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.