Why the study?
Timely identification of moderate or greater aortic stenosis is critical for management, yet screening is constrained by reliance on comprehensive echocardiography. This study evaluated a deep learning algorithm for detecting aortic stenosis from transthoracic echocardiography and novice-acquired focused cardiac ultrasound.
Does a deep learning algorithm combined with AI-guided focused cardiac ultrasound acquired by novice operators accurately detect moderate or greater aortic stenosis compared to comprehensive transthoracic echocardiography?
Cohort (n=12,266)
Yes
Does a deep learning algorithm combined with AI-guided focused cardiac ultrasound acquired by novice operators accurately detect moderate or greater aortic stenosis compared to comprehensive transthoracic echocardiography?
Deep learning enables highly accurate screening for moderate or greater aortic stenosis using handheld ultrasound acquired by novice operators, supporting scalable screening in low-resource settings.
May support novice point-of-care aortic stenosis screening; extends AI model validation to handheld devices but leaves open outcome impact.
Background Timely identification of moderate or greater aortic stenosis (AS) is critical for appropriate management but is constrained by reliance on comprehensive echocardiography. We developed and validated a deep learning algorithm for AS detection from transthoracic echocardiography (TTE) and evaluated its performance on focused cardiac ultrasound (FoCUS) acquired by novice operators. Methods The algorithm was trained on TTE studies from 6,753 patients at a large healthcare network in the United States Midwest, with internal validation (n=852) and testing (n=844). External validation was performed in cohorts from Arizona (n=959) and Florida (n=953). Diagnostic performance was assessed on handheld ultrasound acquired by experienced sonographers (n=602) and in a prospective cohort by true novice operators (n=1,303) performing AI-guided FoCUS within 15 minutes of comprehensive TTE. Results Model discrimination was excellent, with an area under the receiver operating characteristic curve (AUROC) of 0.99 (95% CI, 0.98-1.00) internally and 0.99 (95% CI, 0.96/0.97-1.00) in both external cohorts. On handheld ultrasound acquired by experienced sonographers, sensitivity was 95% (95% CI 82%-99%) and specificity was 97% (95% CI 95%-98%). In the prospective novice-operator FoCUS cohort (3.7% >moderate AS prevalence), 96.6% of studies were suitable for AI analysis, with sensitivity of 93% (95% CI, 82%-99%) and specificity of 96% (95% CI, 95-97%). Expert overread of AI-positive and uninterpretable cases (10% of cases) improved positive predictive value from 49.4%to 91.1%. Conclusions Deep learning enables accurate detection of at least moderate AS from both TTE and FoCUS. Robust performance on AI-guided FoCUS acquired by novice operators supports scalable, low-resource AS screening in settings with limited echocardiography access.
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Lee et al. (2026) conducted a cohort in Moderate or greater aortic stenosis (n=12,266). Deep learning algorithm for AS detection on TTE and AI-guided focused cardiac ultrasound (FoCUS) vs. Comprehensive transthoracic echocardiography (TTE) was evaluated on Detection of moderate or greater aortic stenosis (AUROC 0.99, 95% CI 0.98-1.00).
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