Multimodal explainable artificial intelligence identifies patients with non-ischaemic cardiomyopathy at risk of lethal ventricular arrhythmias
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The efficacy of an implantable cardioverter-defibrillator (ICD) in patients with a non-ischaemic cardiomyopathy for primary prevention of sudden cardiac death is increasingly debated. We developed a multimodal deep learning model for arrhythmic risk prediction that integrated late gadolinium enhanced (LGE) cardiac magnetic resonance imaging (MRI), electrocardiography (ECG) and clinical data. Short-axis LGE-MRI scans and 12-lead ECGs were retrospectively collected from a cohort of 289 patients prior to ICD implantation, across two tertiary hospitals. A residual variational autoencoder was developed to extract physiological features from LGE-MRI and ECG, and used as inputs for a machine learning model (DEEP RISK) to predict malignant ventricular arrhythmia onset. In the validation cohort, the multimodal DEEP RISK model predicted malignant ventricular arrhythmias with an area under the receiver operating characteristic curve (AUROC) of 0.84 (95% confidence interval (CI) 0.71-0.96), a sensitivity of 0.98 (95% CI 0.75-1.00) and a specificity of 0.73 (95% CI 0.58-0.97). The models trained on individual modalities exhibited lower AUROC values compared to DEEP RISK [MRI branch: 0.80 (95% CI 0.65-0.94), ECG branch: 0.54 (95% CI 0.26-0.82), Clinical branch: 0.64 (95% CI 0.39-0.87)]. These results suggest that a multimodal model achieves high prognostic accuracy in predicting ventricular arrhythmias in a cohort of patients with non-ischaemic systolic heart failure, using data collected prior to ICD implantation.
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Some papers studied in this review attempt to incorporate unimodal explainable AI methods, like Shapley values and Grad-CAM.
“Some papers studied in this review indeed attempt to incorporate (unimodal) explainable AI methods, like Shapley values (Kolk et al., 2024; Sangnark et al., 2024; Teuho et al., 2024; Xu et al., 2022a; Parvin et al., 2024) and Grad-CAM (Sangnark et al., 2024; Teuho et al., 2024).”
From Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications (2025) · cites this paper as Kolk et al., 2024
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Multimodal Classification for Multi-Task Medical DatasetsPreprint
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Empowering Heart Failure Management Through Artificial Intelligence
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DIRBTINIS INTELEKTAS KARDIOLOGIJOJE-ARITMOLOGIJOJE
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Artificial Intelligence in Cardiomyopathies
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- Authors
- Kolk, M. Z. H., Ruipérez-Campillo, S., Allaart, C. P., Wilde, A. A. M., Knops, R. E., Narayan, S. M., Tjong, F. V. Y., DEEP RISK investigators, Raijmakers, F. D., Van Der Lingen, A.-L. C. J., Götte, M. J. W., Selder, J. L., Alvarez-Florez, L., Išgum, I., & Bekkers, E. J.
- Affiliations
- Amsterdam UMC Location University of Amsterdam (NL)
- ETH Zurich (CH)
- Amsterdam University Medical Centers (NL)
- Stanford Cardiovascular Institute (US)
- Amsterdam Cardiovascular Sciences (NL)
- University of Amsterdam (NL)
- Corresponding author
- Tjong, Fleur V. Y.
- ORCID
- Venue
- Scientific Reports
- Published
- 2024-06-27
- Volume
- 14
- Issue
- 1
- Pages
- 14889-14889
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC11211323
- OpenAlex
- W4400067888
- Cited by
- 49
- References
- 51
- Citations by year
- 2026: 22 · 2025: 25 · 2024: 2
- FWCI
- 14.68
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion, other-oa)
- UvA-DARE (University of Amsterdam) (submittedVersion, cc-by)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Pure Amsterdam UMC (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Funders
- Dutch Research Council (452019308)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac pacing and defibrillation studies
- Keywords
- non-ischemic cardiomyopathy, sudden cardiac death, implantable cardioverter-defibrillator, late gadolinium enhancement, cardiac magnetic resonance imaging, electrocardiogram, multimodal deep learning, explainable AI, primary prevention, heart failure with reduced ejection fraction
- MeSH terms
- Deep Learning, Aged, Arrhythmias, Cardiac, Artificial Intelligence, Electrocardiography, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Cardiomyopathies, Retrospective Studies, Risk Factors, ROC Curve, Death, Sudden, Cardiac, Defibrillators, Implantable, Risk Assessment
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