Rutger van de Leur

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Verified

Rutger verified their affiliation via an institutional email.

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• MD PhD
• AI in Cardiology at University Medical Center Utrecht

Objective and rationale Small studies have shown that the QT interval follows a circadian rhythm. This finding has never been confirmed in a large real-world hospital population and the clinical meaning of disrupted rhythmicity remains unknown. Methods In this cohort study, all consecutive adult patients with at least one 12-lead ECG acquired betw...

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. Most of these devices contain a limited number of leads and are aimed to detect atrial fibrillation (AF). To...

Background Many portable 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....

Background Conventional approaches to analysing electrocardiograms (ECG) in fragmented parameters (such as the PR interval) ignored the high dimensionality of data which might result in omission of subtle information content relevant the cardiac biology. Deep representation learning of ECG may reveal novel insights. Methods We finetuned an unsuperv...

Background and 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-thr...

Auto-encoders and their variational counterparts form a family of (deep) neural networks that serve a wide range of applications in medical research and clinical practice. In this chapter we provide a comprehensive overview of how auto-encoders work and how they can be used to improve medical research. We elaborate on various topics such as dimensi...

Aims Patients with non-ischemic dilated cardiomyopathy (DCM) are at considerable risk for end-stage heart failure (HF), requiring close monitoring to identify early signs of disease. We aimed to develop a model to predict the 5-years risk of end-stage HF, allowing for tailored patient monitoring and management. Methods and results Derivation data w...

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 dis...

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. Objectives...

Introduction: A heart age biomarker has been developed using deep neural networks applied to electrocardiograms. We investigated whether this biomarker is associated with cognitive function. Methods: Using 12-lead electrocardiogram, heart age was estimated for a population-based sample (N = 7779, age 40-85 years, 45.3% men). Associations between...

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): UiT The Arctic University of Norway, Northern Norway Regional Health Authority. Background/Introduction Machine learning models have been applied to magnetic resonance images of the brain to estimate brain age gap as a biomarker of bio...

Deep learning is a subfield of artificial intelligence (AI) that is concerned with developing large and complex neural networks for various tasks. As of today, there exists a wide variety of DL models yielding promising results in many subfields of AI, such as computer vision (CV) and natural language processing (NLP). In this chapter, we provide a...

Introduction Phospholamban (PLN) p.Arg14del mutation carriers are at risk of developing arrhythmogenic cardiomyopathy, but phenotypic penetrance is incomplete and much interpatient variability exists in the risk of malignant ventricular arrhythmias (MVA). Accurate risk stratification allows for timely device implantation which may prevent sudden ca...

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 1.1 million ECGs fr...

BACKGROUND Electrocardiograms (ECG) 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 to the home environment. OBJECTIVE This scoping review aims to provide a comprehensive overview of the current landscape of mobile ECG devi...

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. Obje...

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 c...

Background Interatrial block (IAB) has been associated with supraventricular arrhythmias and stroke, and even with sudden cardiac death in the general population. Whether IAB is associated with life‐threatening arrhythmias (LTA) and sudden cardiac death in dilated cardiomyopathy (DCM) remains unknown. This study aimed to determine the association b...

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 ai...

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Netherlands Organisation for Health Research and Development (ZonMw) Background Electrocardiogram-based prediction models for cardiac resynchronization therapy (CRT) response mainly focus on the QRS complex, but other information in th...

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 E...

Background and purpose The electrocardiogram (ECG) is frequently obtained in the work-up of COVID-19 patients. So far, no study has evaluated whether ECG-based machine learning models have added value to predict in-hospital mortality specifically in COVID-19 patients. Methods Using data from the CAPACITY-COVID registry, we studied 882 patients adm...

Background Deep neural networks (DNNs) show excellent performance in interpreting electrocardiograms (ECGs), both for conventional ECG interpretation and for novel applications such as detection of reduced ejection fraction and prediction of one-year mortality. Despite these promising developments, clinical implementation is severely hampered by th...

Introduction: Patients with non-ischemic DCM are at risk of life-threatening ventricular arrhythmias. Improved patient selection for ICD implantation is warranted. Deep neural networks (DNN) can discover complex ECG features without the need for hand-crafted feature extraction. This study aimed to distinguish patients at risk of arrhythmias and det...

Electrocardiography (ECG) is an effective and non-invasive diagnostic tool that measures the electrical activity of the heart. Interpretation of ECG signals to detect various abnormalities is a challenging task that requires expertise. Recently, the use of deep neural networks for ECG classification to aid medical practitioners has become popular,...

Background Interpretation of electrocardiograms (ECGs) using artificial intelligence (AI) has shown very promising results recently, such as comprehensive triage of ECGs. Despite this progress, remarkably few of these AI algorithms have been implemented in clinical practice. Mayor challenges before safe widespread implementation is possible, are un...

Background Performing sex-stratified analyses in medical research can lead to new insights. Artificial intelligence is increasingly used on electrocardiograms (ECGs) for prediction of mortality, risk and diagnosis. ECG-based deep neural networks (DNN) have shown to be able to distinguish women from men. This classification inevitably leads to a mis...

Portable ECG devices with a reduced number of leads are increasingly being used in clinical practice. As part of the PhysioNet/Computing in Cardiology Challenge 2021, this study aims to develop an algorithm for automated diagnostis of reduced-lead ECGs. We compared separate baseline classifiers for the different lead-subsets with our newly proposed...

The inherited mutation (R14del) in the calcium regulatory protein phospholamban (PLN) is linked to malignant ventricular arrhythmia with poor prognosis starting at adolescence. However, the underlying early mechanisms that may serve as prognostic factors remain elusive. This study generated humanized mice in which the endogenous gene was replaced w...

Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): The Netherlands Organisation for Health Research and Development (ZonMw) University of Amsterdam Research Priority Area Medical Integromics OnBehalf CAPACITY-COVID19 Registry Background The electrocardiogram (ECG) is an easy to assess, widely available an...

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...

Background - Electrocardiogram (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 (DNN) can discover complex features in ECGs and may facilitate the detection of novel features which possibly play a pathophysiological role...

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 int...

Correct interpretation of the electrocardiogram (ECG) is critical for the diagnosis of many cardiac diseases, and current computerized algorithms are not accurate enough to provide automated comprehensive interpretation of the ECG. This study aimed to develop and validate the use of a pre-trained exponentially dilated causal convolutional neural ne...

Delirium, the clinical expression of acute encephalopathy, is a common neuropsychiatric syndrome that is related to poor outcomes, such as long-term cognitive impairment. Disturbances of functional brain networks are hypothesized to predispose for delirium. The aim of this study in non-delirious elderly individuals was to investigate whether predis...

BACKGROUND The correct interpretation of the ECG is pivotal for the accurate diagnosis of many cardiac abnormalities, and conventional computerized interpretation has not been able to reach physician‐level accuracy in detecting (acute) cardiac abnormalities. This study aims to develop and validate a deep neural network for comprehensive automated E...

Many cardiac catheter interventions require accurate discrimination between healthy and infarcted myocardia. The gold standard for infarct imaging is late gadolinium–enhanced MRI (LGE-MRI), but during cardiac procedures electroanatomical or electromechanical mapping (EAM or EMM, respectively) is usually employed. We aimed to improve the ability of...

Background: The incorporation of repeated measurements into multivariable prediction research may greatly enhance predictive performance. However, the methodological possibilities vary widely and a structured overview of the possible and utilized approaches lacks. Therefore, we [1] propose a structured framework for these approaches, [2] determine...

Delirium is common in critically ill patients and associated with increased length of stay in the intensive care unit (ICU) and long-term cognitive impairment. The pathophysiology of delirium has been explained by neuroinflammation, an aberrant stress response, neurotransmitter imbalances, and neuronal network alterations. Delirium develops mostly...