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In a large cohort of patients, ESPM is a safe procedure that improves clinical symptoms in the majority of patients during long-term follow-up. We show for the first time that this also applies for cases where there is no DNPP but a characteristic ECG documentation, and vice versa.

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This is the first collective analysis of a group of patients presenting with symptoms of pSVT and inducibility of only two AVNEBs. Procedural success and clinical long-term follow-up were in the range of the reported success rates of slow pathway modification of inducible AVNRT, independent of whether ECG documentation was present. Thus, SPM is a safe and effective therapy in patients with two AVNEBs.

Machine learning has immense novel but also disruptive potential for medicine. Numerous applications have already been suggested and evaluated concerning cardiovascular diseases. One important aspect is the detection and management of potentially thrombogenic arrhythmias such as atrial fibrillation. While atrial fibrillation is the most common arrhythmia with a lifetime risk of one in three persons and an increased risk of thromboembolic complications such as stroke, many atrial fibrillation episodes are asymptomatic and a first diagnosis is oftentimes only reached after an embolic event. Therefore, screening for atrial fibrillation represents an important part of clinical practice. Novel technologies such as machine learning have the potential to substantially improve patient care and clinical outcomes. Additionally, machine learning applications may aid cardiologists in the management of patients with already diagnosed atrial fibrillation, for example, by identifying patients at a high risk of recurrence after catheter ablation. We summarize the current state of evidence concerning machine learning and, in particular, artificial neural networks in the detection and management of atrial fibrillation and describe possible future areas of development as well as pitfalls. Graphical abstract Typical data flow in machine learning applications for atrial fibrillation detection.