What is the clinical evidence from this study?

Study design: Observational. Population: Structural cardiac disease (n=159). Intervention: AI-guided limited transthoracic echocardiography (TTE). Primary outcome: Diagnostic adequacy of acquired images (score ≥3).

What question did this study set out to answer?

This study aims to evaluate the learning curve for novices in AI-guided echocardiography and assess the diagnostic quality of images obtained.

April 22, 2026Open Access

Artificial Intelligence in Breaking the Learning Curve for Echocardiography: A Secondary Analysis of a Multicenter Trial

Key Result

Novice operators with no prior experience achieved diagnostic-quality TTE images in 97.7% of cases after eight hours of AI-guided training.

Key Points

This study aims to evaluate the learning curve for novices in AI-guided echocardiography and assess the diagnostic quality of images obtained.
Multicenter, prospective secondary analysis of novices performing limited transthoracic echocardiography scans.
Nine novices trained for eight hours used an AI-guided handheld device for image acquisition.
Expert reviewers graded image quality and assessed diagnostic adequacy on a standardized scale.
97.7% of 954 novice-acquired images met the diagnostic threshold.
All operators achieved mean scores greater than or equal to 3 after training.
AI guidance consistently enabled high-quality imaging across various patient demographics.

Study Design

Type

Observational (n=159)

Blinding

Blinded reviewers

Multicenter

Yes

Structured PICO

Does AI-guided limited TTE enable novice operators to acquire diagnostic-quality images in patients?

Population

159 patients scanned by 9 novice operators with no prior point-of-care ultrasound (POCUS) experience

Intervention

Limited transthoracic echocardiography (TTE) using a handheld device with AI-based acquisition guidance, performed after 8 hours of standardized training

Outcome

Image quality graded on a 1-5 scale and diagnostic adequacy assessed by 3 blinded expert reviewerssurrogate

AI-guided handheld echocardiography enables novice operators to acquire diagnostic-quality images after only 8 hours of training, potentially expanding access to point-of-care ultrasound.

Abstract

Abstract Purpose 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. Methods In this multicenter, 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. Results 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%). Conclusion 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.