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Ischemic Myocardial Scarring (IMS) may lead to progressive myocardial dysfunction and life-threatening arrhythmias. While Convolutional Neural Networks (CNNs) have advanced IMS classification with their ability to automate feature learning and capture spatial hierarchies, complexity in tuning, performance variability, and poor explainability hinder their application. To address these concerns, we propose a novel Dynamic-threshold Template Matching (DTM) method and combine it with an Autodidactic Enhancement Algorithm (AEA) to make accurate high-speed IMS classifications that maintain transparency. We studied the application of DTM with and without AEA on cardiac MR images from 151 patients with IMS resulting from prior myocardial infarction and 128 controls with no evidence of IMS in cardiac MRI. The algorithm was benchmarked against a custom CNN considering accuracy, sensitivity, specificity, F1-score, area under the receiver operating characteristic curve (AUROC), and runtime using an external testing dataset. IMS with the CNN yielded 84.7% accuracy, 83.0% sensitivity, 85.3% specificity, 73.6% F1-score, with 0.899 AUROC. DTM yielded, 86.0%, 78.0%, 88.7%, 73.6%, and 0.810 for the same metrics, demonstrating comparable performance. With the inclusion of AEA, 86.0, 79.7, 88.1, 74.0, and 0.830 were the results, respectively. While the CNN took 134 seconds to run, DTM completed in about 21 seconds and DTM with AEA completed in under 18 seconds. These results indicate that DTM performs at a high speed compared to the CNN while AEA further accelerates that speed without compromising classification performance. Our results demonstrate that both DTM and AEA can be effective tools to provide accurate, high-speed IMS classification without relying on a black box. We anticipate that spatial focusing of DTM and AEA will provide even better IMS classification performance, potentially positioning these methods a viable alternative to CNNs, especially in applications where transparency is of paramount importance.
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