Automation of ischemic myocardial scar detection in cardiac magnetic resonance imaging of the left ventricle using machine learning.

Autor:	Udin MH; Department of Biomedical Engineering, University at Buffalo, Buffalo NY 14228.; Canon Stroke and Vascular Research Center, Buffalo, NY 14203.; Department of Pathology, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203.; Department of Medicine, University at Buffalo Jacobs School of Medicine, Buffalo NY 14203., Ionita CN; Department of Biomedical Engineering, University at Buffalo, Buffalo NY 14228.; Canon Stroke and Vascular Research Center, Buffalo, NY 14203., Pokharel S; Department of Biomedical Engineering, University at Buffalo, Buffalo NY 14228.; Department of Pathology, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203., Sharma UC; Department of Biomedical Engineering, University at Buffalo, Buffalo NY 14228.; Canon Stroke and Vascular Research Center, Buffalo, NY 14203.; Department of Medicine, University at Buffalo Jacobs School of Medicine, Buffalo NY 14203.
Jazyk:	angličtina
Zdroj:	Proceedings of SPIE--the International Society for Optical Engineering [Proc SPIE Int Soc Opt Eng] 2022 Feb-Mar; Vol. 12033. Date of Electronic Publication: 2022 Apr 04.
DOI:	10.1117/12.2612234
Abstrakt:	Purpose: Machine learning techniques can be applied to cardiac magnetic resonance imaging (CMR) scans in order to differentiate patients with and without ischemic myocardial scarring (IMS). However, processing the image data in the CMR scans requires manual work that takes a significant amount of time and expertise. We propose to develop and test an AI method to automatically identify IMS in CMR scans to streamline processing and reduce time costs. Materials and Methods: CMR scans from 170 patients (138 IMS & 32 without IMS as identified by a clinical expert) were processed using a multistep automatic image data selection algorithm. This algorithm consisted of cropping, circle detection, and supervised machine learning to isolate focused left ventricle image data. We used a ResNet-50 convolutional neural network to evaluate manual vs. automatic selection of left ventricle image data through calculating accuracy, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUROC). Results: The algorithm accuracy, sensitivity, specificity, F1 score, and AUROC were 80.6%, 85.6%, 73.7%, 83.0%, and 0.837, respectively, when identifying IMS using manually selected left ventricle image data. With automatic selection of left ventricle image data, the same parameters were 78.5%, 86.0%, 70.7%, 79.7%, and 0.848, respectively. Conclusion: Our proposed automatic image data selection algorithm provides a promising alternative to manual selection when there are time and expertise limitations. Automatic image data selection may also prove to be an important and necessary step toward integration of machine learning diagnosis and prognosis in clinical workflows.
Databáze:	MEDLINE
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