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1. ConvexECG: Lightweight and Explainable Neural Networks for Personalized, Continuous Cardiac Monitoring

7. List of contributors

14. Predicting acute termination and non-termination during ablation of human atrial fibrillation using quantitative indices

20. Electrical Substrate Ablation for Refractory Ventricular Fibrillation: Results of the AVATAR Study.

22. Populations of in silico myocytes and tissues reveal synergy of multiatrial‐predominant K+‐current block in atrial fibrillation

23. Termination of persistent atrial fibrillation by ablating sites that control large atrial areas.

28. Abstract 17934: Performance of an Electrocardiographic Deep Learning Model for Detecting Wall Motion Abnormalities: An Analysis of Impact From Cardiovascular Comorbidities and Race in an External Validation Study

31. Abstract 17420: Enhanced Identification of Cardiac Wall Motion Abnormalities: An Externally Validated Deep Neural Network Approach Outperforms Expert and Quantitative Analysis of Electrocardiograms

34. Opportunities and challenges in heart rhythm research: Rationale and development of an electrophysiology collaboratory

35. Successful cardiac resynchronization therapy reduces negative septal work in patient-specific models of dyssynchronous heart failure.

36. Intra-cardiac Signatures of Atrial Arrhythmias Identified by Machine Learning and Traditional Features

37. Successful Cardiac Resynchronization Therapy Reduces Negative Septal Work in Patient-Specific Models of Dyssynchronous Heart Failure

38. Visions for digital integrated cardiovascular care: HRS Digital Health Committee perspectives

50. Abstract 12117: Machine Learning to Probe Variability in Patient Outcomes After Atrial Fibrillation Ablation - The Stanford AF Registry (start)

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