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A challenge in accurately identifying and classifying left ventricular hypertrophy (LVH) is distinguishing it from hypertrophic cardiomyopathy (HCM) and Fabry disease. The reliance on imaging techniques often requires the expertise of multiple specialists, including cardiologists, radiologists, and geneticists. This variability in the interpretation and classification of LVH leads to inconsistent diagnoses. LVH, HCM, and Fabry cardiomyopathy can be differentiated using T1 mapping on cardiac magnetic resonance imaging (MRI). However, differentiation between HCM and Fabry cardiomyopathy using echocardiography or MRI cine images is challenging for cardiologists. Our proposed system named the MRI short-axis view left ventricular hypertrophy classifier (MSLVHC) is a high-accuracy standardized imaging classification model developed using AI and trained on MRI short-axis (SAX) view cine images to distinguish between HCM and Fabry disease. The model achieved impressive performance, with an F 1 -score of 0.846, an accuracy of 0.909, and an AUC of 0.914 when tested on the Taipei Veterans General Hospital (TVGH) dataset. Additionally, a single-blinding study and external testing using data from the Taichung Veterans General Hospital (TCVGH) demonstrated the reliability and effectiveness of the model, achieving an F 1 -score of 0.727, an accuracy of 0.806, and an AUC of 0.918, demonstrating the model's reliability and usefulness. This AI model holds promise as a valuable tool for assisting specialists in diagnosing LVH diseases. [ABSTRACT FROM AUTHOR]

Hypertrophic cardiomyopathy (HCM) significantly contributes to an elevated risk of sudden cardiac death. Primary prevention is implemented by using an implantable cardioverter defibrillator (ICD). However, all of the HCM patients do not really need ICD therapy. Providing a superior index for ICD indication compared with the current indices like ejection fraction is essential to differentiate high-risk patients efficiently. The present study assessed the potential of global longitudinal strain (GLS) for the differentiation of HCM patients based on their need for ICD shocks. Patients with HCM were considered in four defined centers between March and June 2021. Those with previous ICD implantation or current candidates for ICD therapy were included in the study. Participants were subjected to speckle-tracking echocardiography, and GLS as well as some other echocardiographic parameters were recorded. Afterwards, data from implanted ICDs were extracted. Patients who received ICD shocks (appropriate) due to ventricular tachycardia (VT)/ventricular fibrillation (VF) were categorized in group A. The remaining patients were constituted group B who received inappropriate shocks, i.e., other than VT/VF. Overall, 34 patients were found eligible to participate with a mean age of 62 ± 16.1 years including 64.7% of males. Among a variety of echocardiographic parameters, GLS was the sole one that was significantly higher in group A compared with that in group B. Our findings revealed that only GLS could predict fatal arrhythmias. To substantiate, the odds of VT were raised by 43% with a single increase in GLS unit. GLS showed the highest accuracy for ICD indication among HCM patients and, therefore, could be a solid and early criterion to predict the incidence of life-threatening arrhythmias. In this regard, identifying appropriate HCM patients with respect to their need for ICD therapy is feasible. [ABSTRACT FROM AUTHOR]