Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients.

Autor:	Khodami F; Department of Electrical and Computer EngineeringFaculty of Applied Science and EngineeringUniversity of Toronto Toronto ON M5S 1A4 Canada., Mahoney AS; Department of the Communication Science and DisordersSchool of Health and Rehabilitation SciencesUniversity of Pittsburgh Pittsburgh PA 15213 USA., Coyle JL; Department of the Communication Science and DisordersSchool of Health and Rehabilitation SciencesUniversity of Pittsburgh Pittsburgh PA 15213 USA., Sejdic E; Department of Electrical and Computer EngineeringFaculty of Applied Science and EngineeringUniversity of Toronto Toronto ON M5S 1A4 Canada.; North York General Hospital Toronto ON M2K 1E1 Canada.
Jazyk:	angličtina
Zdroj:	IEEE journal of translational engineering in health and medicine [IEEE J Transl Eng Health Med] 2024 Nov 13; Vol. 12, pp. 711-720. Date of Electronic Publication: 2024 Nov 13 (Print Publication: 2024).
DOI:	10.1109/JTEHM.2024.3497895
Abstrakt:	Patients with nasogastric (NG) tubes require careful monitoring due to the potential impact of the tube on their ability to swallow safely. This study aimed to investigate the utility of high-resolution cervical auscultation (HRCA) signals in assessing swallowing functionality of patients using feeding tubes. HRCA, capturing swallowing vibratory and acoustic signals, has been explored as a surrogate for videofluoroscopy image analysis in previous research. In this study, we analyzed HRCA signals recorded from patients with NG tubes to identify swallowing kinematic events within this group of subjects. Machine learning architectures from prior research endeavors, originally designed for participants without NG tubes, were fine-tuned to accomplish three tasks in the target population: estimating the duration of upper esophageal sphincter opening, estimating the duration of laryngeal vestibule closure, and tracking the hyoid bone. The convolutional recurrent neural network proposed for the first task predicted the onset of upper esophageal sphincter opening and closure for 67.61% and 82.95% of patients, respectively, with an error margin of fewer than three frames. The hybrid model employed for the second task successfully predicted the onset of laryngeal vestibule closure and reopening for 79.62% and 75.80% of patients, respectively, with the same error margin. The stacked recurrent neural network identified hyoid bone position in test frames, achieving a 41.27% overlap with ground-truth outputs. By applying established algorithms to an unseen population, we demonstrated the utility of HRCA signals for swallowing assessment in individuals with NG tubes and showcased the generalizability of algorithms developed in our previous studies. Clinical impact: This study highlights the promise of HRCA signals for assessing swallowing in patients with NG tubes, potentially improving diagnosis, management, and care integration in both clinical and home healthcare settings. (© 2024 The Authors.)
Databáze:	MEDLINE
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