The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
Deep learning for cardiovascular medicine: a practical primer
Open accessAbstract
Deep learning (DL) is a branch of machine learning (ML) showing increasing promise in medicine, to assist in data classification, novel disease phenotyping and complex decision making. Deep learning is a form of ML typically implemented via multi-layered neural networks. Deep learning has accelerated by recent advances in computer hardware and algorithms and is increasingly applied in e-commerce, finance, and voice and image recognition to learn and classify complex datasets. The current medical literature shows both strengths and limitations of DL. Strengths of DL include its ability to automate medical image interpretation, enhance clinical decision-making, identify novel phenotypes, and select better treatment pathways in complex diseases. Deep learning may be well-suited to cardiovascular medicine in which haemodynamic and electrophysiological indices are increasingly captured on a continuous basis by wearable devices as well as image segmentation in cardiac imaging. However, DL also has significant weaknesses including difficulties in interpreting its models (the ‘black-box’ criticism), its need for extensive adjudicated (‘labelled’) data in training, lack of standardization in design, lack of data-efficiency in training, limited applicability to clinical trials, and other factors. Thus, the optimal clinical application of DL requires careful formulation of solvable problems, selection of most appropriate DL algorithms and data, and balanced interpretation of results. This review synthesizes the current state of DL for cardiovascular clinicians and investigators, and provides technical context to appreciate the promise, pitfalls, near-term challenges, and opportunities for this exciting new area.
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Cites
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Cited by
Opening the black box: the promise and limitations of explainable machine learning in cardiology
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Machine learning and deep learning approach for medical image analysis: diagnosis to detection
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Review of multimodal machine learning approaches in healthcare
Krones, F., Marikkar, U., Parsons, G., Szmul, A., & Mahdi, A. (2025) · Information Fusion · doi:10.1016/j.inffus.2024.102690 · Cited by 235
Integrating blockchain technology with artificial intelligence for cardiovascular medicine
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Self-citationLee, S., Chu, Y., Ryu, J., Park, Y. J., Yang, S., & Koh, S. B. (2022) · Yonsei Medical Journal · doi:10.3349/ymj.2022.63.s93 · Cited by 88
Metadata
- Authors
- Krittanawong, C., Johnson, K. W., Rosenson, R. S., Wang, Z., Aydar, M., Baber, U., Min, J. K., Tang, W. H. W., Halperin, J. L., & Narayan, S. M.
- Venue
- European Heart Journal
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-02-27
- Volume
- 40
- Issue
- 25
- Pages
- 2058-2073
- ISSN
- 0195-668X (ISSN-L)
- 1522-9645
- OpenAlex
- W2920679031
- Cited by
- 435
- References
- 76
- FWCI
- 10.17
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Krittanawong, C., Johnson, K. W., Rosenson, R. S., Wang, Z., Aydar, M., Baber, U., Min, J. K., Tang, W. H. W., Halperin, J. L., & Narayan, S. M. (2019). Deep learning for cardiovascular medicine: a practical primer. European Heart Journal. https://doi.org/10.1093/eurheartj/ehz056
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