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van de Leur RR, de Brouwer R, Bleijendaal H, Verstraelen TE, Mahmoud B, Perez-Matos A, Dickhoff C, Schoonderwoerd BA, Germans T, Houweling A, van der Zwaag PA, Cox MGPJ, Peter van Tintelen J, Te Riele ASJM, van den Berg MP, Wilde AAM, Doevendans PA, de Boer RA, van Es R. ECG-only explainable deep learning algorithm predicts the risk for malignant ventricular arrhythmia in phospholamban cardiomyopathy. Heart Rhythm 2024; 21:1102-1112. [PMID: 38403235 DOI: 10.1016/j.hrthm.2024.02.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 02/15/2024] [Indexed: 02/27/2024]
Abstract
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. OBJECTIVE This study aimed to investigate whether an explainable deep learning-based approach allows risk prediction with only electrocardiogram (ECG) data. METHODS A total of 679 PLN p.(Arg14del) carriers without MVA at baseline were identified. A deep learning-based variational auto-encoder, trained on 1.1 million ECGs, was used to convert the 12-lead baseline ECG into its FactorECG, a compressed version of the ECG that summarizes it into 32 explainable factors. Prediction models were developed by Cox regression. RESULTS The deep learning-based ECG-only approach was able to predict MVA with a C statistic of 0.79 (95% CI, 0.76-0.83), comparable to the current prediction model (C statistic, 0.83 [95% CI, 0.79-0.88]; P = .054) and outperforming a model based on conventional ECG parameters (low-voltage ECG and negative T waves; C statistic, 0.65 [95% CI, 0.58-0.73]; P < .001). Clinical simulations showed that a 2-step approach, with ECG-only screening followed by a full workup, resulted in 60% less additional diagnostics while outperforming the multimodal prediction model in all patients. A visualization tool was created to provide interactive visualizations (https://pln.ecgx.ai). CONCLUSION Our deep learning-based algorithm based on ECG data only accurately predicts the occurrence of MVA in PLN p.(Arg14del) carriers, enabling more efficient stratification of patients who need additional diagnostic testing and follow-up.
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Affiliation(s)
- Rutger R van de Leur
- Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Remco de Brouwer
- Department of Cardiology, University Medical Center Groningen, Groningen, The Netherlands
| | - Hidde Bleijendaal
- Department of Cardiology, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands; European Reference Network for Rare, Low-Prevalence, or Complex Diseases of the Heart (ERN GUARD-Heart); Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Tom E Verstraelen
- Department of Cardiology, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands; European Reference Network for Rare, Low-Prevalence, or Complex Diseases of the Heart (ERN GUARD-Heart)
| | - Belend Mahmoud
- Department of Cardiology, University Medical Center Groningen, Groningen, The Netherlands
| | - Ana Perez-Matos
- Department of Cardiology, St Antonius Hospital, Sneek, The Netherlands
| | | | - Bas A Schoonderwoerd
- Department of Cardiology, Medical Centre Leeuwarden, Leeuwarden, The Netherlands
| | - Tjeerd Germans
- Department of Cardiology, Noordwest Hospital Group, Alkmaar, The Netherlands
| | - Arjan Houweling
- Department of Human Genetics, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Paul A van der Zwaag
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
| | - Moniek G P J Cox
- Department of Cardiology, University Medical Center Groningen, Groningen, The Netherlands
| | - J Peter van Tintelen
- European Reference Network for Rare, Low-Prevalence, or Complex Diseases of the Heart (ERN GUARD-Heart); Department of Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Maarten P van den Berg
- Department of Cardiology, University Medical Center Groningen, Groningen, The Netherlands
| | - Arthur A M Wilde
- Department of Cardiology, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands; European Reference Network for Rare, Low-Prevalence, or Complex Diseases of the Heart (ERN GUARD-Heart)
| | - Pieter A Doevendans
- Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands; European Reference Network for Rare, Low-Prevalence, or Complex Diseases of the Heart (ERN GUARD-Heart); Netherlands Heart Institute, Utrecht, The Netherlands; Central Military Hospital, Utrecht, The Netherlands
| | - Rudolf A de Boer
- Department of Cardiology, University Medical Center Groningen, Groningen, The Netherlands; Department of Cardiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - René van Es
- Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands
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Wagner P, Mehari T, Haverkamp W, Strodthoff N. Explaining deep learning for ECG analysis: Building blocks for auditing and knowledge discovery. Comput Biol Med 2024; 176:108525. [PMID: 38749322 DOI: 10.1016/j.compbiomed.2024.108525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 04/22/2024] [Accepted: 04/25/2024] [Indexed: 05/31/2024]
Abstract
Deep neural networks have become increasingly popular for analyzing ECG data because of their ability to accurately identify cardiac conditions and hidden clinical factors. However, the lack of transparency due to the black box nature of these models is a common concern. To address this issue, explainable AI (XAI) methods can be employed. In this study, we present a comprehensive analysis of post-hoc XAI methods, investigating the glocal (aggregated local attributions over multiple samples) and global (concept based XAI) perspectives. We have established a set of sanity checks to identify saliency as the most sensible attribution method. We provide a dataset-wide analysis across entire patient subgroups, which goes beyond anecdotal evidence, to establish the first quantitative evidence for the alignment of model behavior with cardiologists' decision rules. Furthermore, we demonstrate how these XAI techniques can be utilized for knowledge discovery, such as identifying subtypes of myocardial infarction. We believe that these proposed methods can serve as building blocks for a complementary assessment of the internal validity during a certification process, as well as for knowledge discovery in the field of ECG analysis.
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Affiliation(s)
| | - Temesgen Mehari
- Fraunhofer Heinrich Hertz Institute, Berlin, Germany; Physikalisch-Technische Bundesanstalt, Berlin, Germany.
| | | | - Nils Strodthoff
- Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.
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Lu L, Zhu T, Ribeiro AH, Clifton L, Zhao E, Zhou J, Ribeiro ALP, Zhang YT, Clifton DA. Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:247-259. [PMID: 38774384 PMCID: PMC11104458 DOI: 10.1093/ehjdh/ztae014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 02/07/2024] [Accepted: 02/14/2024] [Indexed: 05/24/2024]
Abstract
Aims Electrocardiogram (ECG) is widely considered the primary test for evaluating cardiovascular diseases. However, the use of artificial intelligence (AI) to advance these medical practices and learn new clinical insights from ECGs remains largely unexplored. We hypothesize that AI models with a specific design can provide fine-grained interpretation of ECGs to advance cardiovascular diagnosis, stratify mortality risks, and identify new clinically useful information. Methods and results Utilizing a data set of 2 322 513 ECGs collected from 1 558 772 patients with 7 years follow-up, we developed a deep-learning model with state-of-the-art granularity for the interpretable diagnosis of cardiac abnormalities, gender identification, and hypertension screening solely from ECGs, which are then used to stratify the risk of mortality. The model achieved the area under the receiver operating characteristic curve (AUC) scores of 0.998 (95% confidence interval (CI), 0.995-0.999), 0.964 (95% CI, 0.963-0.965), and 0.839 (95% CI, 0.837-0.841) for the three diagnostic tasks separately. Using ECG-predicted results, we find high risks of mortality for subjects with sinus tachycardia (adjusted hazard ratio (HR) of 2.24, 1.96-2.57), and atrial fibrillation (adjusted HR of 2.22, 1.99-2.48). We further use salient morphologies produced by the deep-learning model to identify key ECG leads that achieved similar performance for the three diagnoses, and we find that the V1 ECG lead is important for hypertension screening and mortality risk stratification of hypertensive cohorts, with an AUC of 0.816 (0.814-0.818) and a univariate HR of 1.70 (1.61-1.79) for the two tasks separately. Conclusion Using ECGs alone, our developed model showed cardiologist-level accuracy in interpretable cardiac diagnosis and the advancement in mortality risk stratification. In addition, it demonstrated the potential to facilitate clinical knowledge discovery for gender and hypertension detection which are not readily available.
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Affiliation(s)
- Lei Lu
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, UK
- School of Life Course and Population Sciences, King’s College London, London, SE1 1UL, UK
| | - Tingting Zhu
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, UK
| | - Antonio H Ribeiro
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Lei Clifton
- Nuffield Department of Population Health, University of Oxford Big Data Institute, Oxford, OX3 7LF, UK
| | - Erying Zhao
- Psychological Science and Health Management Center, Harbin Medical University, Harbin, 150076, China
- Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK
| | - Jiandong Zhou
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Division of Health Science, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK
| | - Antonio Luiz P Ribeiro
- Department of Internal Medicine, Faculdade de Medicina, and Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Yuan-Ting Zhang
- Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong SAR, China
| | - David A Clifton
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, UK
- Oxford Suzhou Centre for Advanced Research, Suzhou, 215123, China
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Sigfstead S, Jiang R, Avram R, Davies B, Krahn AD, Cheung CC. Applying Artificial Intelligence for Phenotyping of Inherited Arrhythmia Syndromes. Can J Cardiol 2024:S0828-282X(24)00335-0. [PMID: 38670456 DOI: 10.1016/j.cjca.2024.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 04/08/2024] [Accepted: 04/21/2024] [Indexed: 04/28/2024] Open
Abstract
Inherited arrhythmia disorders account for a significant proportion of sudden cardiac death, particularly among young individuals. Recent advances in our understanding of these syndromes have improved patient diagnosis and care, yet certain clinical gaps remain, particularly within case ascertainment, access to genetic testing, and risk stratification. Artificial intelligence (AI), specifically machine learning and its subset deep learning, present promising solutions to these challenges. The capacity of AI to process vast amounts of patient data and identify disease patterns differentiates them from traditional methods, which are time- and resource-intensive. To date, AI models have shown immense potential in condition detection (including asymptomatic/concealed disease) and genotype and phenotype identification, exceeding expert cardiologists in these tasks. Additionally, they have exhibited applicability for general population screening, improving case ascertainment in a set of conditions that are often asymptomatic such as left ventricular dysfunction. Third, models have shown the ability to improve testing protocols; through model identification of disease and genotype, specific clinical testing (eg, drug challenges or further diagnostic imaging) can be avoided, reducing health care expenses, speeding diagnosis, and possibly allowing for more incremental or targeted genetic testing approaches. These significant benefits warrant continued investigation of AI, particularly regarding the development and implementation of clinically applicable screening tools. In this review we summarize key developments in AI, including studies in long QT syndrome, Brugada syndrome, hypertrophic cardiomyopathy, and arrhythmogenic cardiomyopathies, and provide direction for effective future AI implementation in clinical practice.
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Affiliation(s)
- Sophie Sigfstead
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - River Jiang
- Division of Cardiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Robert Avram
- Heartwise (heartwise.ai), Montreal Heart Institute, Montreal, Quebec, Canada; Department of Medicine, Montreal Heart Institute, Université de Montréal, Montreal, Quebec, Canada
| | - Brianna Davies
- Center for Cardiovascular Innovation, Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Andrew D Krahn
- Center for Cardiovascular Innovation, Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
| | - Christopher C Cheung
- Division of Cardiology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
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5
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Togo S, Sugiura Y, Suzuki S, Ohno K, Akita K, Suwa K, Shibata SI, Kimura M, Maekawa Y. Model for classification of heart failure severity in patients with hypertrophic cardiomyopathy using a deep neural network algorithm with a 12-lead electrocardiogram. Open Heart 2023; 10:e002414. [PMID: 38056911 DOI: 10.1136/openhrt-2023-002414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 11/14/2023] [Indexed: 12/08/2023] Open
Abstract
OBJECTIVES In hypertrophic cardiomyopathy (HCM), specific ECG abnormalities are observed. Therefore, ECG is a valuable screening tool. Although several studies have reported on estimating the risk of developing fatal arrhythmias from ECG findings, the use of ECG to identify the severity of heart failure (HF) by applying deep learning (DL) methods has not been established. METHODS We assessed whether data-driven machine-learning methods could effectively identify the severity of HF in patients with HCM. A residual neural network-based model was developed using 12-lead ECG data from 218 patients with HCM and 245 patients with non-HCM, categorised them into two (mild-to-moderate and severe) or three (mild, moderate and severe) severities of HF. These severities were defined according to the New York Heart Association functional class and levels of the N-terminal prohormone of brain natriuretic peptide. In addition, the patients were divided into groups according to Kansas City Cardiomyopathy Questionnaire (KCCQ)-12. A transfer learning method was applied to resolve the issue of the low number of target samples. The model was trained in advance using PTB-XL, which is an open ECG dataset. RESULTS The model trained with our dataset achieved a weighted average F1 score of 0.745 and precision of 0.750 for the mild-to-moderate class samples. Similar results were obtained for grouping based on KCCQ-12. Through data analyses using the Guided Gradient Weighted-Class Activation Map and Integrated Gradients, QRS waves were intensively highlighted among true-positive mild-to-moderate class cases, while the highlighted part was highly variable among true-positive severe class cases. CONCLUSIONS We developed a model for classifying HF severity in patients with HCM using a deep neural network algorithm with 12-lead ECG data. Our findings suggest that applications of this DL algorithm for using 12-lead ECG data may be useful to classify the HF status in patients with HCM.
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Affiliation(s)
- Sanshiro Togo
- Hamamatsu University School of Medicine, Hamamatsu, Japan
| | - Yuki Sugiura
- Hamamatsu University School of Medicine, Hamamatsu, Japan
| | - Sayumi Suzuki
- Division of Cardiology, Internal Medicine III, Hamamatsu University School of Medicine, Hamamatsu, Japan
| | - Kazuto Ohno
- Division of Cardiology, Internal Medicine III, Hamamatsu University School of Medicine, Hamamatsu, Japan
| | - Keitaro Akita
- Division of Cardiology, Internal Medicine III, Hamamatsu University School of Medicine, Hamamatsu, Japan
| | - Kenichiro Suwa
- Division of Cardiology, Internal Medicine III, Hamamatsu University School of Medicine, Hamamatsu, Japan
| | - Shin-Ichi Shibata
- Department of Medical Informatics, Hamamatsu University School of Medicine, Hamamatsu, Japan
| | - Michio Kimura
- Department of Medical Informatics, Hamamatsu University School of Medicine, Hamamatsu, Japan
| | - Yuichiro Maekawa
- Division of Cardiology, Internal Medicine III, Hamamatsu University School of Medicine, Hamamatsu, Japan
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6
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Avula V, Wu KC, Carrick RT. Clinical Applications, Methodology, and Scientific Reporting of Electrocardiogram Deep-Learning Models: A Systematic Review. JACC. ADVANCES 2023; 2:100686. [PMID: 38288263 PMCID: PMC10824530 DOI: 10.1016/j.jacadv.2023.100686] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 08/31/2023] [Accepted: 09/19/2023] [Indexed: 01/31/2024]
Abstract
BACKGROUND The electrocardiogram (ECG) is one of the most common diagnostic tools available to assess cardio-vascular health. The advent of advanced computational techniques such as deep learning has dramatically expanded the breadth of clinical problems that can be addressed using ECG data, leading to increasing popularity of ECG deep-learning models aimed at predicting clinical endpoints. OBJECTIVES The purpose of this study was to define the current landscape of clinically relevant ECG deep-learning models and examine practices in the scientific reporting of these studies. METHODS We performed a systematic review of PubMed and EMBASE databases to identify clinically relevant ECG deep-learning models published through July 1, 2022. RESULTS We identified 44 manuscripts including 53 unique, clinically relevant ECG deep-learning models. The rate of publication of ECG deep-learning models is increasing rapidly. The most common clinical applications of ECG deep learning were identification of cardiomyopathy (14/53 [26%]), followed by arrhythmia detection (9/53 [17%]). Methodologic reporting varied; while 33/44 (75%) publications included model architecture diagrams, complete information required to reproduce these models was provided in only 10/44 (23%). Saliency analysis was performed in 20/44 (46%) of publications. Only 18/53 (34%) models were tested within external validation cohorts. Model code or resources allowing for model implementation by external groups were available for only 5/44 (11%) publications. CONCLUSIONS While ECG deep-learning models are increasingly clinically relevant, their reporting is highly variable, and few publications provide sufficient detail for methodologic reproduction or model validation by external groups. The field of ECG deep learning would benefit from adherence to a set of standardized scientific reporting guidelines.
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Affiliation(s)
- Vennela Avula
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Katherine C. Wu
- Division of Cardiology, Johns Hopkins University Department of Medicine, Baltimore, Maryland, USA
| | - Richard T. Carrick
- Division of Cardiology, Johns Hopkins University Department of Medicine, Baltimore, Maryland, USA
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7
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Taha K, van de Leur RR, Vessies M, Mast TP, Cramer MJ, Cauwenberghs N, Verstraelen TE, de Brouwer R, Doevendans PA, Wilde A, Asselbergs FW, van den Berg MP, D'hooge J, Kuznetsova T, Teske AJ, van Es R. Deep neural network-based clustering of deformation curves reveals novel disease features in PLN pathogenic variant carriers. Int J Cardiovasc Imaging 2023; 39:2149-2161. [PMID: 37566298 PMCID: PMC10673970 DOI: 10.1007/s10554-023-02924-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 07/24/2023] [Indexed: 08/12/2023]
Abstract
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.Arg14del variant carriers. A DNN was trained to discriminate PLN variant carriers (n = 278) from control subjects (n = 621) using raw deformation curves obtained by 2D-speckle tracking in the longitudinal axis. A visualization technique was used to identify the parts of these curves that were used by the DNN for classification. The PLN variant carriers were clustered according to the output of the visualization technique. The DNN showed excellent discriminatory performance (C-statistic 0.93 [95% CI 0.87-0.97]). We identified four clusters with PLN-associated disease features in the deformation curves. Two clusters showed previously described features: apical post-systolic shortening and reduced systolic strain. The two other clusters revealed novel features, both reflecting delayed relaxation. Additionally, a fifth cluster was identified containing variant carriers without disease features in the deformation curves, who were classified as controls by the DNN. This latter cluster had a very benign disease course regarding development of ventricular arrhythmias. Applying an explainable DNN-based pipeline to myocardial deformation curves enables automated detection and visualization of disease features. In PLN variant carriers, we discovered novel disease features which may improve individual risk stratification. Applying this approach to other diseases will further expand our knowledge on disease-specific deformation patterns. Overview of the deep neural network-based pipeline for feature detection in myocardial deformation curves. Firstly, phospholamban (PLN) p.Arg14del variant carriers and controls were selected and a deep neural network (DNN) was trained to detect the PLN variant carriers. Subsequently, a clustering-based approach was performed on the attention maps of the DNN, which revealed 4 distinct phenotypes of PLN variant carriers with different prognoses. Moreover, a cluster without features and a benign prognosis was detected.
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Affiliation(s)
- Karim Taha
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
| | - Rutger R van de Leur
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
| | - Melle Vessies
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
- Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
| | - Thomas P Mast
- Department of Cardiology, Catharina Ziekenhuis, Eindhoven, The Netherlands
| | - Maarten J Cramer
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Nicholas Cauwenberghs
- Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
| | - Tom E Verstraelen
- Heart Center, Department of Cardiology, Amsterdam University Medical Center, Location Academic Medical Center, Amsterdam, The Netherlands
| | - Remco de Brouwer
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Pieter A Doevendans
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
- Central Military Hospital, Utrecht, The Netherlands
| | - Arthur Wilde
- Heart Center, Department of Cardiology, Amsterdam University Medical Center, Location Academic Medical Center, Amsterdam, The Netherlands
| | - Folkert W Asselbergs
- Heart Center, Department of Cardiology, Amsterdam University Medical Center, Location Academic Medical Center, Amsterdam, The Netherlands
- Health Data Research United Kingdom and Institute of Health Informatics, University College London, London, UK
| | - Maarten P van den Berg
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Jan D'hooge
- Laboratory on Cardiovascular Imaging and Dynamics, KU Leuven, Leuven, Belgium
| | - Tatiana Kuznetsova
- Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
| | - Arco J Teske
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - René van Es
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
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8
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Doevendans PA. Should we offer preventive treatment to all carriers of PLN p.(Arg14del) variant? : Con: Do no harm to asymptomatic carriers. Neth Heart J 2023:10.1007/s12471-023-01794-z. [PMID: 37491506 PMCID: PMC10400497 DOI: 10.1007/s12471-023-01794-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/06/2023] [Indexed: 07/27/2023] Open
Affiliation(s)
- Pieter A Doevendans
- University Medical Centre Utrecht, Central Military Hospital, Utrecht, The Netherlands.
- Netherlands Heart Institute, Utrecht, The Netherlands.
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9
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Boonstra MJ, Oostendorp TF, Roudijk RW, Kloosterman M, Asselbergs FW, Loh P, Van Dam PM. Incorporating structural abnormalities in equivalent dipole layer based ECG simulations. Front Physiol 2022; 13:1089343. [PMID: 36620207 PMCID: PMC9814485 DOI: 10.3389/fphys.2022.1089343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022] Open
Abstract
Introduction: Electrical activity of the myocardium is recorded with the 12-lead ECG. ECG simulations can improve our understanding of the relation between abnormal ventricular activation in diseased myocardium and body surface potentials (BSP). However, in equivalent dipole layer (EDL)-based ECG simulations, the presence of diseased myocardium breaks the equivalence of the dipole layer. To simulate diseased myocardium, patches with altered electrophysiological characteristics were incorporated within the model. The relation between diseased myocardium and corresponding BSP was investigated in a simulation study. Methods: Activation sequences in normal and diseased myocardium were simulated and corresponding 64-lead BSP were computed in four models with distinct patch locations. QRS-complexes were compared using correlation coefficient (CC). The effect of different types of patch activation was assessed. Of one patient, simulated electrograms were compared to electrograms recorded during invasive electro-anatomical mapping. Results: Hundred-fifty-three abnormal activation sequences were simulated. Median QRS-CC of delayed versus dyssynchronous were significantly different (1.00 vs. 0.97, p < 0.001). Depending on the location of the patch, BSP leads were affected differently. Within diseased regions, fragmentation, low bipolar voltages and late potentials were observed in both recorded and simulated electrograms. Discussion: A novel method to simulate cardiomyopathy in EDL-based ECG simulations was established and evaluated. The new patch-based approach created a realistic relation between ECG waveforms and underlying activation sequences. Findings in the simulated cases were in agreement with clinical observations. With this method, our understanding of disease progression in cardiomyopathies may be further improved and used in advanced inverse ECG procedures.
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Affiliation(s)
- Machteld J Boonstra
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands,*Correspondence: Machteld J Boonstra,
| | - Thom F Oostendorp
- Radboud University Nijmegen Medical Centre, Donders Institute for Brain, Cognition and Behavior, Nijmegen, Netherlands
| | - Rob W Roudijk
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Manon Kloosterman
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Folkert W Asselbergs
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands,Faculty of Population Health Sciences, Institute of Cardiovascular Science, University College London, London, United Kingdom,Health Data Research UK and Institute of Health Informatics, University College London, London, United Kingdom
| | - Peter Loh
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Peter M Van Dam
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands,ECG Excellence BV, Nieuwerbrug aan den Rijn, Weijland, Netherlands
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10
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van de Leur RR, Hassink RJ, van Es R. Variational auto-encoders improve explainability over currently employed heatmap methods for deep learning-based interpretation of the electrocardiogram. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 3:502-504. [PMID: 36710900 PMCID: PMC9779792 DOI: 10.1093/ehjdh/ztac063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Rutger R van de Leur
- Department of Cardiology, University Medical Center Utrecht, Internal ref E03.511, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Rutger J Hassink
- Department of Cardiology, University Medical Center Utrecht, Internal ref E03.511, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - René van Es
- Corresponding author. Tel: +0031 88 757 3453, Fax: +0031 88 757 3453,
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11
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van de Leur RR, Bos MN, Taha K, Sammani A, Yeung MW, van Duijvenboden S, Lambiase PD, Hassink RJ, van der Harst P, Doevendans PA, Gupta DK, van Es R. Improving explainability of deep neural network-based electrocardiogram interpretation using variational auto-encoders . EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 3:390-404. [PMID: 36712164 PMCID: PMC9707974 DOI: 10.1093/ehjdh/ztac038] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 06/16/2022] [Indexed: 02/01/2023]
Abstract
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. Methods and results We present a novel pipeline consisting of a variational auto-encoder (VAE) to learn the underlying factors of variation of the median beat ECG morphology (the FactorECG), which are subsequently used in common and interpretable prediction models. As the ECG factors can be made explainable by generating and visualizing ECGs on both the model and individual level, the pipeline provides improved explainability over heatmap-based methods. By training on a database with 1.1 million ECGs, the VAE can compress the ECG into 21 generative ECG factors, most of which are associated with physiologically valid underlying processes. Performance of the explainable pipeline was similar to 'black box' DNNs in conventional ECG interpretation [area under the receiver operating curve (AUROC) 0.94 vs. 0.96], detection of reduced EF (AUROC 0.90 vs. 0.91), and prediction of 1-year mortality (AUROC 0.76 vs. 0.75). Contrary to the 'black box' DNNs, our pipeline provided explainability on which morphological ECG changes were important for prediction. Results were confirmed in a population-based external validation dataset. Conclusions Future studies on DNNs for ECGs should employ pipelines that are explainable to facilitate clinical implementation by gaining confidence in artificial intelligence and making it possible to identify biased models.
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Affiliation(s)
| | | | - Karim Taha
- Department of Cardiology, University Medical Center Utrecht, Internal ref E03.511, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands,Netherlands Heart Institute, Moreelsepark 1, 3511 EP Utrecht, The Netherlands
| | - Arjan Sammani
- Department of Cardiology, University Medical Center Utrecht, Internal ref E03.511, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Ming Wai Yeung
- Department of Cardiology, University Medical Center Groningen, Hanzeplein 1. 9713 GZ Groningen, The Netherlands
| | - Stefan van Duijvenboden
- Institute of Cardiovascular Science, University College London, 62 Huntley St, London Wc1E 6Dd, UK
| | - Pier D Lambiase
- Institute of Cardiovascular Science, University College London, 62 Huntley St, London Wc1E 6Dd, UK
| | - Rutger J Hassink
- Department of Cardiology, University Medical Center Utrecht, Internal ref E03.511, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Pim van der Harst
- Department of Cardiology, University Medical Center Utrecht, Internal ref E03.511, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Pieter A Doevendans
- Department of Cardiology, University Medical Center Utrecht, Internal ref E03.511, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands,Netherlands Heart Institute, Moreelsepark 1, 3511 EP Utrecht, The Netherlands,Central Military Hospital, Lundlaan 1, 3584 Utrecht, The Netherlands
| | - Deepak K Gupta
- Informatics Institute, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - René van Es
- Corresponding author. Tel: +31 88 757 3453, Fax: +31 88 757 3453,
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12
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Sammani A, van de Leur RR, Henkens MTHM, Meine M, Loh P, Hassink RJ, Oberski DL, Heymans SRB, Doevendans PA, Asselbergs FW, Te Riele ASJM, van Es R. Life-threatening ventricular arrhythmia prediction in patients with dilated cardiomyopathy using explainable electrocardiogram-based deep neural networks. Europace 2022; 24:1645-1654. [PMID: 35762524 PMCID: PMC9559909 DOI: 10.1093/europace/euac054] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 04/10/2022] [Indexed: 11/17/2022] Open
Abstract
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. Methods and results In this two-phase study, we first developed a variational autoencoder DNN on more than 1 million 12-lead median beat ECGs, compressing the ECG into 21 different factors (F): FactorECG. Next, we used two cohorts with a combined total of 695 DCM patients and entered these factors in a Cox regression for the composite LTVA outcome, which was defined as sudden cardiac arrest, spontaneous sustained ventricular tachycardia, or implantable cardioverter-defibrillator treated ventricular arrhythmia. Most patients were male (n = 442, 64%) with a median age of 54 years [interquartile range (IQR) 44–62], and median left ventricular ejection fraction of 30% (IQR 23–39). A total of 115 patients (16.5%) reached the study outcome. Factors F8 (prolonged PR-interval and P-wave duration, P < 0.005), F15 (reduced P-wave height, P = 0.04), F25 (increased right bundle branch delay, P = 0.02), F27 (P-wave axis P < 0.005), and F32 (reduced QRS-T voltages P = 0.03) were significantly associated with LTVA. Conclusion Inherently explainable DNNs can detect patients at risk of LTVA which is mainly driven by P-wave abnormalities.
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Affiliation(s)
- Arjan Sammani
- Department of Cardiology, University Medical Centre Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Rutger R van de Leur
- Department of Cardiology, University Medical Centre Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Michiel T H M Henkens
- Department of Cardiology, CARIM, Maastricht University Medical Centre, Maastricht, The Netherlands.,Netherlands Heart Institute (NLHI), Utrecht, The Netherlands
| | - Mathias Meine
- Department of Cardiology, University Medical Centre Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Peter Loh
- Department of Cardiology, University Medical Centre Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Rutger J Hassink
- Department of Cardiology, University Medical Centre Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Daniel L Oberski
- Department of Cardiology, University Medical Centre Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.,Department of Methodology and Statistics, Faculty of Social Sciences, Utrecht University and University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Stephane R B Heymans
- Department of Cardiology, CARIM, Maastricht University Medical Centre, Maastricht, The Netherlands.,Netherlands Heart Institute (NLHI), Utrecht, The Netherlands.,Department of Cardiovascular Research, University of Leuven, Leuven, Belgium
| | - Pieter A Doevendans
- Department of Cardiology, University Medical Centre Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.,Netherlands Heart Institute (NLHI), Utrecht, The Netherlands.,Central Military Hospital, Utrecht, The Netherlands
| | - Folkert W Asselbergs
- Department of Cardiology, University Medical Centre Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.,Institute of Cardiovascular Science and Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK
| | - Anneline S J M Te Riele
- Department of Cardiology, University Medical Centre Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - René van Es
- Department of Cardiology, University Medical Centre Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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13
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Liao S, Bokhari M, Chakraborty P, Suszko A, Jones G, Spears D, Gollob M, Zhang Z, Wang B, Chauhan VS. Use of Wearable Technology and Deep Learning to Improve the Diagnosis of Brugada Syndrome. JACC Clin Electrophysiol 2022; 8:1010-1020. [DOI: 10.1016/j.jacep.2022.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/28/2022] [Accepted: 05/04/2022] [Indexed: 10/17/2022]
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14
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Petmezas G, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers JA, Katsaggelos AK, Maglaveras N. State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint). JMIR Med Inform 2022; 10:e38454. [PMID: 35969441 PMCID: PMC9425174 DOI: 10.2196/38454] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/03/2022] [Accepted: 07/03/2022] [Indexed: 11/13/2022] Open
Abstract
Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. Objective This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. Methods The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. Results We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. Conclusions We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.
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Affiliation(s)
- Georgios Petmezas
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Leandros Stefanopoulos
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vassilis Kilintzis
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Andreas Tzavelis
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States
| | - John A Rogers
- Department of Material Science, Northwestern University, Evanston, IL, United States
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Nicos Maglaveras
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
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15
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Siegersma KR, van de Leur RR, Onland-Moret NC, Leon DA, Diez-Benavente E, Rozendaal L, Bots ML, Coronel R, Appelman Y, Hofstra L, van der Harst P, Doevendans PA, Hassink RJ, den Ruijter HM, van Es R. Deep neural networks reveal novel sex-specific electrocardiographic features relevant for mortality risk. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 3:245-254. [PMID: 36713005 PMCID: PMC9707888 DOI: 10.1093/ehjdh/ztac010] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 02/04/2022] [Accepted: 03/18/2022] [Indexed: 02/01/2023]
Abstract
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. Second, we studied ECG drivers of DNN-classified sex and mortality. Methods and results A DNN was trained to classify sex based on 131 673 normal ECGs. The algorithm was validated on internal (68 500 ECGs) and external data sets (3303 and 4457 ECGs). The survival of sex (mis)classified groups was investigated using time-to-event analysis and sex-stratified mediation analysis of ECG features. The DNN successfully distinguished female from male ECGs {internal validation: area under the curve (AUC) 0.96 [95% confidence interval (CI): 0.96, 0.97]; external validations: AUC 0.89 (95% CI: 0.88, 0.90), 0.94 (95% CI: 0.93, 0.94)}. Sex-misclassified individuals (11%) had a 1.4 times higher mortality risk compared with correctly classified peers. The ventricular rate was the strongest mediating ECG variable (41%, 95% CI: 31%, 56%) in males, while the maximum amplitude of the ST segment was strongest in females (18%, 95% CI: 11%, 39%). Short QRS duration was associated with higher mortality risk. Conclusion Deep neural networks accurately classify sex based on ECGs. While the proportion of ECG-based sex misclassifications is low, it is an interesting biomarker. Investigation of the causal pathway between misclassification and mortality uncovered new ECG features that might be associated with mortality. Increased emphasis on sex as a biological variable in artificial intelligence is warranted.
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Affiliation(s)
| | | | - N Charlotte Onland-Moret
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - David A Leon
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK,International Laboratory for Population and Health, National Research University, Higher School of Economics, Moscow 101000, Russian Federation,Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
| | - Ernest Diez-Benavente
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | | | - Michiel L Bots
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Ruben Coronel
- Heart Center, Department of Experimental Cardiology, AMC, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Yolande Appelman
- Department of Cardiology, Amsterdam University Medical Centres, VU University Amsterdam, Amsterdam, The Netherlands
| | - Leonard Hofstra
- Department of Cardiology, Amsterdam University Medical Centres, VU University Amsterdam, Amsterdam, The Netherlands,Cardiology Centers of the Netherlands, Amsterdam, The Netherlands
| | - Pim van der Harst
- Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Pieter A Doevendans
- Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands,Netherlands Heart Institute, Utrecht, The Netherlands
| | - Rutger J Hassink
- Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands
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16
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van de Leur RR, Bleijendaal H, Taha K, Mast T, Gho JMIH, Linschoten M, van Rees B, Henkens MTHM, Heymans S, Sturkenboom N, Tio RA, Offerhaus JA, Bor WL, Maarse M, Haerkens-Arends HE, Kolk MZH, van der Lingen ACJ, Selder JJ, Wierda EE, van Bergen PFMM, Winter MM, Zwinderman AH, Doevendans PA, van der Harst P, Pinto YM, Asselbergs FW, van Es R, Tjong FVY. Electrocardiogram-based mortality prediction in patients with COVID-19 using machine learning. Neth Heart J 2022; 30:312-318. [PMID: 35301688 PMCID: PMC8929464 DOI: 10.1007/s12471-022-01670-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/27/2022] [Indexed: 11/09/2022] Open
Abstract
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 admitted with COVID-19 across seven hospitals in the Netherlands. Raw format 12-lead ECGs recorded within 72 h of admission were studied. With data from five hospitals (n = 634), three models were developed: (a) a logistic regression baseline model using age and sex, (b) a least absolute shrinkage and selection operator (LASSO) model using age, sex and human annotated ECG features, and (c) a pre-trained deep neural network (DNN) using age, sex and the raw ECG waveforms. Data from two hospitals (n = 248) was used for external validation. Results Performances for models a, b and c were comparable with an area under the receiver operating curve of 0.73 (95% confidence interval [CI] 0.65–0.79), 0.76 (95% CI 0.68–0.82) and 0.77 (95% CI 0.70–0.83) respectively. Predictors of mortality in the LASSO model were age, low QRS voltage, ST depression, premature atrial complexes, sex, increased ventricular rate, and right bundle branch block. Conclusion This study shows that the ECG-based prediction models could be helpful for the initial risk stratification of patients diagnosed with COVID-19, and that several ECG abnormalities are associated with in-hospital all-cause mortality of COVID-19 patients. Moreover, this proof-of-principle study shows that the use of pre-trained DNNs for ECG analysis does not underperform compared with time-consuming manual annotation of ECG features. Supplementary Information The online version of this article (10.1007/s12471-022-01670-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- R R van de Leur
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.,Netherlands Heart Institute, Utrecht, The Netherlands
| | - H Bleijendaal
- Department of Clinical and Experimental Cardiology, Amsterdam University Medical Centres, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, The Netherlands.,Department of Clinical Epidemiology, Biostatistics & Bioinformatics, Amsterdam University Medical Centres, University of Amsterdam, Amsterdam, The Netherlands
| | - K Taha
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.,Netherlands Heart Institute, Utrecht, The Netherlands
| | - T Mast
- Department of Cardiology, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - J M I H Gho
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.,Department of Cardiology, Jeroen Bosch Hospital, 's-Hertogenbosch, The Netherlands
| | - M Linschoten
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - B van Rees
- Department of Cardiology, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - M T H M Henkens
- Department of Cardiology, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - S Heymans
- Department of Cardiology, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands.,Centre for Molecular and Vascular Biology, Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - N Sturkenboom
- Department of Cardiology, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - R A Tio
- Department of Cardiology, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - J A Offerhaus
- Department of Clinical and Experimental Cardiology, Amsterdam University Medical Centres, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - W L Bor
- Department of Cardiology, St. Antonius Hospital, Nieuwegein, The Netherlands
| | - M Maarse
- Department of Clinical and Experimental Cardiology, Amsterdam University Medical Centres, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, The Netherlands.,Department of Cardiology, St. Antonius Hospital, Nieuwegein, The Netherlands
| | - H E Haerkens-Arends
- Department of Cardiology, Jeroen Bosch Hospital, 's-Hertogenbosch, The Netherlands
| | - M Z H Kolk
- Department of Clinical and Experimental Cardiology, Amsterdam University Medical Centres, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - A C J van der Lingen
- Department of Cardiology, Amsterdam University Medical Centres, Amsterdam Cardiovascular Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - J J Selder
- Department of Cardiology, Amsterdam University Medical Centres, Amsterdam Cardiovascular Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - E E Wierda
- Department of Cardiology, Dijklander Hospital, Hoorn, The Netherlands
| | | | - M M Winter
- Department of Clinical and Experimental Cardiology, Amsterdam University Medical Centres, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - A H Zwinderman
- Department of Clinical Epidemiology, Biostatistics & Bioinformatics, Amsterdam University Medical Centres, University of Amsterdam, Amsterdam, The Netherlands
| | - P A Doevendans
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.,Netherlands Heart Institute, Utrecht, The Netherlands.,Central Military Hospital, Utrecht, The Netherlands
| | - P van der Harst
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Y M Pinto
- Department of Clinical and Experimental Cardiology, Amsterdam University Medical Centres, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - F W Asselbergs
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.,Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK.,Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - R van Es
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - F V Y Tjong
- Department of Clinical and Experimental Cardiology, Amsterdam University Medical Centres, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, The Netherlands.
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Clinical and Molecular Characteristics of Patients with PLN R14del Cardiomyopathy: State-of-the-Art Review. CARDIOGENETICS 2022. [DOI: 10.3390/cardiogenetics12010012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
The deletion of the arginine 14 codon (R14del) in the phospholamban (PLN) gene is a rare cause of arrhythmogenic cardiomyopathy (ACM) and is associated with prevalent ventricular arrhythmias, heart failure, and sudden cardiac death. The pathophysiological mechanism which culminates in the ACM phenotype is multifactorial and mainly based on the alteration of the endoplasmic reticulum proteostasis, mitochondrial dysfunction and compromised Ca2+ cytosolic homeostasis. The symptoms of this condition are usually non-specific and consist of arrhythmia-related or heart failure-related manifestation; however, some peculiar diagnostic clues were detected, such as the T-wave inversion in the lateral leads, low QRS complexes voltages, mid-wall or epicardial fibrosis of the inferolateral wall of the left ventricle, and their presence should raise the suspicion of this condition. The risk stratification for sudden cardiac death is mandatory and several predictors were identified in recent years. However, the management of affected patients is often challenging due to the absence of specific prediction tools and therapies. This review aims to provide the current state of the art of PLN R14del cardiomyopathy, focusing on its pathophysiology, clinical manifestation, risk stratification for sudden cardiac death, and management.
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18
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Echocardiographic Deformation Imaging for Early Detection of Genetic Cardiomyopathies: JACC Review Topic of the Week. J Am Coll Cardiol 2022; 79:594-608. [PMID: 35144751 DOI: 10.1016/j.jacc.2021.11.045] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/12/2021] [Accepted: 11/22/2021] [Indexed: 12/14/2022]
Abstract
Clinical screening of the relatives of patients with genetic cardiomyopathies is challenging, as they often lack detectable cardiac abnormalities at presentation. Life-threatening adverse events can already occur in these early stages of disease, so sensitive tools to reveal the earliest signs of disease are needed. The utility of echocardiographic deformation imaging for early detection has been explored for this population in multiple studies but has not been broadly implemented in clinical practice. The authors discuss contemporary evidence on the utility of deformation imaging in relatives of patients with genetic cardiomyopathies. The available body of data shows that deformation imaging reveals early disease-specific abnormalities in dilated cardiomyopathy, hypertrophic cardiomyopathy, and arrhythmogenic cardiomyopathy. Deformation imaging seems promising to enhance the screening and follow-up protocols in relatives, and the authors propose measures to accelerate its implementation in clinical care.
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19
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Bleijendaal H, Croon PM, Pool MDO, Malekzadeh A, Aufiero S, Amin AS, Zwinderman AH, Pinto YM, Wilde AA, Winter MM. Clinical applicability of artificial intelligence for patients with an inherited heart disease: a scoping review. Trends Cardiovasc Med 2022:S1050-1738(22)00013-5. [DOI: 10.1016/j.tcm.2022.01.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 01/06/2022] [Accepted: 01/23/2022] [Indexed: 01/22/2023]
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20
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Maleckar MM, Myklebust L, Uv J, Florvaag PM, Strøm V, Glinge C, Jabbari R, Vejlstrup N, Engstrøm T, Ahtarovski K, Jespersen T, Tfelt-Hansen J, Naumova V, Arevalo H. Combined In-silico and Machine Learning Approaches Toward Predicting Arrhythmic Risk in Post-infarction Patients. Front Physiol 2021; 12:745349. [PMID: 34819872 PMCID: PMC8606551 DOI: 10.3389/fphys.2021.745349] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 10/06/2021] [Indexed: 11/29/2022] Open
Abstract
Background: Remodeling due to myocardial infarction (MI) significantly increases patient arrhythmic risk. Simulations using patient-specific models have shown promise in predicting personalized risk for arrhythmia. However, these are computationally- and time- intensive, hindering translation to clinical practice. Classical machine learning (ML) algorithms (such as K-nearest neighbors, Gaussian support vector machines, and decision trees) as well as neural network techniques, shown to increase prediction accuracy, can be used to predict occurrence of arrhythmia as predicted by simulations based solely on infarct and ventricular geometry. We present an initial combined image-based patient-specific in silico and machine learning methodology to assess risk for dangerous arrhythmia in post-infarct patients. Furthermore, we aim to demonstrate that simulation-supported data augmentation improves prediction models, combining patient data, computational simulation, and advanced statistical modeling, improving overall accuracy for arrhythmia risk assessment. Methods: MRI-based computational models were constructed from 30 patients 5 days post-MI (the “baseline” population). In order to assess the utility biophysical model-supported data augmentation for improving arrhythmia prediction, we augmented the virtual baseline patient population. Each patient ventricular and ischemic geometry in the baseline population was used to create a subfamily of geometric models, resulting in an expanded set of patient models (the “augmented” population). Arrhythmia induction was attempted via programmed stimulation at 17 sites for each virtual patient corresponding to AHA LV segments and simulation outcome, “arrhythmia,” or “no-arrhythmia,” were used as ground truth for subsequent statistical prediction (machine learning, ML) models. For each patient geometric model, we measured and used choice data features: the myocardial volume and ischemic volume, as well as the segment-specific myocardial volume and ischemia percentage, as input to ML algorithms. For classical ML techniques (ML), we trained k-nearest neighbors, support vector machine, logistic regression, xgboost, and decision tree models to predict the simulation outcome from these geometric features alone. To explore neural network ML techniques, we trained both a three - and a four-hidden layer multilayer perceptron feed forward neural networks (NN), again predicting simulation outcomes from these geometric features alone. ML and NN models were trained on 70% of randomly selected segments and the remaining 30% was used for validation for both baseline and augmented populations. Results: Stimulation in the baseline population (30 patient models) resulted in reentry in 21.8% of sites tested; in the augmented population (129 total patient models) reentry occurred in 13.0% of sites tested. ML and NN models ranged in mean accuracy from 0.83 to 0.86 for the baseline population, improving to 0.88 to 0.89 in all cases. Conclusion: Machine learning techniques, combined with patient-specific, image-based computational simulations, can provide key clinical insights with high accuracy rapidly and efficiently. In the case of sparse or missing patient data, simulation-supported data augmentation can be employed to further improve predictive results for patient benefit. This work paves the way for using data-driven simulations for prediction of dangerous arrhythmia in MI patients.
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Affiliation(s)
- Mary M Maleckar
- Computational Physiology, Simula Research Laboratory, Oslo, Norway
| | - Lena Myklebust
- Computational Physiology, Simula Research Laboratory, Oslo, Norway
| | - Julie Uv
- Computational Physiology, Simula Research Laboratory, Oslo, Norway
| | | | - Vilde Strøm
- Computational Physiology, Simula Research Laboratory, Oslo, Norway
| | - Charlotte Glinge
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Reza Jabbari
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Niels Vejlstrup
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Thomas Engstrøm
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Kiril Ahtarovski
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Thomas Jespersen
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jacob Tfelt-Hansen
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.,Department of Forensic Medicine, Faculty of Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Valeriya Naumova
- Computational Physiology, Simula Research Laboratory, Oslo, Norway
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21
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Melzi P, Tolosana R, Cecconi A, Sanz-Garcia A, Ortega GJ, Jimenez-Borreguero LJ, Vera-Rodriguez R. Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization. Sci Rep 2021; 11:22786. [PMID: 34815461 PMCID: PMC8610971 DOI: 10.1038/s41598-021-02179-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 11/10/2021] [Indexed: 11/26/2022] Open
Abstract
Atrial fibrillation (AF) is an abnormal heart rhythm, asymptomatic in many cases, that causes several health problems and mortality in population. This retrospective study evaluates the ability of different AI-based models to predict future episodes of AF from electrocardiograms (ECGs) recorded during normal sinus rhythm. Patients are divided into two classes according to AF occurrence or sinus rhythm permanence along their several ECGs registry. In the constrained scenario of balancing the age distributions between classes, our best AI model predicts future episodes of AF with area under the curve (AUC) 0.79 (0.72–0.86). Multiple scenarios and age-sex-specific groups of patients are considered, achieving best performance of prediction for males older than 70 years. These results point out the importance of considering different demographic groups in the analysis of AF prediction, showing considerable performance gaps among them. In addition to the demographic analysis, we apply feature visualization techniques to identify the most important portions of the ECG signals in the task of AF prediction, improving this way the interpretability and understanding of the AI models. These results and the simplicity of recording ECGs during check-ups add feasibility to clinical applications of AI-based models.
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Affiliation(s)
- Pietro Melzi
- Biometrics and Data Pattern Analytics Lab, Escuela Politecnica Superior, Universidad Autonoma de Madrid, Calle Francisco Tomas Y Valiente, 11, C-235, 28049, Madrid, Spain
| | - Ruben Tolosana
- Biometrics and Data Pattern Analytics Lab, Escuela Politecnica Superior, Universidad Autonoma de Madrid, Calle Francisco Tomas Y Valiente, 11, C-235, 28049, Madrid, Spain.
| | - Alberto Cecconi
- Instituto de Investigacion Sanitaria del Hospital Universitario de La Princesa, Madrid, Spain
| | - Ancor Sanz-Garcia
- Instituto de Investigacion Sanitaria del Hospital Universitario de La Princesa, Madrid, Spain
| | - Guillermo J Ortega
- Instituto de Investigacion Sanitaria del Hospital Universitario de La Princesa, Madrid, Spain.,Science and Technology Department, National University of Quilmes, Bernal, Argentina.,Consejo Nacional de Investigaciones Cientificas y Tecnicas, CONICET, Buenos Aires, Argentina
| | - Luis Jesus Jimenez-Borreguero
- Instituto de Investigacion Sanitaria del Hospital Universitario de La Princesa, Madrid, Spain.,CIBERCV, Centro de Investigacion Biomedica en Red Enfermedades Cardiovasculares, Madrid, Spain
| | - Ruben Vera-Rodriguez
- Biometrics and Data Pattern Analytics Lab, Escuela Politecnica Superior, Universidad Autonoma de Madrid, Calle Francisco Tomas Y Valiente, 11, C-235, 28049, Madrid, Spain
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22
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Bleijendaal H, Wilde AAM. From a Polish 3-Year-Old Boy Who Visited Maastricht to Automatic Detection Using Deep Learning: Brugada Syndrome Is Being Revolutionised. Can J Cardiol 2021; 38:149-151. [PMID: 34571168 DOI: 10.1016/j.cjca.2021.09.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 09/15/2021] [Accepted: 09/16/2021] [Indexed: 11/28/2022] Open
Affiliation(s)
- Hidde Bleijendaal
- Heart Center, Department of Clinical Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
| | - Arthur A M Wilde
- Heart Center, Department of Clinical Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
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23
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The Role of Artificial Intelligence and Machine Learning in Clinical Cardiac Electrophysiology. Can J Cardiol 2021; 38:246-258. [PMID: 34333029 DOI: 10.1016/j.cjca.2021.07.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 07/11/2021] [Accepted: 07/25/2021] [Indexed: 11/21/2022] Open
Abstract
In recent years, artificial intelligence (AI) has found numerous applications in cardiology due in part to large digitized datasets and the evolution of high performance computing. In the discipline of cardiac electrophysiology (EP), a number of clinical, imaging, and electrical waveform data are considered in the diagnosis, prognostication and management of arrhythmias, which lend themselves well to automation through AI. But equally relevant, AI offers a unique opportunity to discover novel EP concepts and improve clinical care through its inherent, hierarchical tenets of self-learning. This review will focus on the application of AI in clinical EP and summarize state-of-the art, large, clinical studies in the following key domains: (1) ECG-based arrhythmia and disease classification, (2) atrial fibrillation source detection, (3) substrate and risk assessment for atrial fibrillation and ventricular tachyarrhythmias, and (4) predicting outcomes after cardiac resynchronization therapy. Many are small, single-center, proof-of-concept investigations, but they still demonstrate groundbreaking performance of deep learning, a subdomain of AI, which surpasses traditional statistical analysis. Larger studies, for instance classifying arrhythmias from ECG recordings, have further provided external validation of their high accuracy. Ultimately, the performance of AI is dependent on the quality of the input data and the rigor of algorithm development. The field is still nascent and several barriers will need to be overcome, including prospective validation in large, well-labelled datasets and more seamless information technology-based data collection/integration, before AI can be adopted into broader clinical EP practice. This review will conclude with a discussion of these challenges and future work.
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24
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Impaired Right Ventricular Calcium Cycling Is an Early Risk Factor in R14del-Phospholamban Arrhythmias. J Pers Med 2021; 11:jpm11060502. [PMID: 34204946 PMCID: PMC8226909 DOI: 10.3390/jpm11060502] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 05/27/2021] [Accepted: 05/30/2021] [Indexed: 12/22/2022] Open
Abstract
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 with either human wild type or R14del-PLN and addressed the early molecular and cellular pathogenic mechanisms. R14del-PLN mice exhibited stress-induced impairment of atrioventricular conduction, and prolongation of both ventricular activation and repolarization times in association with ventricular tachyarrhythmia, originating from the right ventricle (RV). Most of these distinct electrocardiographic features were remarkably similar to those in R14del-PLN patients. Studies in isolated cardiomyocytes revealed RV-specific calcium defects, including prolonged action potential duration, depressed calcium kinetics and contractile parameters, and elevated diastolic Ca-levels. Ca-sparks were also higher although SR Ca-load was reduced. Accordingly, stress conditions induced after contractions, and inclusion of the CaMKII inhibitor KN93 reversed this proarrhythmic parameter. Compensatory responses included altered expression of key genes associated with Ca-cycling. These data suggest that R14del-PLN cardiomyopathy originates with RV-specific impairment of Ca-cycling and point to the urgent need to improve risk stratification in asymptomatic carriers to prevent fatal arrhythmias and delay cardiomyopathy onset.
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25
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Sammani A, Baas AF, Asselbergs FW, te Riele ASJM. Diagnosis and Risk Prediction of Dilated Cardiomyopathy in the Era of Big Data and Genomics. J Clin Med 2021; 10:921. [PMID: 33652931 PMCID: PMC7956169 DOI: 10.3390/jcm10050921] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 02/17/2021] [Accepted: 02/22/2021] [Indexed: 12/19/2022] Open
Abstract
Dilated cardiomyopathy (DCM) is a leading cause of heart failure and life-threatening ventricular arrhythmias (LTVA). Work-up and risk stratification of DCM is clinically challenging, as there is great heterogeneity in phenotype and genotype. Throughout the last decade, improved genetic testing of patients has identified genotype-phenotype associations and enhanced evaluation of at-risk relatives leading to better patient prognosis. The field is now ripe to explore opportunities to improve personalised risk assessments. Multivariable risk models presented as "risk calculators" can incorporate a multitude of clinical variables and predict outcome (such as heart failure hospitalisations or LTVA). In addition, genetic risk scores derived from genome/exome-wide association studies can estimate an individual's lifetime genetic risk of developing DCM. The use of clinically granular investigations, such as late gadolinium enhancement on cardiac magnetic resonance imaging, is warranted in order to increase predictive performance. To this end, constructing big data infrastructures improves accessibility of data by using electronic health records, existing research databases, and disease registries. By applying methods such as machine and deep learning, we can model complex interactions, identify new phenotype clusters, and perform prognostic modelling. This review aims to provide an overview of the evolution of DCM definitions as well as its clinical work-up and considerations in the era of genomics. In addition, we present exciting examples in the field of big data infrastructures, personalised prognostic assessment, and artificial intelligence.
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Affiliation(s)
- Arjan Sammani
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, 3582 CX Utrecht, The Netherlands; (A.S.); (F.W.A.)
| | - Annette F. Baas
- Department of Genetics, Division Laboratories, Pharmacy and Biomedical Genetics, University Medical Centre Utrecht, University of Utrecht, 3582 CX Utrecht, The Netherlands;
| | - Folkert W. Asselbergs
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, 3582 CX Utrecht, The Netherlands; (A.S.); (F.W.A.)
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London WC1E 6BT, UK
- Health Data Research UK and Institute of Health Informatics, University College London, London WC1E 6BT, UK
| | - Anneline S. J. M. te Riele
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, 3582 CX Utrecht, The Netherlands; (A.S.); (F.W.A.)
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26
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van der Boon RMA. Predicting heart failure with preserved ejection fraction: revisiting an old friend with new knowledge. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:104-105. [PMID: 36711177 PMCID: PMC9707879 DOI: 10.1093/ehjdh/ztab017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Robert M A van der Boon
- Department of Cardiology, Erasmus Medical Center Rotterdam, ‘s-Gravendijkwal 230, 3015 CE, Rotterdam, the Netherlands,Corresponding author. Tel: +31650033213,
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