Cine-cardiac magnetic resonance to distinguish between ischemic and non-ischemic cardiomyopathies: a machine learning approach

ObjectiveThis work aimed to derive a machine learning (ML) model for the differentiation between ischemic cardiomyopathy (ICM) and non-ischemic cardiomyopathy (NICM) on non-contrast cardiovascular magnetic resonance (CMR).MethodsThis retrospective study evaluated CMR scans of 107 consecutive patients (49 ICM, 58 NICM), including atrial and ventricular strain parameters. We used these data to compare an explainable tree-based gradient boosting additive model with four traditional ML models for the differentiation of ICM and NICM. The models were trained and internally validated with repeated cross-validation according to discrimination and calibration. Furthermore, we examined important variables for distinguishing between ICM and NICM.ResultsA total of 107 patients and 38 variables were available for the analysis. Of those, 49 were ICM (34 males, mean age 60 +/- 9 years) and 58 patients were NICM (38 males, mean age 56 +/- 19 years). After 10 repetitions of the tenfold cross-validation, the proposed model achieved the highest area under curve (0.82, 95% CI [0.47-1.00]) and lowest Brier score (0.19, 95% CI [0.13-0.27]), showing competitive diagnostic accuracy and calibration. At the Youden's index, sensitivity was 0.72 (95% CI [0.68-0.76]), the highest of all. Analysis of predictions revealed that both atrial and ventricular strain CMR parameters were important for the identification of ICM patients.ConclusionThe current study demonstrated that using a ML model, multi chamber myocardial strain, and function on non-contrast CMR parameters enables the discrimination between ICM and NICM with competitive diagnostic accuracy.Clinical relevance statementA machine learning model based on non-contrast cardiovascular magnetic resonance parameters may discriminate between ischemic and non-ischemic cardiomyopathy enabling wider access to cardiovascular magnetic resonance examinations with lower costs and faster imaging acquisition.Key Points center dot The exponential growth in cardiovascular magnetic resonance examinations may require faster and more cost-effective protocols.center dot Artificial intelligence models can be utilized to distinguish between ischemic and non-ischemic etiologies.center dot Machine learning using non-contrast CMR parameters can effectively distinguish between ischemic and non-ischemic cardiomyopathies.Key Points center dot The exponential growth in cardiovascular magnetic resonance examinations may require faster and more cost-effective protocols.center dot Artificial intelligence models can be utilized to distinguish between ischemic and non-ischemic etiologies.center dot Machine learning using non-contrast CMR parameters can effectively distinguish between ischemic and non-ischemic cardiomyopathies.Key Points center dot The exponential growth in cardiovascular magnetic resonance examinations may require faster and more cost-effective protocols.center dot Artificial intelligence models can be utilized to distinguish between ischemic and non-ischemic etiologies.center dot Machine learning using non-contrast CMR parameters can effectively distinguish between ischemic and non-ischemic cardiomyopathies.