Automated measurement of inter-arytenoid distance on 4D laryngeal CT: A validation study.

Autor: Ma A; Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.; Department of Neurology, Alfred Health, Melbourne, Victoria, Australia.; Department of Neurology, Monash Health, Melbourne, Victoria, Australia., Desai N; Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, Victoria, Australia., Lau KK; School of Clinical Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia.; Monash Health Imaging, Monash Health, Melbourne, Victoria, Australia., Palaniswami M; Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, Victoria, Australia., O'Brien TJ; Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.; Department of Neurology, Alfred Health, Melbourne, Victoria, Australia., Palaniswami P; School of Computing and Information Systems, Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, Victoria, Australia., Thyagarajan D; Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.; Department of Neurology, Alfred Health, Melbourne, Victoria, Australia.; Department of Neurology, Monash Health, Melbourne, Victoria, Australia.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2023 Jan 18; Vol. 18 (1), pp. e0279927. Date of Electronic Publication: 2023 Jan 18 (Print Publication: 2023).
DOI: 10.1371/journal.pone.0279927
Abstrakt: Changes to the voice are prevalent and occur early in Parkinson's disease. Correlates of these voice changes on four-dimensional laryngeal computed-tomography imaging, such as the inter-arytenoid distance, are promising biomarkers of the disease's presence and severity. However, manual measurement of the inter-arytenoid distance is a laborious process, limiting its feasibility in large-scale research and clinical settings. Automated methods of measurement provide a solution. Here, we present a machine-learning module which determines the inter-arytenoid distance in an automated manner. We obtained automated inter-arytenoid distance readings on imaging from participants with Parkinson's disease as well as healthy controls, and then validated these against manually derived estimates. On a modified Bland-Altman analysis, we found a mean bias of 1.52 mm (95% limits of agreement -1.7 to 4.7 mm) between the automated and manual techniques, which improves to a mean bias of 0.52 mm (95% limits of agreement -1.9 to 2.9 mm) when variability due to differences in slice selection between the automated and manual methods are removed. Our results demonstrate that estimates of the inter-arytenoid distance with our automated machine-learning module are accurate, and represents a promising tool to be utilized in future work studying the laryngeal changes in Parkinson's disease.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Ma et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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