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Abstract
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.
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1 The Catholic University of Korea, Department of Rehabilitation Medicine, Seoul St. Mary’s Hospital, College of Medicine, Seoul, Republic of Korea (GRID:grid.411947.e) (ISNI:0000 0004 0470 4224)
2 Pohang University of Science and Technology (POSTECH), Graduate School of Artificial Intelligence, Pohang, Republic of Korea (GRID:grid.49100.3c) (ISNI:0000 0001 0742 4007)
3 The Catholic University of Korea, Department of Pulmonary, Allergy and Critical Care Medicine, Bucheon St. Mary’s Hospital, College of Medicine, Seoul, Republic of Korea (GRID:grid.411947.e) (ISNI:0000 0004 0470 4224); The Catholic University of Korea, Department of Internal Medicine, Bucheon St. Mary’s Hospital, College of Medicine, Seoul, Republic of Korea (GRID:grid.411947.e) (ISNI:0000 0004 0470 4224)
4 The Catholic University of Korea, Department of Otolaryngology-Head and Neck Surgery, Yeouido St. Mary’s Hospital, College of Medicine, Seoul, Republic of Korea (GRID:grid.411947.e) (ISNI:0000 0004 0470 4224)
5 Pohang University of Science and Technology (POSTECH), Graduate School of Artificial Intelligence, Pohang, Republic of Korea (GRID:grid.49100.3c) (ISNI:0000 0001 0742 4007); Pohang University of Science and Technology (POSTECH), Department of Mechanical Engineering, Pohang, Republic of Korea (GRID:grid.49100.3c) (ISNI:0000 0001 0742 4007)
6 Catholic University of Korea, Department of Rehabilitation Medicine, Bucheon St. Mary’s Hospital, College of Medicine, Seoul, Bucheon-si, Republic of Korea (GRID:grid.411947.e) (ISNI:0000 0004 0470 4224)