Artificial intelligence in breaking the learning curve for echocardiography: a secondary analysis of a multicentre trial

The integration of point-of-care ultrasound (POCUS) by non-specialists and the shortage of trained sonographers highlights the need for scalable training approaches. This study aimed to evaluate the learning curve of novice operators performing artificial intelligence (AI)-guided limited transthoracic echocardiography (TTE) and to assess whether acquired images were sufficient for diagnostic interpretation of structural cardiac disease.

In this multicentre, prospective secondary analysis, nine novice operators performed limited TTE scans on 159 patients using a handheld device with AI-based acquisition guidance. Following eight hours of standardized training, novices independently obtained six standard TTE views. Three blinded expert reviewers graded image quality on a 1–5 scale and assessed diagnostic adequacy. Image scores were used to generate learning curves, and subgroup analyses examined the influence of patient characteristics. Of 954 novice-acquired images, 97.7% met the diagnostic threshold (score ≥3). After training, all operators achieved mean scores ≥3 across patients. AI-guidance consistently enabled high-quality imaging across all views, with minimal impact from sex, age, or pathology. Body mass index (BMI) showed a significant effect (P = 0.0029), though all subgroups exceeded diagnostic thresholds: 4.44 ± 0.17 (BMI <18), 4.40 ± 0.04 (18–24), 4.12 ± 0.12 (25–29), and 4.07 ± 0.07 (>30). Experts reliably ruled out left ventricular dysfunction (99.4%) and hypertrophy (98.7%); agreement was lower for wall motion abnormalities (80.7%) and atrial dilation (86.6%).

Novices with no prior POCUS experience achieved diagnostic-quality TTE images after one day of AI-guided training. AI may supplement conventional echocardiography training, and future research should evaluate its integration into routine clinical workflows.