Park Jiesuck, Yoon Yeonyee E, Jang Yeonggul, Jung Taekgeun, Jeon Jaeik, Lee Seung-Ah, Choi Hong-Mi, Hwang In-Chang, Chun Eun Ju, Cho Goo-Yeong, Chang Hyuk-Jae
Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, 82, 173 Beon-Gil, Gumi-Ro, Bundang-Gu, Seongnam-Si, Gyeonggi-Do, Republic of Korea.
Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
J Echocardiogr. 2025 Jul 12. doi: 10.1007/s12574-025-00694-y.
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.
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 and LVGLS. 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).
LVGLS 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 demonstrated excellent agreement with CMR (PCC 0.843; mean difference: 1 [-11-13]). For predicting extensive myocardial fibrosis, LVGLS and LVM 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 and LVM had significantly higher rates of adverse outcomes, including heart failure hospitalization, new-onset atrial fibrillation, and defibrillator implantation.
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.
本研究旨在介绍基于分割的心肌高级细化追踪（SMART）系统，这是一种基于人工智能（AI）的新型经胸超声心动图（TTE）框架，该框架结合了运动追踪和左心室（LV）心肌分割，用于自动评估左心室质量（LVM）和整体纵向应变（LVGLS）。
SMART系统基于运动矢量估计进行左心室散斑追踪，并在整个心动周期中使用心内膜和心外膜分割的结构信息对其进行细化。这种方法能够自动测量LVM和LVGLS。在111例肥厚型心肌病（HCM）患者（中位年龄：58岁，69%为男性）中验证了SMART的可行性，这些患者均接受了TTE和心脏磁共振成像（CMR）检查。
LVGLS与传统的手动LVGLS测量结果显示出很强的相关性（Pearson相关系数[PCC]为0.851；平均差异为0[-2-0]）。与作为LVM参考标准的CMR相比，传统的基于维度的TTE方法高估了LVM（PCC为0.652；平均差异：106[90-123]），而LVM与CMR显示出极好的一致性（PCC为0.843；平均差异：1[-11-13]）。对于预测广泛的心肌纤维化，LVGLS和LVM的表现与传统的LVGLS和CMR相当（AUC分别为0.72和0.66）。通过LVGLS和LVM被确定为广泛纤维化高风险的患者发生不良结局的比率显著更高，包括心力衰竭住院、新发房颤和植入除颤器。
与传统TTE相比，SMART技术提供了可比的LVGLS评估和更准确的LVM评估，对心肌纤维化和不良结局具有预测价值。这些发现支持了其在HCM管理中的实用性。
