Artificial Intelligence Advancements in Cardiomyopathies: Implications for Diagnosis and Management of Arrhythmogenic Cardiomyopathy.

Purpose Of Review: This review aims to explore the emerging potential of artificial intelligence (AI) in refining risk prediction, clinical diagnosis, and treatment stratification for cardiomyopathies, with a specific emphasis on arrhythmogenic cardiomyopathy (ACM).

Recent Findings: Recent developments highlight the capacity of AI to construct sophisticated models that accurately distinguish affected from non-affected cardiomyopathy patients. These AI-driven approaches not only offer precision in risk prediction and diagnostics but also enable early identification of individuals at high risk of developing cardiomyopathy, even before symptoms occur.

On the design and development of a handheld electrocardiogram device in a clinical setting.

Cardiovascular diseases (CVDs) are a global burden that requires attention. For the detection and diagnosis of CVDs, the 12-lead ECG is a key tool. With technological advancements, ECG devices are becoming smaller and available for home use.

Detection of Ischemic ST-Segment Changes Using a Novel Handheld ECG Device in a Porcine Model.

Background: Portable, smartphone-sized electrocardiography (ECG) has the potential to reduce time to treatment for patients suffering acute cardiac ischemia, thereby lowering the morbidity and mortality. In the UMC Utrecht, a portable, smartphone-sized, multi-lead precordial ECG recording device (miniECG 1.0, UMC Utrecht) was developed.

Detection of acute coronary occlusion with a novel mobile electrocardiogram device: a pilot study.

Aims: Many portable electrocardiogram (ECG) devices have been developed to monitor patients at home, but the majority of these devices are single lead and only intended for rhythm disorders. We developed the miniECG, a smartphone-sized portable device with four dry electrodes capable of recording a high-quality multi-lead ECG by placing the device on the chest. The aim of our study was to investigate the ability of the miniECG to detect occlusive myocardial infarction (OMI) in patients with chest pain.

ECG-only explainable deep learning algorithm predicts the risk for malignant ventricular arrhythmia in phospholamban cardiomyopathy.

Background: Phospholamban (PLN) p.(Arg14del) variant carriers are at risk for development of malignant ventricular arrhythmia (MVA). Accurate risk stratification allows timely implantation of intracardiac defibrillators and is currently performed with a multimodality prediction model.

ANOCA patients with and without coronary vasomotor dysfunction present with limited electrocardiographic remodeling.

Background: Coronary vasomotor dysfunction (CVDys) comprises coronary vasospasm (CVS) and/or coronary microvascular dysfunction (CMD) and is highly prevalent in patients with angina and non-obstructive coronary artery disease (ANOCA). Invasive coronary function testing (CFT) to diagnose CVDys is becoming more common, enabling pathophysiologic research of CVDys. This study aims to explore the electrophysiological characteristics of ANOCA patients with CVDys.

Automatic triage of twelve-lead electrocardiograms using deep convolutional neural networks: a first implementation study.

Aims: Expert knowledge to correctly interpret electrocardiograms (ECGs) is not always readily available. An artificial intelligence (AI)-based triage algorithm (DELTAnet), able to support physicians in ECG prioritization, could help reduce current logistic burden of overreading ECGs and improve time to treatment for acute and life-threatening disorders. However, the effect of clinical implementation of such AI algorithms is rarely investigated.

Deep neural network-based clustering of deformation curves reveals novel disease features in PLN pathogenic variant carriers.

Echocardiographic deformation curves provide detailed information on myocardial function. Deep neural networks (DNNs) may enable automated detection of disease features in deformation curves, and improve the clinical assessment of these curves. We aimed to investigate whether an explainable DNN-based pipeline can be used to detect and visualize disease features in echocardiographic deformation curves of phospholamban (PLN) p.

Electrocardiogram Devices for Home Use: Technological and Clinical Scoping Review.

Background: Electrocardiograms (ECGs) are used by physicians to record, monitor, and diagnose the electrical activity of the heart. Recent technological advances have allowed ECG devices to move out of the clinic and into the home environment. There is a great variety of mobile ECG devices with the capabilities to be used in home environments.

Machine-learning-derived heart and brain age are independently associated with cognition.

Background And Purpose: A heart age biomarker has been developed using deep neural networks applied to electrocardiograms. Whether this biomarker is associated with cognitive function was investigated.

Methods: Using 12-lead electrocardiograms, heart age was estimated for a population-based sample (N = 7779, age 40-85 years, 45.

Deep neural networks reveal novel sex-specific electrocardiographic features relevant for mortality risk.

Aims: Incorporation of sex in study design can lead to discoveries in medical research. Deep neural networks (DNNs) accurately predict sex based on the electrocardiogram (ECG) and we hypothesized that misclassification of sex is an important predictor for mortality. Therefore, we first developed and validated a DNN that classified sex based on the ECG and investigated the outcome.

Improving explainability of deep neural network-based electrocardiogram interpretation using variational auto-encoders.

Aims: Deep neural networks (DNNs) perform excellently in interpreting electrocardiograms (ECGs), both for conventional ECG interpretation and for novel applications such as detection of reduced ejection fraction (EF). Despite these promising developments, implementation is hampered by the lack of trustworthy techniques to explain the algorithms to clinicians. Especially, currently employed heatmap-based methods have shown to be inaccurate.

Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy.

Aims: This study aims to identify and visualize electrocardiogram (ECG) features using an explainable deep learning-based algorithm to predict cardiac resynchronization therapy (CRT) outcome. Its performance is compared with current guideline ECG criteria and QRSAREA.

Methods And Results: A deep learning algorithm, trained on 1.

Multielectrode Contact Measurement Can Improve Long-Term Outcome of Pulmonary Vein Isolation Using Circular Single-Pulse Electroporation Ablation.

Background: Irreversible electroporation (IRE) ablation is generally performed with multielectrode catheters. Electrode-tissue contact is an important predictor for the success of pulmonary vein (PV) isolation; however, contact force is difficult to measure with multielectrode ablation catheters. In a preclinical study, we assessed the feasibility of a multielectrode impedance system (MEIS) as a predictor of long-term success of PV isolation.

Life-threatening ventricular arrhythmia prediction in patients with dilated cardiomyopathy using explainable electrocardiogram-based deep neural networks.

Aims: While electrocardiogram (ECG) characteristics have been associated with life-threatening ventricular arrhythmias (LTVA) in dilated cardiomyopathy (DCM), they typically rely on human-derived parameters. Deep neural networks (DNNs) can discover complex ECG patterns, but the interpretation is hampered by their 'black-box' characteristics. We aimed to detect DCM patients at risk of LTVA using an inherently explainable DNN.

Feasibility of Linear Irreversible Electroporation Ablation in the Coronary Sinus.

Introduction: Previous studies demonstrated that the coronary sinus (CS) is an important target for ablation in persistent atrial fibrillation. However, radiofrequency ablation in the CS is associated with coronary vessel damage and tamponade. Animal data suggest irreversible electroporation (IRE) ablation can be a safe ablation modality in vicinity of coronary arteries.

Safety and feasibility study of non-invasive robot-assisted high-intensity focused ultrasound therapy for the treatment of atherosclerotic plaques in the femoral artery: protocol for a pilot study.

Introduction: Peripheral arterial disease (PAD) is an atherosclerotic disease leading to stenosis and/or occlusion of the arterial circulation of the lower extremities. The currently available revascularisation methods have an acceptable initial success rate, but the long-term patency is limited, while surgical revascularisation is associated with a relatively high perioperative risk. This urges the need for development of less invasive and more effective treatment modalities.

Artificial Intelligence to Improve Risk Prediction with Nuclear Cardiac Studies.

As machine learning-based artificial intelligence (AI) continues to revolutionize the way in which we analyze data, the field of nuclear cardiology provides fertile ground for the implementation of these complex analytics. This review summarizes and discusses the principles regarding nuclear cardiology techniques and AI, and the current evidence regarding its performance and contribution to the improvement of risk prediction in cardiovascular disease. There is a growing body of evidence on the experimentation with and implementation of machine learning-based AI on nuclear cardiology studies both concerning SPECT and PET technology for the improvement of risk-of-disease (classification of disease) and risk-of-events (prediction of adverse events) estimations.

Characteristics and time course of acute and chronic myocardial lesion formation after electroporation ablation in the porcine model.

Introduction: Electroporation ablation creates deep and wide myocardial lesions. No data are available on time course and characteristics of acute lesion formation.

Methods: For the acute phase of myocardial lesion development, seven pigs were investigated.

Uncertainty estimation for deep learning-based automated analysis of 12-lead electrocardiograms.

Aims: Automated interpretation of electrocardiograms (ECGs) using deep neural networks (DNNs) has gained much attention recently. While the initial results have been encouraging, limited attention has been paid to whether such results can be trusted, which is paramount for their clinical implementation. This study aims to systematically investigate uncertainty estimation techniques for automated classification of ECGs using DNNs and to gain insight into its utility through a clinical simulation.

Discovering and Visualizing Disease-Specific Electrocardiogram Features Using Deep Learning: Proof-of-Concept in Phospholamban Gene Mutation Carriers.

Background: ECG interpretation requires expertise and is mostly based on physician recognition of specific patterns, which may be challenging in rare cardiac diseases. Deep neural networks (DNNs) can discover complex features in ECGs and may facilitate the detection of novel features which possibly play a pathophysiological role in relatively unknown diseases. Using a cohort of PLN (phospholamban) p.

Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology.

The combination of big data and artificial intelligence (AI) is having an increasing impact on the field of electrophysiology. Algorithms are created to improve the automated diagnosis of clinical ECGs or ambulatory rhythm devices. Furthermore, the use of AI during invasive electrophysiological studies or combining several diagnostic modalities into AI algorithms to aid diagnostics are being investigated.

Efficacy of multi-electrode linear irreversible electroporation.

Aims: We investigated the efficacy of linear multi-electrode irreversible electroporation (IRE) ablation in a porcine model.

Methods And Results: The study was performed in six pigs (weight 60-75 kg). After median sternotomy and opening of the pericardium, a pericardial cradle was formed and filled with blood.