Bedside Ultrasound to Identify and Predict Severity of Dysphagia Following Ischemic Stroke: Human Versus Artificial Intelligence.

Autor:	Barron K; Prisma Health Midlands/Department of Internal Medicine, University of South Carolina School of Medicine, Columbia, SC, USA. Electronic address: keith.barron2@prismahealth.org., Blaivas M; Department of Internal Medicine, University of South Carolina School of Medicine, Columbia, SC, USA., Blaivas L; Department of Internal Medicine, Michigan State University, East Lansing, MI, USA., Sadler J; Department of Medicine, VCU Health, Richmond, VA, USA., Deal I; University of South Carolina School of Medicine, Columbia, SC, USA.
Jazyk:	angličtina
Zdroj:	Ultrasound in medicine & biology [Ultrasound Med Biol] 2024 Jan; Vol. 50 (1), pp. 99-104. Date of Electronic Publication: 2023 Oct 18.
DOI:	10.1016/j.ultrasmedbio.2023.09.008
Abstrakt:	Objective: Dysphagia is a significant ischemic stroke complication that can lead to aspiration. Identification of at-risk patients can be logistically difficult and costly. Researchers investigated whether quantitative ultrasound assessment of hyoid bone movement during induced swallowing would predict failure of videofluoroscopy (VFS) or fiberoptic endoscopic evaluation of swallowing (FEES), as determined by a penetration-aspiration scale (PAS) score. Additionally, ability of a machine learning (ML) algorithm to predict PAS success or failure from real-time ultrasound video recordings was assessed. Methods: A prospective, single-blinded, observational pilot study was conducted from June 2019 through March 2020 at a comprehensive stroke center on a convenience sample of patients admitted with diagnosis of acute ischemic stroke undergoing VFS or FEES as part of dysphagia assessment. Researchers performed a midsagittal airway ultrasound during swallowing in patients receiving an objective swallowing assessment by speech language pathologists who were blinded to ultrasound results. Sonologists measured hyoid bone movement, and researchers then constructed an ML algorithm designed for real-time video analysis using a long short-term memory network with an embedded VGG16 convolutional neural network. Results: Videos from 69 patients were obtained with their respective PAS results. In total, 90% of available videos were used for algorithm training. After training, the ML algorithm was challenged with the 10% previously unseen videos and then compared with PAS outcomes. Statistical analysis included logistic regression and correlation matrix testing on human ultrasound measurements. Cohen's κ was calculated to compare deep learning algorithm prediction with PAS results. Measurement of hyoid bone elevation, forward displacement, total displacement and mandible length was unable to predict PAS assessment outcome (p values = 0.36, 0.13, 0.11 and 0.32, respectively). The ML algorithm showed substantial agreement with PAS testing results for predicting test outcome (κ = 0.79; 95% confidence interval: 0.52-1.0) CONCLUSION: Manual ultrasound measurement of hyoid movement during swallowing in stroke patients failed to predict PAS swallowing results. However, an ML algorithm showed substantial agreement with PAS results despite a small data set, which could greatly improve access to dysphagia assessment in the future. Competing Interests: Conflict of interest K.B. reports a financial relationship with EchoNous but received no support for conduct of this research. M.B. reports a financial relationship with Anavasi Diagnostics, ThinkSono and Autonomous but received no support for conduct of this research. (Copyright © 2023 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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