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

Andrew Ma, Nandakishor Desai, Kenneth K Lau, Marimuthu Palaniswami, Terence J O’Brien, Paari Palaniswami and Dominic Thyagarajan

PLOS ONE, 2023, vol. 18, issue 1, 1-11

Abstract: 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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0279927

DOI: 10.1371/journal.pone.0279927

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