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BACKGROUND: Pulsed field ablation is now widely used for atrial fibrillation (AF) ablation, but atrial tachyarrhythmia recurrence remains common and postablation risk tools are limited. We assessed whether a validated sinus‑rhythm artificial intelligence— enabled ECG AF probability score, and its early postablation change, predicts recurrence after pulsed field ablation. METHODS: We studied consecutive patients undergoing index pulsed field ablation across 3 Mayo Clinic sites (February 2024–March 2025) with ≥1 sinus‑rhythm ECG within 180 days preprocedure. Baseline artificial intelligence—enabled ECG AF score (range 0–1) was the mean across baseline ECGs. Recovery indices (RI30/60/90), defined as mean postprocedure artificial intelligence—enabled ECG score within days 1–30/60/90 minus baseline, predicted post‑blanking recurrence using corresponding 30‑, 60‑, and 90‑day blanking windows. The primary outcome was time to first documented AF/atrial flutter/atrial tachycardia episode >30 seconds, ascertained via clinic ECGs, ambulatory monitoring, and device interrogations. Cox regression adjusted for prespecified clinical and echocardiographic covariates assessed associations; Harrell C‑statistic quantified discrimination. RESULTS: Among 1052 patients (67±10 years; 33% women), median baseline score was 0.35; 320 (30%) recurred over 188 days. Higher baseline score independently predicted recurrence (hazard ratio per 0.1 increase, 1.08 [95% CI, 1.03–1.14]). Scores rose on day 0 after ablation, then declined over subsequent weeks, with persistently higher, less‑improving trajectories among patients who recurred (time×recurrence interaction P <0.001). Day 0 score was not independently associated with recurrence after adjustment. Attenuated early recovery (higher recovery index) was independently associated with recurrence, most robustly for RI-90 (hazard ratio per 0.1 increase, 1.15 [95% CI, 1.07–1.24]), and improved discrimination beyond baseline and covariates (C‑statistic, 0.63–0.67; ΔC=0.04). Findings were consistent in a historical thermal ablation cohort. CONCLUSIONS: Baseline and early postablation dynamics of a sinus‑rhythm artificial intelligence—enabled ECG AF probability score are associated with pulsed field ablation recurrence and may aid multivariable risk stratification; prospective validation is warranted.

Background Bicuspid aortic valve (BAV) is the most common congenital heart defect, associated with a lifetime morbidity burden exceeding 80% and an estimated 7% probability of transmission to first-degree relatives. Hence, echocardiographic screening of relatives is recommended. The aim of our study is to develop an artificial intelligence-enabled electrocardiogram (AI-ECG) model for the detection of BAV. Methods Between January 1, 1990, and June 30, 2023, the Mayo Clinic healthcare system database was searched for patients aged ≥18 years with echocardiograms performed for family history of BAV, abnormal auscultation, a BAV-related diagnosis, or to rule out the condition, and with an electrocardiogram (ECG) obtained within 6 months. Cases were patients with a confirmed BAV diagnosis, and controls were those with normal aortic valves. ECGs were randomly assigned into training (80%), internal validation (10%), and test (10%) groups. The AI-ECG model was built using Keras under TensorFlow. Results A total of 13,065 valid ECG–echocardiogram pairs were included (mean [SD] age, 58 [17] years; 44% women), with a median time of 1 day (interquartile range, 0–3 days) between studies. With a 45% BAV prevalence in the test group ( n = 1301), the area under the curve was 0.704, positive predictive value (PPV) 60.1%, and negative predictive value (NPV) 69.4%. At a prevalence of 7%, the NPV increased to 96.1%, but the PPV decreased to 12.2%. Conclusions In this retrospective cohort and using the current AI-ECG model, BAV detection showed modest diagnostic performance and limited standalone clinical utility, suggesting that ECG-only screening for BAV may require additional modalities or more refined phenotyping.

Background Diastolic dysfunction is common in patients with aortic stenosis and may influence outcomes following surgical aortic valve replacement. We aimed to examine the association of preoperative artificial intelligence (AI)‐generated diastolic function grades with early and late outcomes following aortic valve replacement and how postoperative progression influence prognosis. Methods We identified 5503 patients undergoing aortic valve replacement between 2000 and 2023. Diastolic function was assessed using a validated deep‐learning AI model applied to 12‐lead ECGs done preoperatively and on postoperative follow‐up. Diastolic grades were classified by AI into Grades 1 to 3. Longitudinal trend analyses and multivariable regression models were used to assess study end points. Results Among 5503 patients (mean age 72.4±10.8 years; 39% female), higher AI ECG diastolic grades were associated with greater comorbidity burden, including diabetes, renal disease, and heart failure. AI ECG diastolic Grade 3 was independently associated with higher in‐hospital mortality (odds ratio, 2.5; P =0.007) and other complications. At 5‐year follow‐up, patients with Grade 3 showed the least improvement in diastolic function by both ECG and echocardiography. Grades 2 and 3 diastolic function at baseline were independently associated with increased late mortality (hazard ratio, 1.3 and 2.45, respectively; both P <0.001). Additionally, lack of improvement in AI ECG diastolic grade by 1 year was also independently associated with late mortality. Conclusions AI ECG–derived diastolic function grades strongly correlates with early complications and long‐term mortality and diastolic progression after aortic valve replacement. AI ECG provides a powerful, noninvasive tool for risk stratification and longitudinal monitoring of patients with aortic stenosis.

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 Introduction Atrial fibrillation (AF) is a major contributor to stroke risk, yet many cases remain undiagnosed due to its silent and paroxysmal nature. To address this, multiple risk scores have been developed to predict incident AF. This study aimed to evaluate the predictive accuracy of five widely used clinical AF prediction tools (i.e., CHARGE-AF, CHA2DS2-VASc, mC2HEST, HATCH, and HAVOC) among patients undergoing long-term continuous monitoring with implantable loop recorders (ILRs). Methods We conducted a retrospective cohort study across three major US academic medical centers, including patients with ILRs and no prior diagnosis or ECG evidence of AF. Clinical variables used to calculate each risk score were extracted from electronic health records. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC). Kaplan-Meier survival analysis and Cox proportional hazards models were used to evaluate the association between each score’s risk categories and incident AF (defined as any episode lasting ≥6 minutes). Calibration was assessed using calibration plots and Brier scores. Results A total of 3,895 patients were included (mean age 61 ± 17 yrs.; 51% male). Incident AF was detected in 879 patients (22%) over a median follow up of 17 months (IQR 8-35). Table 1 presents baseline characteristics and device detected AF event data during follow up. CHARGE-AF had the highest AUC (0.68) but also the highest Brier score (Fig. 1). Failure curves showed clear risk stratification across all tools (Fig. 2). Calibration plots (Fig. 3) demonstrated modest agreement between predicted and observed risk across all models. Using CHARGE-AF as the reference, pairwise comparisons of Brier scores via the Wilcoxon signed-rank test showed lower calibration accuracy than CHA2DS2-VASc (p = 0.03), mC2HEST (p = 0.001), and HAVOC (p = 0.001), with no significant difference compared to HATCH (p = 0.15). Conclusions In patients undergoing continuous rhythm monitoring with ILRs, commonly used AF prediction scores demonstrated modest discriminatory ability and similar calibration performance. These findings suggest that existing risk models, which were largely developed in cohorts with clinically diagnosed AF, may not optimally translate to continuously monitored populations. Future work is needed to develop and validate AF prediction models specifically in cohorts undergoing long-term continuous cardiac monitoring.Figure 1Figure 2-3

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 &lt;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 Rheumatic heart disease (RHD) remains the most prevalent acquired cardiovascular disease among young adults in developing countries, and there is a critical need for an efficient, affordable, and available screening strategy to detect RHD in these resource-limited areas. The 12-lead electrocardiogram (ECG) is a ubiquitous, inexpensive diagnostic tool, and application of artificial intelligence to the ECG (AI-ECG) has allowed for screening of various cardiovascular pathologies. Methods and results Patients aged &gt;18 years with severe RHD with at least one transthoracic echocardiogram and one standard 12-lead ECG performed at Groote Schuur Hospital, Cape Town, within 180 days of each other were eligible. A total of 920 RHD patients were identified, with 2199 corresponding controls from the same medical centre. Patients were allocated to derivation (80%), validation (10%), and testing (10%) groups, respectively. The AI-ECG to detect RHD from a standard 12-lead ECG was built using the Keras framework under Tensorflow backend. In the test group, the AI-ECG for RHD exhibited strong algorithm performance with an AUC of 0.890 (95% Confidence Interval 0.848–0.940) and an accuracy of 83.8%. The algorithm remained robust between RHD presentation: mitral valve alone (AUC 0.913) and mixed mitral and aortic valve disease (AUC 0.858). There were minor performance differences in the subgroup analysis of patient sex, age, and ECG characteristics. Conclusion In this study, an AI-ECG algorithm was successfully developed to screen for advanced RHD from a standard 12-lead ECG, and the AI-ECG performance remained acceptable across most subgroups, including RHD manifestation, patient sex, age &lt;70 years, and ECG characteristics.

Abstract Aims Early detection of structural heart disease (SHD) improves patient outcomes. However, population-based screening is not recommended due to the lack of accurate and cost-effective tools. We evaluated the costs of artificial intelligence–enabled electrocardiogram (AI-ECG) alone vs. AI-ECG followed by handheld cardiac ultrasound (HCU) for SHD screening. Methods and results We performed a model-based cost analysis using data from 286 adult patients who underwent ECG and same-day HCU performed by a novice operator. Transthoracic echocardiogram (TTE) was the reference standard. We compared two screening strategies: (i) AI-ECG alone and (ii) a stepwise approach (AI-ECG followed by HCU). We assessed costs per diagnosis of aortic stenosis (AS), increased left ventricular wall thickness (ILVWT), and left ventricular systolic dysfunction (LVSD). Sensitivity analyses were conducted for varying disease prevalence. The stepwise approach decreased the cost per diagnosis of AS from $6386 (AI-ECG alone) to $2746 (57.0% savings), ILVWT from $4448 to $2895 (34.9% savings), and LVSD from $1469 to $1296 (11.8% savings). Overall, the cost per diagnosis for all SHDs combined decreased from $1940 to $1570 (19.1% savings). Sensitivity analysis demonstrated that cost savings were inversely proportional to disease prevalence. Nevertheless, stepwise screening remained cost-saving compared with AI-ECG alone until prevalence exceeded ∼55.9% for AS, 28.9% for ILVWT, 20.7% for LVSD, and 40.8% for all SHDs combined. Conclusion A stepwise screening strategy incorporating HCU after a positive AI-ECG reduces the immediate costs of SHD detection by minimizing unnecessary TTEs. This approach may enhance the feasibility of population-based SHD screening, particularly in lower-prevalence settings.