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Krasteva V, Stoyanov T, Schmid R, Jekova I. Delineation of 12-Lead ECG Representative Beats Using Convolutional Encoder-Decoders with Residual and Recurrent Connections. SENSORS (BASEL, SWITZERLAND) 2024; 24:4645. [PMID: 39066042 PMCID: PMC11280871 DOI: 10.3390/s24144645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Revised: 07/11/2024] [Accepted: 07/16/2024] [Indexed: 07/28/2024]
Abstract
The aim of this study is to address the challenge of 12-lead ECG delineation by different encoder-decoder architectures of deep neural networks (DNNs). This study compares four concepts for encoder-decoders based on a fully convolutional architecture (CED-Net) and its modifications with a recurrent layer (CED-LSTM-Net), residual connections between symmetrical encoder and decoder feature maps (CED-U-Net), and sequential residual blocks (CED-Res-Net). All DNNs transform 12-lead representative beats to three diagnostic ECG intervals (P-wave, QRS-complex, QT-interval) used for the global delineation of the representative beat (P-onset, P-offset, QRS-onset, QRS-offset, T-offset). All DNNs were trained and optimized using the large PhysioNet ECG database (PTB-XL) under identical conditions, applying an advanced approach for machine-based supervised learning with a reference algorithm for ECG delineation (ETM, Schiller AG, Baar, Switzerland). The test results indicate that all DNN architectures are equally capable of reproducing the reference delineation algorithm's measurements in the diagnostic PTB database with an average P-wave detection accuracy (96.6%) and time and duration errors: mean values (-2.6 to 2.4 ms) and standard deviations (2.9 to 11.4 ms). The validation according to the standard-based evaluation practices of diagnostic electrocardiographs with the CSE database outlines a CED-Net model, which measures P-duration (2.6 ± 11.0 ms), PQ-interval (0.9 ± 5.8 ms), QRS-duration (-2.4 ± 5.4 ms), and QT-interval (-0.7 ± 10.3 ms), which meet all standard tolerances. Noise tests with high-frequency, low-frequency, and power-line frequency noise (50/60 Hz) confirm that CED-Net, CED-Res-Net, and CED-LSTM-Net are robust to all types of noise, mostly presenting a mean duration error < 2.5 ms when compared to measurements without noise. Reduced noise immunity is observed for the U-net architecture. Comparative analysis with other published studies scores this research within the lower range of time errors, highlighting its competitive performance.
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Affiliation(s)
- Vessela Krasteva
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl. 105, 1113 Sofia, Bulgaria; (V.K.); (T.S.)
| | - Todor Stoyanov
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl. 105, 1113 Sofia, Bulgaria; (V.K.); (T.S.)
| | - Ramun Schmid
- Signal Processing, Schiller AG, Altgasse 68, CH-6341 Baar, Switzerland;
| | - Irena Jekova
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl. 105, 1113 Sofia, Bulgaria; (V.K.); (T.S.)
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2
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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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3
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Pastika L, Sau A, Patlatzoglou K, Sieliwonczyk E, Ribeiro AH, McGurk KA, Khan S, Mandic D, Scott WR, Ware JS, Peters NS, Ribeiro ALP, Kramer DB, Waks JW, Ng FS. Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease. NPJ Digit Med 2024; 7:167. [PMID: 38918595 PMCID: PMC11199586 DOI: 10.1038/s41746-024-01170-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 06/14/2024] [Indexed: 06/27/2024] Open
Abstract
The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone. Developed from 512,950 12-lead ECGs from the Beth Israel Deaconess Medical Center (BIDMC), a secondary care cohort, and validated on UK Biobank (UKB) (n = 42,386), the model achieved a Pearson correlation coefficient (r) of 0.65 and 0.62, and an R2 of 0.43 and 0.39 in the BIDMC cohort and UK Biobank, respectively for AI-ECG BMI vs. measured BMI. We found delta-BMI, the difference between measured BMI and AI-ECG-predicted BMI (AI-ECG-BMI), to be a biomarker of cardiometabolic health. The top tertile of delta-BMI showed increased risk of future cardiometabolic disease (BIDMC: HR 1.15, p < 0.001; UKB: HR 1.58, p < 0.001) and diabetes mellitus (BIDMC: HR 1.25, p < 0.001; UKB: HR 2.28, p < 0.001) after adjusting for covariates including measured BMI. Significant enhancements in model fit, reclassification and improvements in discriminatory power were observed with the inclusion of delta-BMI in both cohorts. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits, including SCN10A, SCN5A, EXOG and RXRG. In summary, our AI-ECG-BMI model accurately predicts BMI and introduces delta-BMI as a non-invasive biomarker for cardiometabolic risk stratification.
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Affiliation(s)
- Libor Pastika
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Arunashis Sau
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, United Kingdom
| | | | - Ewa Sieliwonczyk
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- MRC Laboratory of Medical Sciences, Imperial College London, London, United Kingdom
| | - Antônio H Ribeiro
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Kathryn A McGurk
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- MRC Laboratory of Medical Sciences, Imperial College London, London, United Kingdom
| | - Sadia Khan
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Chelsea and Westminster NHS Foundation Trust, London, United Kingdom
| | - Danilo Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
| | - William R Scott
- MRC Laboratory of Medical Sciences, Imperial College London, London, United Kingdom
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - James S Ware
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- MRC Laboratory of Medical Sciences, Imperial College London, London, United Kingdom
| | - Nicholas S Peters
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - 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
| | - Daniel B Kramer
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Jonathan W Waks
- Harvard-Thorndike Electrophysiology Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Fu Siong Ng
- National Heart and Lung Institute, Imperial College London, London, United Kingdom.
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, United Kingdom.
- Chelsea and Westminster NHS Foundation Trust, London, United Kingdom.
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4
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Ose B, Sattar Z, Gupta A, Toquica C, Harvey C, Noheria A. Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review. Curr Cardiol Rep 2024; 26:561-580. [PMID: 38753291 DOI: 10.1007/s11886-024-02062-1] [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] [Accepted: 04/17/2024] [Indexed: 06/26/2024]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is transforming electrocardiography (ECG) interpretation. AI diagnostics can reach beyond human capabilities, facilitate automated access to nuanced ECG interpretation, and expand the scope of cardiovascular screening in the population. AI can be applied to the standard 12-lead resting ECG and single-lead ECGs in external monitors, implantable devices, and direct-to-consumer smart devices. We summarize the current state of the literature on AI-ECG. RECENT FINDINGS Rhythm classification was the first application of AI-ECG. Subsequently, AI-ECG models have been developed for screening structural heart disease including hypertrophic cardiomyopathy, cardiac amyloidosis, aortic stenosis, pulmonary hypertension, and left ventricular systolic dysfunction. Further, AI models can predict future events like development of systolic heart failure and atrial fibrillation. AI-ECG exhibits potential in acute cardiac events and non-cardiac applications, including acute pulmonary embolism, electrolyte abnormalities, monitoring drugs therapy, sleep apnea, and predicting all-cause mortality. Many AI models in the domain of cardiac monitors and smart watches have received Food and Drug Administration (FDA) clearance for rhythm classification, while others for identification of cardiac amyloidosis, pulmonary hypertension and left ventricular dysfunction have received breakthrough device designation. As AI-ECG models continue to be developed, in addition to regulatory oversight and monetization challenges, thoughtful clinical implementation to streamline workflows, avoiding information overload and overwhelming of healthcare systems with false positive results is necessary. Research to demonstrate and validate improvement in healthcare efficiency and improved patient outcomes would be required before widespread adoption of any AI-ECG model.
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Affiliation(s)
- Benjamin Ose
- The University of Kansas School of Medicine, Kansas City, KS, USA
| | - Zeeshan Sattar
- Division of General and Hospital Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
| | - Amulya Gupta
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA
| | | | - Chris Harvey
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA
| | - Amit Noheria
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA.
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA.
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5
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Han C, Yoon D. An Explainable Artificial Intelligence-enabled ECG Framework for the Prediction of Subclinical Coronary Atherosclerosis. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2024; 2024:535-544. [PMID: 38827057 PMCID: PMC11141849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Coronary artery calcium (CAC) as assessed by computed tomography (CT) is a marker of subclinical coronary atherosclerosis. However, routine application of CAC scoring via CT is limited by high costs and accessibility. An electrocardiogram (ECG) is a widely-used, sensitive, cost-effective, non-invasive, and radiation-free diagnostic tool. Considering this, if artificial intelligence (AI)-enabled electrocardiograms (ECGs) could opportunistically detect CAC, it would be particularly beneficial for the asymptomatic or subclinical populations, acting as an initial screening measure, paving the way for further confirmatory tests and preventive strategies, a step ahead of conventional practices. With this aim, we developed an AI-enabled ECG framework that not only predicts a CAC score ≥400 but also offers a visual explanation of the associated potential morphological ECG changes, and tested its efficacy on individuals undergoing health checkups, a group primarily comprising healthy or subclinical individuals. To ensure broader applicability, we performed external validation at a separate institution.
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Affiliation(s)
- Changho Han
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin-si, Gyeonggi-do, Republic of Korea
| | - Dukyong Yoon
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin-si, Gyeonggi-do, Republic of Korea
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6
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Linz D, Andrade JG, Arbelo E, Boriani G, Breithardt G, Camm AJ, Caso V, Nielsen JC, De Melis M, De Potter T, Dichtl W, Diederichsen SZ, Dobrev D, Doll N, Duncker D, Dworatzek E, Eckardt L, Eisert C, Fabritz L, Farkowski M, Filgueiras-Rama D, Goette A, Guasch E, Hack G, Hatem S, Haeusler KG, Healey JS, Heidbuechel H, Hijazi Z, Hofmeister LH, Hove-Madsen L, Huebner T, Kääb S, Kotecha D, Malaczynska-Rajpold K, Merino JL, Metzner A, Mont L, Ng GA, Oeff M, Parwani AS, Puererfellner H, Ravens U, Rienstra M, Sanders P, Scherr D, Schnabel R, Schotten U, Sohns C, Steinbeck G, Steven D, Toennis T, Tzeis S, van Gelder IC, van Leerdam RH, Vernooy K, Wadhwa M, Wakili R, Willems S, Witt H, Zeemering S, Kirchhof P. Longer and better lives for patients with atrial fibrillation: the 9th AFNET/EHRA consensus conference. Europace 2024; 26:euae070. [PMID: 38591838 PMCID: PMC11003300 DOI: 10.1093/europace/euae070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 02/16/2024] [Indexed: 04/10/2024] Open
Abstract
AIMS Recent trial data demonstrate beneficial effects of active rhythm management in patients with atrial fibrillation (AF) and support the concept that a low arrhythmia burden is associated with a low risk of AF-related complications. The aim of this document is to summarize the key outcomes of the 9th AFNET/EHRA Consensus Conference of the Atrial Fibrillation NETwork (AFNET) and the European Heart Rhythm Association (EHRA). METHODS AND RESULTS Eighty-three international experts met in Münster for 2 days in September 2023. Key findings are as follows: (i) Active rhythm management should be part of the default initial treatment for all suitable patients with AF. (ii) Patients with device-detected AF have a low burden of AF and a low risk of stroke. Anticoagulation prevents some strokes and also increases major but non-lethal bleeding. (iii) More research is needed to improve stroke risk prediction in patients with AF, especially in those with a low AF burden. Biomolecules, genetics, and imaging can support this. (iv) The presence of AF should trigger systematic workup and comprehensive treatment of concomitant cardiovascular conditions. (v) Machine learning algorithms have been used to improve detection or likely development of AF. Cooperation between clinicians and data scientists is needed to leverage the potential of data science applications for patients with AF. CONCLUSIONS Patients with AF and a low arrhythmia burden have a lower risk of stroke and other cardiovascular events than those with a high arrhythmia burden. Combining active rhythm control, anticoagulation, rate control, and therapy of concomitant cardiovascular conditions can improve the lives of patients with AF.
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Affiliation(s)
- Dominik Linz
- Department of Cardiology, Maastricht University Medical Center, Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jason G Andrade
- Division of Cardiology, Vancouver General Hospital, Vancouver, Canada
- Montreal Heart Institute, Montreal, Canada
| | - Elena Arbelo
- Institut Clínic Cardiovascular, Hospital Clinic, Universitat de Barcelona, Barcelona, Catalonia, Spain
- Institut d’Investigació August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
- European Reference Network for Rare, Low Prevalence and Complex Diseases of the Heart—ERN GUARD-Heart
| | - Giuseppe Boriani
- Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Polyclinic of Modena, Modena, Italy
| | - Guenter Breithardt
- Department of Cardiovascular Medicine, University Hospital, Münster, Germany
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
| | - A John Camm
- Cardiology Clinical Academic Group, Molecular and Clinical Sciences Institute, St. George's University of London, London, UK
| | - Valeria Caso
- Stroke Unit, Santa Maria della Misericordia Hospital, University of Perugia, Perugia, Italy
| | - Jens Cosedis Nielsen
- Department of Cardiology, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | | | | | - Wolfgang Dichtl
- Department of Internal Medicine III, Cardiology and Angiology, Medical University Innsbruck, Innsbruck, Austria
| | | | - Dobromir Dobrev
- Institute of Pharmacology, Faculty of Medicine, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Nicolas Doll
- Department of Cardiac Surgery, Schüchtermann-Klinik, Bad Rothenfelde, Germany
| | - David Duncker
- Hannover Heart Rhythm Center, Department of Cardiology and Angiology, Hannover Medical School, Hannover, Germany
| | | | - Lars Eckardt
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- Department of Cardiology II—Electrophysiology, University Hospital Münster, Münster, Germany
| | | | - Larissa Fabritz
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- University Center of Cardiovascular Science, UHZ, UKE, Hamburg, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site: Hamburg/Kiel/Lübeck, Hamburg, Germany
- Institute of Cardiovascular Sciences, University of Birmingham, Edgbaston, Birmingham, UK
| | - Michal Farkowski
- Department of Cardiology, Ministry of Interior and Administration, National Medical Institute, Warsaw, Poland
| | - David Filgueiras-Rama
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Novel Arrhythmogenic Mechanisms Program, Madrid, Spain
- Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Cardiovascular Institute, C/ Profesor Martín Lagos, Madrid, Spain
| | - Andreas Goette
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- Department of Cardiology and Intensive Care Medicine, St Vincenz-Hospital Paderborn, Paderborn, Germany
| | - Eduard Guasch
- Institut d’Investigació August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
- Clinic Barcelona, University of Barcelona, Barcelona, Spain
| | - Guido Hack
- Bristol-Myers Squibb GmbH & Co. KGaA, Munich, Germany
| | | | - Karl Georg Haeusler
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- Department of Neurology, Universitätsklinikum Würzburg (UKW), Würzburg, Germany
| | - Jeff S Healey
- Division of Cardiology, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton, Ontario, Canada
| | - Hein Heidbuechel
- Antwerp University Hospital, Cardiovascular Sciences, University of Antwerp, Antwerp, Belgium
| | - Ziad Hijazi
- Antwerp University Hospital, Cardiovascular Sciences, University of Antwerp, Antwerp, Belgium
- Department of Medical Sciences, Cardiology, Uppsala University, Uppsala, Sweden
- Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | | | - Leif Hove-Madsen
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
- Biomedical Research Institute Barcelona (IIBB-CSIC), Barcelona, Spain
- IR Sant Pau, Hospital de Sant Pau, Barcelona, Spain
| | | | - Stefan Kääb
- European Reference Network for Rare, Low Prevalence and Complex Diseases of the Heart—ERN GUARD-Heart
- Department of Medicine I, University Hospital, LMU Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich, Munich Heart Alliance, Munich, Germany
| | - Dipak Kotecha
- Institute of Cardiovascular Sciences, University of Birmingham, Edgbaston, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Trust, Birmingham, UK
| | - Katarzyna Malaczynska-Rajpold
- Lister Hospital, East and North Hertfordshire NHS Trust, Stevenage, UK
- Royal Brompton Hospital, Guy’s and St Thomas’ NHS Foundation Trust, London, UK
| | - José Luis Merino
- La Paz University Hospital, IdiPaz, Autonomous University of Madrid, Madrid, Spain
| | - Andreas Metzner
- Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, Germany
| | - Lluís Mont
- Institut Clínic Cardiovascular, Hospital Clinic, Universitat de Barcelona, Barcelona, Catalonia, Spain
- Institut d’Investigació August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
| | - Ghulam Andre Ng
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Leicester, UK
| | - Michael Oeff
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- Cardiology Department, Medizinische Hochschule Brandenburg, Brandenburg/Havel, Germany
| | - Abdul Shokor Parwani
- Department of Cardiology, Deutsches Herzzentrum der Charité (CVK), Berlin, Germany
| | | | - Ursula Ravens
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- Institute of Experimental Cardiovascular Medicine, University Clinic Freiburg, Freiburg, Germany
| | - Michiel Rienstra
- University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Prashanthan Sanders
- Centre for Heart Rhythm Disorders, University of Adelaide and Royal Adelaide Hospital, Adelaide, Australia
| | - Daniel Scherr
- Division of Cardiology, Medical University of Graz, Graz, Austria
| | - Renate Schnabel
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site: Hamburg/Kiel/Lübeck, Hamburg, Germany
- Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, Germany
| | - Ulrich Schotten
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- Departments of Physiology, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
| | - Christian Sohns
- Herz- und Diabeteszentrum Nordrhein-Westfalen, Universitätsklinik der Ruhr-Universität Bochum, Klinik für Elektrophysiologie—Rhythmologie, Bad Oeynhausen, Germany
| | - Gerhard Steinbeck
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- Center for Cardiology at Clinic Starnberg, Starnberg, Germany
| | - Daniel Steven
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- Heart Center, Department of Electrophysiology, University Hospital Cologne, Cologne, Germany
| | - Tobias Toennis
- German Centre for Cardiovascular Research (DZHK), Partner Site: Hamburg/Kiel/Lübeck, Hamburg, Germany
- Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, Germany
| | | | - Isabelle C van Gelder
- University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | | | - Kevin Vernooy
- Department of Cardiology, Maastricht University Medical Center, Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands
| | - Manish Wadhwa
- Medical Office, Philips Ambulatory Monitoring and Diagnostics, San Diego, CA, USA
| | - Reza Wakili
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- Department of Medicine and Cardiology, Goethe University, Frankfurt, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Rhine-Main, Germany
| | - Stephan Willems
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site: Hamburg/Kiel/Lübeck, Hamburg, Germany
- Asklepios Hospital St. Georg, Department of Cardiology and Internal Care Medicine, Faculty of Medicine, Semmelweis University Campus, Hamburg, Germany
| | | | - Stef Zeemering
- Departments of Physiology, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
| | - Paulus Kirchhof
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site: Hamburg/Kiel/Lübeck, Hamburg, Germany
- Institute of Cardiovascular Sciences, University of Birmingham, Edgbaston, Birmingham, UK
- Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, Germany
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7
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Makimoto H, Kohro T. Adopting artificial intelligence in cardiovascular medicine: a scoping review. Hypertens Res 2024; 47:685-699. [PMID: 37907600 DOI: 10.1038/s41440-023-01469-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 09/03/2023] [Accepted: 09/26/2023] [Indexed: 11/02/2023]
Abstract
Recent years have witnessed significant transformations in cardiovascular medicine, driven by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to capture the breadth of AI applications within cardiovascular science. Employing a structured approach, we sourced relevant articles from PubMed, with an emphasis on journals encompassing general cardiology and digital medicine. We applied filters to highlight cardiovascular articles published in journals focusing on general internal medicine, cardiology and digital medicine, thereby identifying the prevailing trends in the field. Following a comprehensive full-text screening, a total of 140 studies were identified. Over the preceding 5 years, cardiovascular medicine's interplay with AI has seen an over tenfold augmentation. This expansive growth encompasses multiple cardiovascular subspecialties, including but not limited to, general cardiology, ischemic heart disease, heart failure, and arrhythmia. Deep learning emerged as the predominant methodology. The majority of AI endeavors in this domain have been channeled toward enhancing diagnostic and prognostic capabilities, utilizing resources such as hospital datasets, electrocardiograms, and echocardiography. A significant uptrend was observed in AI's application for omics data analysis. However, a clear gap persists in AI's full-scale integration into the clinical decision-making framework. AI, particularly deep learning, has demonstrated robust applications across cardiovascular subspecialties, indicating its transformative potential in this field. As we continue on this trajectory, ensuring the alignment of technological progress with medical ethics becomes crucial. The abundant digital health data today further accentuates the need for meticulous systematic reviews, tailoring them to each cardiovascular subspecialty.
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Affiliation(s)
- Hisaki Makimoto
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan.
| | - Takahide Kohro
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan
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8
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König S, Hohenstein S, Nitsche A, Pellissier V, Leiner J, Stellmacher L, Hindricks G, Bollmann A. Artificial intelligence-based identification of left ventricular systolic dysfunction from 12-lead electrocardiograms: external validation and advanced application of an existing model. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:144-151. [PMID: 38505486 PMCID: PMC10944686 DOI: 10.1093/ehjdh/ztad081] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 12/06/2023] [Accepted: 12/14/2023] [Indexed: 03/21/2024]
Abstract
Aims The diagnostic application of artificial intelligence (AI)-based models to detect cardiovascular diseases from electrocardiograms (ECGs) evolves, and promising results were reported. However, external validation is not available for all published algorithms. The aim of this study was to validate an existing algorithm for the detection of left ventricular systolic dysfunction (LVSD) from 12-lead ECGs. Methods and results Patients with digitalized data pairs of 12-lead ECGs and echocardiography (at intervals of ≤7 days) were retrospectively selected from the Heart Center Leipzig ECG and electronic medical records databases. A previously developed AI-based model was applied to ECGs and calculated probabilities for LVSD. The area under the receiver operating characteristic curve (AUROC) was computed overall and in cohorts stratified for baseline and ECG characteristics. Repeated echocardiography studies recorded ≥3 months after index diagnostics were used for follow-up (FU) analysis. At baseline, 42 291 ECG-echocardiography pairs were analysed, and AUROC for LVSD detection was 0.88. Sensitivity and specificity were 82% and 77% for the optimal LVSD probability cut-off based on Youden's J. AUROCs were lower in ECG subgroups with tachycardia, atrial fibrillation, and wide QRS complexes. In patients without LVSD at baseline and available FU, model-generated high probability for LVSD was associated with a four-fold increased risk of developing LVSD during FU. Conclusion We provide the external validation of an existing AI-based ECG-analysing model for the detection of LVSD with robust performance metrics. The association of false positive LVSD screenings at baseline with a deterioration of ventricular function during FU deserves a further evaluation in prospective trials.
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Affiliation(s)
- Sebastian König
- Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
- Helios Health Institute, Real World Evidence & Health Technology Assessment, Schwanebecker Chaussee 50, 13125 Berlin, Germany
| | - Sven Hohenstein
- Helios Health Institute, Real World Evidence & Health Technology Assessment, Schwanebecker Chaussee 50, 13125 Berlin, Germany
| | - Anne Nitsche
- Helios Health Institute, Real World Evidence & Health Technology Assessment, Schwanebecker Chaussee 50, 13125 Berlin, Germany
| | - Vincent Pellissier
- Helios Health Institute, Real World Evidence & Health Technology Assessment, Schwanebecker Chaussee 50, 13125 Berlin, Germany
| | - Johannes Leiner
- Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
- Helios Health Institute, Real World Evidence & Health Technology Assessment, Schwanebecker Chaussee 50, 13125 Berlin, Germany
| | - Lars Stellmacher
- Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
- Helios Health Institute, Real World Evidence & Health Technology Assessment, Schwanebecker Chaussee 50, 13125 Berlin, Germany
| | - Gerhard Hindricks
- Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
| | - Andreas Bollmann
- Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
- Helios Health Institute, Real World Evidence & Health Technology Assessment, Schwanebecker Chaussee 50, 13125 Berlin, Germany
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9
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Kolk MZH, Ruipérez-Campillo S, Alvarez-Florez L, Deb B, Bekkers EJ, Allaart CP, Van Der Lingen ALCJ, Clopton P, Išgum I, Wilde AAM, Knops RE, Narayan SM, Tjong FVY. Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator. EBioMedicine 2024; 99:104937. [PMID: 38118401 PMCID: PMC10772563 DOI: 10.1016/j.ebiom.2023.104937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 10/20/2023] [Accepted: 12/12/2023] [Indexed: 12/22/2023] Open
Abstract
BACKGROUND Risk stratification for ventricular arrhythmias currently relies on static measurements that fail to adequately capture dynamic interactions between arrhythmic substrate and triggers over time. We trained and internally validated a dynamic machine learning (ML) model and neural network that extracted features from longitudinally collected electrocardiograms (ECG), and used these to predict the risk of malignant ventricular arrhythmias. METHODS A multicentre study in patients implanted with an implantable cardioverter-defibrillator (ICD) between 2007 and 2021 in two academic hospitals was performed. Variational autoencoders (VAEs), which combine neural networks with variational inference principles, and can learn patterns and structure in data without explicit labelling, were trained to encode the mean ECG waveforms from the limb leads into 16 variables. Supervised dynamic ML models using these latent ECG representations and clinical baseline information were trained to predict malignant ventricular arrhythmias treated by the ICD. Model performance was evaluated on a hold-out set, using time-dependent receiver operating characteristic (ROC) and calibration curves. FINDINGS 2942 patients (61.7 ± 13.9 years, 25.5% female) were included, with a total of 32,129 ECG recordings during a mean follow-up of 43.9 ± 35.9 months. The mean time-varying area under the ROC curve for the dynamic model was 0.738 ± 0.07, compared to 0.639 ± 0.03 for a static (i.e. baseline-only model). Feature analyses indicated dynamic changes in latent ECG representations, particularly those affecting the T-wave morphology, were of highest importance for model predictions. INTERPRETATION Dynamic ML models and neural networks effectively leverage routinely collected longitudinal ECG recordings for personalised and updated predictions of malignant ventricular arrhythmias, outperforming static models. FUNDING This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).
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Affiliation(s)
- Maarten Z H Kolk
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands
| | - Samuel Ruipérez-Campillo
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA; Department of Information Technology and Electrical Engineering, Swiss Federal Institute of Technology Zurich (ETHz), Gloriastrasse 35, Zurich, Switzerland; ITACA Institute, Universtitat Politècnica de València, Camino de Vera S/n, Valencia, Spain
| | - Laura Alvarez-Florez
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - Brototo Deb
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Erik J Bekkers
- Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam, the Netherlands
| | - Cornelis P Allaart
- Department of Cardiology, Amsterdam UMC, Location VU Medical Center, De Boelelaan 1118, Amsterdam, the Netherlands
| | | | - Paul Clopton
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands; Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - Arthur A M Wilde
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands
| | - Reinoud E Knops
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands
| | - Sanjiv M Narayan
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Fleur V Y Tjong
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands.
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10
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van de Leur RR, van Sleuwen MTGM, Zwetsloot PPM, van der Harst P, Doevendans PA, Hassink RJ, van Es R. Automatic triage of twelve-lead electrocardiograms using deep convolutional neural networks: a first implementation study. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:89-96. [PMID: 38264701 PMCID: PMC10802816 DOI: 10.1093/ehjdh/ztad070] [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: 06/20/2023] [Revised: 10/10/2023] [Accepted: 11/07/2023] [Indexed: 01/25/2024]
Abstract
Aims Expert knowledge to correctly interpret electrocardiograms (ECGs) is not always readily available. An artificial intelligence (AI)-based triage algorithm (DELTAnet), able to support physicians in ECG prioritization, could help reduce current logistic burden of overreading ECGs and improve time to treatment for acute and life-threatening disorders. However, the effect of clinical implementation of such AI algorithms is rarely investigated. Methods and results Adult patients at non-cardiology departments who underwent ECG testing as a part of routine clinical care were included in this prospective cohort study. DELTAnet was used to classify 12-lead ECGs into one of the following triage classes: normal, abnormal not acute, subacute, and acute. Performance was compared with triage classes based on the final clinical diagnosis. Moreover, the associations between predicted classes and clinical outcomes were investigated. A total of 1061 patients and ECGs were included. Performance was good with a mean concordance statistic of 0.96 (95% confidence interval 0.95-0.97) when comparing DELTAnet with the clinical triage classes. Moreover, zero ECGs that required a change in policy or referral to the cardiologist were missed and there was a limited number of cases predicted as acute that did not require follow-up (2.6%). Conclusion This study is the first to prospectively investigate the impact of clinical implementation of an ECG-based AI triage algorithm. It shows that DELTAnet is efficacious and safe to be used in clinical practice for triage of 12-lead ECGs in non-cardiology hospital departments.
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Affiliation(s)
- Rutger R van de Leur
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - Meike T G M van Sleuwen
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - Peter-Paul M Zwetsloot
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - Pim van der Harst
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - Pieter A Doevendans
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
- Central Military Hospital, Utrecht, The Netherlands
| | - Rutger J Hassink
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - René van Es
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
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11
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Wouters PC, van de Leur RR, Vessies MB, van Stipdonk AMW, Ghossein MA, Hassink RJ, Doevendans PA, van der Harst P, Maass AH, Prinzen FW, Vernooy K, Meine M, van Es R. Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy. Eur Heart J 2022; 44:680-692. [PMID: 36342291 PMCID: PMC9940988 DOI: 10.1093/eurheartj/ehac617] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 09/23/2022] [Accepted: 10/13/2022] [Indexed: 11/09/2022] Open
Abstract
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 from 251 473 patients, was used to compress the median beat ECG, thereby summarizing most ECG features into only 21 explainable factors (FactorECG). Pre-implantation ECGs of 1306 CRT patients from three academic centres were converted into their respective FactorECG. FactorECG predicted the combined clinical endpoint of death, left ventricular assist device, or heart transplantation [c-statistic 0.69, 95% confidence interval (CI) 0.66-0.72], significantly outperforming QRSAREA and guideline ECG criteria [c-statistic 0.61 (95% CI 0.58-0.64) and 0.57 (95% CI 0.54-0.60), P < 0.001 for both]. The addition of 13 clinical variables was of limited added value for the FactorECG model when compared with QRSAREA (Δ c-statistic 0.03 vs. 0.10). FactorECG identified inferolateral T-wave inversion, smaller right precordial S- and T-wave amplitude, ventricular rate, and increased PR interval and P-wave duration to be important predictors for poor outcome. An online visualization tool was created to provide interactive visualizations (https://crt.ecgx.ai). CONCLUSION Requiring only a standard 12-lead ECG, FactorECG held superior discriminative ability for the prediction of clinical outcome when compared with guideline criteria and QRSAREA, without requiring additional clinical variables. End-to-end automated visualization of ECG features allows for an explainable algorithm, which may facilitate rapid uptake of this personalized decision-making tool in CRT.
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Affiliation(s)
| | | | - Melle B Vessies
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Antonius M W van Stipdonk
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Centre (MUMC+), Maastricht, The Netherlands
| | - Mohammed A Ghossein
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Rutger J Hassink
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, 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
| | - Pim van der Harst
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Alexander H Maass
- Department of Cardiology, Thoraxcentre, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Frits W Prinzen
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Kevin Vernooy
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Centre (MUMC+), Maastricht, The Netherlands
| | - Mathias Meine
- 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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12
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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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13
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Higaki A, Yamaguchi O. The explainability of the latent variables is limited to the synthesis of electrocardiogram. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 3:500-501. [PMID: 36710890 PMCID: PMC9779878 DOI: 10.1093/ehjdh/ztac052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Akinori Higaki
- Corresponding author. Tel: +81-89-960-5303, Fax: +81-89-960-5306,
| | - Osamu Yamaguchi
- Department of Cardiology, Pulmonology, Hypertension, and Nephrology, Ehime University Graduate School of Medicine, 454 Shitsukawa, Toon, Ehime 791-0295, Japan
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