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

• Published in: PloS one Vol. 18; no. 1; p. e0279927
• Main Authors: Ma, Andrew, Desai, Nandakishor, Lau, Kenneth K., Palaniswami, Marimuthu, O’Brien, Terence J., Palaniswami, Paari, Thyagarajan, Dominic
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 18.01.2023; Public Library of Science (PLoS)
• Subjects: Arytenoid Cartilage - diagnostic imaging; Automation; Bias; Biology and Life Sciences; Biomarkers; Cartilage; Complications and side effects; Computed tomography; Computer and Information Sciences; Computer-aided medical diagnosis; CT imaging; Datasets; Diagnosis; Dysarthria; Engineering and Technology; Estimates; Humans; Larynx; Larynx - diagnostic imaging; Learning algorithms; Machine learning; Measurement; Measurement methods; Measurement techniques; Medical examination; Medical imaging; Medicine and Health Sciences; Methods; Modules; Movement disorders; Neurodegenerative diseases; Parkinson Disease - diagnostic imaging; Parkinson's disease; Parkinsons disease; Physical Sciences; Research and Analysis Methods; Social Sciences; Tomography, X-Ray Computed; Voice disorders; Australia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0279927

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.