Valvular & structural heart diseaseDiagnostic or prognostic study

Artificial Intelligence–Enabled Acquisition and Interpretation for Screening Aortic Stenosis

JAMA CardiolPublished

  • Diagnostic accuracy study
  • retrospective development and validation
  • prospective evaluation
  • multicentre

Authors' conclusion

A deep learning algorithm accurately detected moderate or greater AS across validation cohorts in this study. In prospective evaluation, novice operators were able to acquire AI-guided FoCUS examinations that enabled accurate detection of moderate or greater AS.

Study context

Population
Patients at Mayo Clinic sites in the Midwest, Arizona and Florida assessed for moderate or greater aortic stenosis (AS)6,753 for model development
Participants
844 / 1,912 / 602 / 1,302
Comparison
Internal test vs Validation cohorts vs Experienced sonographers vs Novice operators
  • Internal testn = 844
  • Validation cohortsn = 1,912
  • Experienced sonographersn = 602
  • Novice operatorsn = 1,302
Outcome
Detection of moderate or greater AS (AUROC, sensitivity, specificity, PPV)

Performance

As reported in the abstract

Detection of moderate or greater AS
Test or modelAUROCAUROC, ArizonaAUROC, FloridaSensitivitySpecificity
Internal test0.99 (95% CI, 0.98-1.00)
Validation cohorts0.99 (95% CI, 0.97-1.00)0.99 (95% CI, 0.96-1.00)
Experienced sonographers95% (95% CI, 82-99)97% (95% CI, 95-98)
Prospective novice-operator cohort: 1258 of 1302 examinations suitable for automated analysis
96.6%
Prospective novice-operator cohort: sensitivity
93% (95% CI, 82-99)
Prospective novice-operator cohort: specificity
96% (95% CI, 95-97)

Further findings

Expert review of artificial intelligence (AI)-positive and uninterpretable examinations (novice-operator cohort)

Sensitivity with expert review
85.4%
PPV increased from 49.4% to 91.1%

Original publication

Artificial Intelligence–Enabled Acquisition and Interpretation for Screening Aortic Stenosis. JAMA Cardiol. Published 28 August 2026. doi:10.1001/jamacardio.2026.3829

Summarised from the published abstract; every number is checked against it