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Recent studies highlight artificial intelligence’s ability to identify ventricular dysfunction via electrocardiograms (ECGs); however, specific indicative waveforms remain unclear. This study analysed ECG and echocardiography data from 17,422 cases in Japan and Germany. We developed 10-layer convolutional neural networks to detect left ventricular ejection fractions below 50%, using four-fold cross-validation. Model performance, evaluated among different ECG configurations (3 s strips, single-beat, and two-beat overlay) and segments (PQRST, QRST, P, QRS, and PQRS), showed two-beat ECGs performed best, followed by single-beat models, surpassing 3 s models in both internal and external validations. Single-beat models revealed limb leads, particularly I and aVR, as most indicative of dysfunction. An analysis indicated segments from QRS to T-wave were most revealing, with P segments enhancing model performance. This study confirmed that dual-beat ECGs enabled the most precise ventricular function classification, and segments from the P- to T-wave in ECGs were more effective for assessing ventricular dysfunction, with leads I and aVR offering higher diagnostic utility.

Simple sensor-based procedures, including auscultation and electrocardiography (ECG), can facilitate early diagnosis of valvular diseases, resulting in timely treatment. This study assessed the impact of combining these sensor-based procedures with machine learning on diagnosing valvular abnormalities and ventricular dysfunction. Data from auscultation at three distinct locations and 12-lead ECGs were collected from 1052 patients undergoing echocardiography. An independent cohort of 103 patients was used for clinical validation. These patients were screened for severe aortic stenosis (AS), severe mitral regurgitation (MR), and left ventricular dysfunction (LVD) with ejection fractions ≤ 40%. Optimal neural networks were identified by a fourfold cross-validation training process using heart sounds and various ECG leads, and their outputs were combined using a stacking technique. This composite sensor model had high diagnostic efficiency (area under the receiver operating characteristic curve (AUC) values: AS, 0.93; MR, 0.80; LVD, 0.75). Notably, the contribution of individual sensors to disease detection was found to be disease-specific, underscoring the synergistic potential of the sensor fusion approach. Thus, machine learning models that integrate auscultation and ECG can efficiently detect conditions typically diagnosed via imaging. Moreover, this study highlights the potential of multimodal artificial intelligence applications.

Abstract Background The prognostic implications of non-sustained ventricular tachycardia (NSVT) and their significance as therapeutic targets in patients without prior sustained ventricular arrhythmias remain undetermined. The aim of this study was to investigate the prognostic significance of asymptomatic NSVT in patients who had primary prevention implantable cardioverter-defibrillator (ICD) implantation due to ischemic or non-ischemic cardiomyopathy (ICM, NICM). Methods We enrolled 157 consecutive primary prevention ICD patients without previous appropriate ICD therapy (AIT). Patients were allocated to two groups depending on the presence or absence of NSVT in a 6-month period prior to enrollment. The incidence of AIT and unplanned hospitalization due to decompensated heart failure (HF) were assessed during follow-up. Results In 51 patients (32%), precedent NSVT was documented. During a median follow-up of 1011 days, AIT occurred in 36 patients (23%) and unplanned HF hospitalization was observed in 32 patients (20%). In precedent NSVT patients, the incidence of AIT and unplanned HF hospitalization was significantly higher as compared to patients without precedent NSVT (AIT: 29/51 [57%] vs. 7/106 [7%], P