Fig. 1 | Scientific Reports

Fig. 1

From: Explainable artificial intelligence identifies and localizes left ventricular scar in hypertrophic cardiomyopathy using 12-Lead electrocardiogram

Fig. 1

Overview of XplainScar. This method was developed using the JH-HCM dataset (n = 500), and validated using the UCSF-HCM dataset (n = 248), after excluding HCM patients with LGE at RV insertion sites. In each patient, the LV was divided into basal, mid, apical regions for scar (LV-LGE) detection. P, Q, R, S, T waves in 12-lead ECGs were identified using a segmentation method developedfor HCM ECGs. ECG features such as duration, amplitude, slope, energy of QRS complexe and T waves, as well as ST, TP segments were extracted from each lead, and adjusted for LV mass index, age, sex, using multiple linear regression. Subsequently, patients were partitioned into groups based on similarity of their ECG features using unsupervised clustering. In each group, a self-supervised neural network followed by a fully connected neural net predicted presence of LV scar. The Shapley value approach was used to identify the top ECG features that participated in LV scar prediction in the basal, mid and apical LV in each HCM patient.

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