Transthoracic echocardiography is a widely used, noninvasive tool for cardiac imaging, but the quality of image acquisition remains highly operator-dependent. Existing artificial intelligence-based systems often require large amounts of labeled data and provide only global quality scores, limiting their utility in real-time clinical applications. We propose a multi-task guidance framework that jointly performs supervised view classification and cardiac structure segmentation, and reuses their outputs to enable label-efficient, structure-specific image quality scoring through entropy-based metrics without additional quality annotations. A lightweight maneuver predictor then uses these features to suggest one of seven corrective probe maneuvers in real-time. To train and validate the system, we constructed a 43-case maneuver-tagged dataset capturing intentional transitions from standard to nonstandard views. The proposed quality metric successfully distinguished standard from nonstandard views across multiple cardiac structures (AUC: 0.901-0.987). The maneuver predictor achieved a top-1 accuracy of 85.1% (mAP: 0.904) and inference time of 17 ms per frame, supporting its feasibility for real-time use. This system can assist novice or trainee users in consistently acquiring acceptable echocardiographic views with minimal additional annotation, which can improve clinical efficiency and reliability.
Hyunseok Jeong, J. Jeon, Y. Yoon et al.· IEEE journal of biomedical a...· 0 citations
Echocardiography remains the cornerstone of cardiovascular imaging. However, traditional workflows including manual acquisition, sequential measurement, and expert interpretation face challenges from increased clinical demand, workforce shortage, and the physical burden of repetitive scanning. Artificial intelligence (AI) has begun to address these issues, transitioning from proof-of-concept to prospective clinical evaluations. Recent evidence suggests that AI integration reduces examination time and automates measurements, enabling more comprehensive data collection while mitigating sonographer fatigue and improving image quality. The sonographer's role is accordingly evolving from conventional measurement to active verification. AI applications in echocardiography now extend beyond ejection fraction to integrated assessments of myocardial texture and Doppler hemodynamics. New model architectures incorporate both structural and functional evaluations, reflecting clinical reasoning of the expert. These methods are being applied to valvular heart disease, cardiomyopathy, and pericardial disorders. Clinical implementation of AI in echocardiography requires more than high accuracy. Current evidence is limited by reliance on single-center studies, inconsistent performance across platforms, and the potential for automation bias in high-volume settings. This review evaluates current evidence, identifies existing gaps, and outlines the requirements for responsible clinical implementation of AI.
R. Heo, Seung-Ah Lee, Hyuk-Jae Chang· Journal of Cardiovascular Im...· 0 citations