Kane, Conor J.

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.

Abstract Aims Heart failure is prevalent; however, there is no cost-effective screening option. Against formal echocardiography, we assessed the diagnostic performance of focused cardiac ultrasound (FoCUS), performed by novice users guided and interpreted by artificial intelligence (AI) for the assessment of left ventricular ejection fraction (LVEF). Methods and results We prospectively enrolled 496 adults referred for diagnostic echocardiography. Novice operators (without clinical or imaging experience) underwent a 4-h imaging workshop, then performed FoCUS using a point-of-care device (Philips Lumify) utilizing real-time AI-image guidance (UltraSight). Images were assessed by AI-image interpretation software (Mayo Clinic), and two expert blinded echocardiologists. Median age was 67, 39% female, and body mass index range 17–56 kg/m2. Forty-one subjects (8.3%) exhibited moderate or greater LV dilatation and 28 (5.6%) had LVEF <40%. The median scan time was 4 min (IQR 3–5). Adequate views were achieved in 95.0% and 97.4% of subjects for AI interpretation and expert analysis, respectively. A two-step diagnostic screening process for low EF in which only abnormal AI reads or uninterpretable scans were reviewed by experts (15.1%) had a sensitivity 96.2%, specificity 95.4%, PPV 54.3%, and NPV 99.8%. A subsequent prospective validation study of 344 subjects demonstrated similar results (sensitivity 100%, specificity 99.4%). Conclusion In this early feasibility investigation, AI-guidance technology embedded on a handheld device, enabled novice users without clinical or imaging experience to acquire sufficient quality images to accurately assess LVEF with minimal training. The technology evaluated in this study may hold promise for AI-guidance and interpretation to facilitate low-cost screening for cardiac dysfunction.

Abstract Aims Tricuspid valve regurgitation (TR) is common and associated with increased mortality. The interaction between the pulmonary artery (PA) circulation and the right ventricle (RV) and association with outcomes is incompletely understood. The effective PA elastance (PA Ea) is a composite measure of RV afterload that refers to the pulmonary vascular load that the RV must overcome to eject blood. Whether PA Ea discriminates mortality risk in unselected patients with significant TR is unknown. Methods and results In consecutive patients with ≥moderate TR, we compared all-cause mortality at 5 years based on the PA Ea (defined as the RV systolic pressure/stroke volume as measured by transthoracic echocardiography). In a total of 12 682 patients, the median PA Ea was 0.7 mm Hg/mL (interquartile range 0.5, 0.9). Increasing levels of PA Ea were associated with heart failure, liver and kidney disease, and clinical markers of risk such as the Tricuspid Regurgitation Impact on Outcomes score. Increasing levels of PA Ea were associated with larger RVs, worse RV systolic function, higher N-terminal pro-brain natriuretic peptide levels, and greater degrees of right heart failure. After adjusting for age and sex, PA Ea was associated with higher risk of death (hazard ratio 1.55 per 0.5 mm Hg/mL change of PA Ea; 95% confidence intervals 1.51–1.60; P < 0.0001). In multivariable modelling incorporating available clinical variables, PA Ea was independently predictive of outcome compared with other indexes of RV–PA coupling. Conclusion In patients with significant TR, higher PA Ea is progressively associated with RV dysfunction, right heart failure, and worse survival. Incorporating PA Ea into the routine echo assessment may help stratify risk.

Abstract Aims Tricuspid valve regurgitation (TR) is a common valvular disease associated with increased mortality. There is a need for tools to assess the interaction between the pulmonary artery (PA) circulation and the right ventricle in patients with TR and to investigate their association with outcomes. The pulmonary artery pulsatility index (PAPi) has emerged as a haemodynamic risk predictor in left heart disease and pulmonary hypertension (PH). Whether PAPi discriminates risk in unselected patients with greater than or equal to moderate TR is unknown. Methods and results In 5079 patients with greater than or equal to moderate TR (regardless of aetiology) and PA systolic and diastolic pressures measured on their first echocardiogram, we compared all-cause mortality at 5 years based on the presence or absence of PH and PAPi levels. A total of 2741 (54%) patients had PH. The median PAPi was 3.0 (IQR 1.9, 4.4). Both the presence of PH and decreasing levels of PAPi were associated with larger right ventricles, worse right ventricular systolic function, higher NT-pro BNP levels, greater degrees of right heart failure, and worse survival. In a subset of patients who had an echo and right heart catheterization within 24 h, the correlation of non-invasive to invasive PA pressures and PAPi levels was very good (r = 0.76). Conclusion In patients with greater than or equal to moderate TR with and without PH, lower PAPi is associated with right ventricular dysfunction, right heart failure, and worse survival. Incorporating PA pressure and PAPi may help stratify disease severity in patients with greater than or equal to moderate TR regardless of aetiology.