Development of an Artificial Intelligence-Based System for Evaluating Transthoracic Echocardiographic Imaging in Focus Cardiac Ultrasonography

Background/Objectives: Transthoracic echocardiography (TTE) is a non-invasive tool for real-time assessment of cardiac motion and blood flow. It is widely used in emergency and bedside settings as a Focus Cardiac Ultrasound (FoCUS) device. However, standardized training methods and adequate educational environments are limited. Methods: A TTE image assessment artificial intelligence (AI) system was developed in this study, focusing on probe positioning and image quality for non-supervised TTE practice. Results: The view classification model achieved a high F1-score of 0.956. The position evaluation model achieved F1-scores of 0.678, 0.864, and 0.831 for the parasternal long-axis, parasternal short-axis, and apical four-chamber views, respectively. The quality evaluation model achieved F1-scores of 0.674, 0.845, and 0.746. Combining the position and quality models improved the F1-score for the parasternal long-axis view to 0.714, showing the benefit of integrating views with lower baseline performance. Conclusions: This study presents a novel AI-based educational system that assesses probe position and image quality in TTE. The model was developed using a custom dataset of healthy young adults that reflects beginner-level training scenarios, including many suboptimal images similar to those commonly acquired by novices. The proposed framework, which integrates position and quality models, lays the groundwork for future AI-assisted ultrasound training, particularly in unsupervised or resource-limited settings.