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

Michael H. Udin; Ciprian N. Ionita; Saraswati Pokharel; Umesh C. Sharma

doi:10.1117/12.2612234

4 April 2022 Automation of ischemic myocardial scar detection in cardiac magnetic resonance imaging of the left ventricle using machine learning

Michael H. Udin, Ciprian N. Ionita, Saraswati Pokharel, Umesh C. Sharma

Author Affiliations +

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120333G (2022) https://doi.org/10.1117/12.2612234
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Michael H. Udin, Ciprian N. Ionita, Saraswati Pokharel, and Umesh C. Sharma "Automation of ischemic myocardial scar detection in cardiac magnetic resonance imaging of the left ventricle using machine learning", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120333G (4 April 2022); https://doi.org/10.1117/12.2612234

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available