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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.

Introduction: Renal accessory arteries originate at the aorta and deliver renal blood flow independent of the main renal artery, while renal artery branches originate from and their flow depends on the main renal artery. However, the clinical significance of accessory arteries remains unclear. We tested the hypothesis that characteristics of renal accessory and/or branching arteries morphology are linked to resistant hypertension (RH). Methods: In a case-control study we compared the presence and characteristics of renal accessory and branching arteries (exposures) in individuals with RH (cases) vs. healthy kidney donors (normotensive controls) (Figure 1). Using MR angiography we calculated the length, diameter, tortuosity, and resistance (8*Length*blood viscosity)/(πr 4 )) of each artery (Figure 2). In separate logistic regression analyses we compared cases and controls for each of the following accessory or branching artery parameters: number of vessels, smallest diameter, length of longest vessel, greatest tortuosity, and greatest resistance. Results: We compared 89 unique RH cases to 248 unique normotensive controls (Table 1). For accessory arteries, their number was inversely related with RH (OR 0.38, 95% CI: 0.21 to 0.69, p<0.01), while length was mildly associated with RH (OR 1.06, 95% CI 1.00 to 1.13, p=0.04). Diameter and tortuosity were not associated in either direction. For branching arteries, RH was strongly associated with their diameter (OR 12.86, 95% CI 2.89 to 57.13, p<0.01), but more mildly and inversely with their resistance (OR 0.99, 95% CI: 0.97 to 1.00, p=0.01). The number and length of branching arteries were not associated in either direction. Conclusion: RH is strongly associated with a reduced number of accessory renal arteries and wider branching arteries, both being novel findings. Future longitudinal studies should determine if these changes are causally related to development of hypertension. Elucidation of this relationship may open new avenues for management of RH.