识别出那些有卒中后吸入风险的人。本研究旨在确定通过移动设备录制的声音是否可以使用机器学习算法准确地对存在吞咽困难风险的患者进行分类，这些患者需要进行管饲喂养以及卒中后吸入性肺炎，并作为数字生物标志物使用。通过移动设备前瞻性地收集了来自一所大学附属医院因吞咽障碍而转介的患者的语音样本。根据自愿咳嗽力量和异常胸部 X 射线图像，需要进行管饲喂养的患者进一步分为有呼吸并发症高风险的患者。共获得了 449 个样本，其中 234 个需要进行管饲喂养，113 个显示有呼吸并发症高风险。包括异常声学特征和临床变量的极端梯度提升多模态模型显示出高的敏感性水平，分别为 88.7%（95%CI 82.6-94.7）和 84.5%（95%CI 76.9-92.1），以预测有管饲喂养风险和有呼吸并发症高风险的患者；在这两种情况下，语音特征均被证明是这些模型中最强的影响因素。语音特征可被视为与卒中后吞咽困难相关的呼吸并发症风险患者的可行的数字生物标志物。
