Background Aortic stenosis is still one of the major causes of sudden cardiac death in the elderly. Noninvasive screening for severe aortic valve stenosis (AS) may result in early cardiac diagnostic leading to an appropriate and timely medical intervention. Purpose The aims of this study were 1) to develop an artificial intelligence to detect severe AS based on heart sounds and 2) to build an application to screen patients using electronic stethoscope and smartphones, which will provide an efficient diagnostic workflow for screening as a complementary tool in daily clinical practice. Methods We enrolled 100 patients diagnosed with severe AS and 200 patients without severe AS (no echocardiographic sign of AS [n=100], mild AS [n=50], moderate AS [n=50]). The heart sounds were recorded in 4000 Hz waveform audio format at the following 3 sites of each patient; the 2nd intercostal right sternal border, the Erb's area and the apex. Each record was divided into multiple data of 4 seconds duration, which built 10800 sound records in total. We developed multiple convolutional neural networks (CNN) designed to recognize severe AS in heart sounds according to the recorded 3 sites. We adopted a stratified 4-fold cross-validation method by which the CNN was trained with 60% of the whole data, validated with 20% data and tested with the remaining 20% data not used during training and validation. As performance metrics we adopted the accuracy, F1 value and the area under the curve (AUC) calculated as the average of all cross-validation folds. For the smartphone application, we combined the best CNN-models from each recorded site for the best performance. Further 40 patients were newly enrolled for its clinical validation (no AS [n=10], mild AS [n=10], moderate AS [n=10], severe AS [n=10]). Results The accuracy, F1 value and AUC of each model were 88.9±5.7%, 0.888±0.006 and 0.953±0.008, respectively. The sensitivity and specificity were 87.9±2.2% and 89.9±2.4%. The recognition accuracy of moderate AS was significantly lower as compared to the other AS grades (moderate AS 74.1±6.1% vs no AS 98.0±1.4%, mild AS 97.6±1.2%, severe AS 87.9±2.2%, respectively, P<0.05). Our smartphone application showed a sensitivity of 100% (10/10), a specificity of 73.3% (22/30), and an accuracy of 80.0% (32/40), which implicated a good utility for screening. In the detailed analysis of 8 mistaken decisions, these were highly affected by the presence of severe mitral or tricuspid valve regurgitation despite of non-severe AS (7/8 [87.5%]). Conclusions This study demonstrated the promising possibility of an end-to-end screening for severe aortic valve stenosis using an electronic stethoscope and a smartphone application. This technology may improve the efficacy of daily medicine particularly where the human resource is limited or support a remote medical consultation. Further investigations are necessary to increase accuracy. Funding Acknowledgement Type of funding sources: None.

Funding Acknowledgements Type of funding sources: None. Background Despite repeated pulmonary vein isolation (re-PVI) due to recurrent atrial fibrillation (AF) after PVI has been a standard procedure, the detailed ablation strategy in case of re-PVI remains disputable. Objective The aim of this study was to assess the efficacy of re-PVI using wide antral circumferential ablation (WACA) supported by high density mapping (HDM) for recurrent AF after PVI as compared to simple repeated PVI supported by circular mapping catheter. Methods Consecutive patients with recurrent AF after PVI were prospectively enrolled in this study, who underwent left atrial HDM and subsequently WACA antral (re-)isolation ("Re-WACA" group). The historical controls with re-PVI between 2016 and 2018 using circular mapping catheter, but without HDM were also enrolled ("control group"). These control patients underwent re-PVI with gap ablation at the pulmonary vein ostium. Primary endpoint was defined as any recurrence and ECG documentation of atrial tachyarrhythmias (AT) including AF or atrial tachycardias over 30 seconds. The patients were routinely followed up for 1 year with at least twice annual holter-monitoring. Results In total, 116 patients were enrolled in this study (Re-WACA group [N=56, 68±10 years], control group [N=58, 65±10 years]). There were no significant differences in clinical characteristics including the number of previous left atrial ablation procedures between two groups. In all 56 patients with Re-WACA, residual PV antral potentials were demonstrated (100%), whereas 7 patients (13%) showed no electrical potentials inside any PVs. During a mean follow-up period of 402±71 days, 6 out of 56 Re-WACA patients (11%) and 18 out of 58 controls (31%) experienced AT recurrences. Kaplan-Meier analysis demonstrated that the patients who underwent Re-WACA showed significantly lower AT recurrence after the index Re-PVI procedure as compared to the controls (log-rank, P = 0.010). Multivariate Cox regression showed that Re-WACA was an independent predictor of freedom from AT recurrence (hazard ratio = 0.39; 95% confidence-interval 0.16-0.93; P=0.034). The number of previous PVI procedures predicted AT recurrence during follow-up (hazard ratio = 2.35; 95% confidence-interval 1.20-4.46; P=0.010). Conclusions Residual pulmonary vein antral potential in patients with recurrent AF after previously performed PVI is a frequent finding. These antral potentials can be easily visualized by HDM. Repeated isolation of wide PV antrum (Re-WACA) is an effective strategy to reduce further AF recurrence as compared to conventional re-PVI without left atrial HDM.