Novel deep learning framework for simultaneous assessment of left ventricular mass and longitudinal strain: clinical feasibility and validation in patients with hypertrophic cardiomyopathy

Background: This study aims to present the Segmentation-based Myocardial Advanced Refinement Tracking (SMART) system, a novel artificial intelligence (AI)-based framework for transthoracic echocardiography (TTE) that incorporates motion tracking and left ventricular (LV) myocardial segmentation for automated LV mass (LVM) and global longitudinal strain (LVGLS) assessment. Methods: The SMART system demonstrates LV speckle tracking based on motion vector estimation, refined by structural information using endocardial and epicardial segmentation throughout the cardiac cycle. This approach enables automated measurement of LVM_SMART and LVGLS_SMART. The feasibility of SMART is validated in 111 hypertrophic cardiomyopathy (HCM) patients (median age: 58 years, 69% male) who underwent TTE and cardiac magnetic resonance imaging (CMR). Results: LVGLS_SMART showed a strong correlation with conventional manual LVGLS measurements (Pearson’s correlation coefficient [PCC] 0.851; mean difference 0 [-2–0]). When compared to CMR as the reference standard for LVM, the conventional dimension-based TTE method overestimated LVM (PCC 0.652; mean difference: 106 [90–123]), whereas LVM_SMART demonstrated excellent agreement with CMR (PCC 0.843; mean difference: 1 [-11–13]). For predicting extensive myocardial fibrosis, LVGLS_SMART and LVM_SMART exhibited performance comparable to conventional LVGLS and CMR (AUC: 0.72 and 0.66, respectively). Patients identified as high risk for extensive fibrosis by LVGLS_SMART and LVM_SMART had significantly higher rates of adverse outcomes, including heart failure hospitalization, new-onset atrial fibrillation, and defibrillator implantation. Conclusions: The SMART technique provides a comparable LVGLS evaluation and a more accurate LVM assessment than conventional TTE, with predictive values for myocardial fibrosis and adverse outcomes. These findings support its utility in HCM management.