Why the study?
Screening for aortic stenosis remains limited by dependence on comprehensive echocardiography and trained imaging personnel, despite timely identification being essential for appropriate clinical management.
Does an AI-guided focused cardiac ultrasound acquired by novice operators accurately detect moderate or greater aortic stenosis?
Observational (n=12,265)
Yes
Does an AI-guided focused cardiac ultrasound acquired by novice operators accurately detect moderate or greater aortic stenosis?
Effect estimate: AUROC 0.99 (95% CI 0.98-1.00)
An AI-guided focused cardiac ultrasound acquired by novice operators demonstrated high sensitivity and specificity for detecting moderate or greater aortic stenosis, offering a scalable screening strategy.
Captured external expert commentary on this paper, strongest first. Original sources are linked where available.
“A comprehensive echocardiogram is the standard diagnostic test for aortic stenosis. However, its use is resource-intensive, requiring specialized equipment as well as trained personnel with expertise in both image acquisition and interpretation. These requirements can limit access, particularly in resource-constrained settings, and make comprehensive echocardiography neither feasible nor cost-effective as a broad screening tool.”
“#AI-guided acquisition and interpretation of #Echocardiography could support earlier detection of #AorticStenosis beyond symptom-based case finding and expand access to screening in settings with limited expert imaging resources. The editorial indicates that AI may help nonexpert operators obtain interpretable studies and help triage patients who need comprehensive transthoracic echocardiography, surveillance, or intervention.”
“We wanted to see whether combining AI guidance with focused cardiac ultrasound could help people with no previous ultrasound experience capture usable heart images and identify patients who may need further evaluation.”
May support novice-operator AS screening; hypothesis-generating pending randomized validation.
Importance: Timely identification of aortic stenosis (AS) is essential for appropriate clinical management, yet screening remains limited by dependence on comprehensive echocardiography and trained imaging personnel. Objective: To develop and validate a deep learning algorithm for detection of moderate or greater AS and prospectively evaluate its performance using artificial intelligence (AI)-guided focused cardiac ultrasound (FoCUS) acquired by novice operators. Design, Setting, and Participants: This diagnostic study included retrospective algorithm development and validation and prospective evaluation of AI-guided FoCUS across Mayo Clinic sites in the Midwest, Arizona, and Florida. The model was developed using 6753 patients and evaluated in internal validation (n = 852), internal test (n = 844), and validation (n = 1912) cohorts. Performance was assessed on FoCUS acquired by experienced sonographers (n = 602) and prospectively by novice operators (n = 1302). The retrospective model development and validation cohorts comprised studies performed from January 2005 through September 2022. Prospective study was conducted in 2 enrollment periods from June to August 2024 and from June to September 2025. Participants from both periods were combined to comprise the final prospective cohort. Exposure: AI-guided FoCUS acquisition and automated deep learning-based assessment for detection of moderate or greater AS. Main Outcomes and Measures: Detection of moderate or greater AS. Performance was assessed using area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and positive predictive value. Results: The model demonstrated excellent discrimination in the internal test cohort (AUROC, 0.99; 95% CI, 0.98-1.00) and geographically distinct validation cohorts in Arizona (AUROC, 0.99; 95% CI, 0.97-1.00) and Florida (AUROC, 0.99; 95% CI, 0.96-1.00). Among FoCUS examinations acquired by experienced sonographers, sensitivity was 95% (95% CI, 82-99) and specificity was 97% (95% CI, 95-98). In the prospective novice-operator cohort, 1258 of 1302 examinations (96.6%) were suitable for automated analysis. Sensitivity was 93% (95% CI, 82-99) and specificity was 96% (95% CI, 95-97). Expert review of AI-positive and uninterpretable examinations increased the positive predictive value from 49.4% to 91.1%, with sensitivity of 85.4%. Conclusions and Relevance: 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. These findings support a scalable strategy that may expand access to AS detection in settings with limited echocardiography resources.
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Lee et al. (2026) conducted an observational in Aortic stenosis (n=12,265). AI-guided focused cardiac ultrasound (FoCUS) was evaluated on Detection of moderate or greater AS (AUROC 0.99, 95% CI 0.98-1.00).
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