Eunjung Lee, Jwan A Naser, Conor J Kane, Jude L Kovac, Christie Greason, Méabh M Killalea, Mayari A Gulati, John I Jackson, Jordan Borgeson, Daniel A Schonfeld, Jeffrey G Malins, D M Anisuzzaman, Maria M Crestanello, Jessica Zacher, Seda Camalan, Jeremy J Thaden, Vidhu Anand, Vuyisile T Nkomo, Ratnasari Padang, Timothy J Poterucha, Chieh-Ju Chao, Patricia A Pellikka, Francisco Lopez-Jimenez, Paul A Friedman, Jae K Oh, Garvan C Kane, Zachi I Attia, Sorin V Pislaru, Jared G Bird, Gal Tsaban
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.
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.