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Peer-reviewedCC BY-NC-NDAnnual Review of Biomedical Data Science

Artificial Intelligence in Image-Based Cardiovascular Disease Analysis

  • Xin Wang1
  • Mingcheng Hu2
  • Connie W. Tsao3
  • Hongtu Zhu B2
  1. 11Department of Epidemiology and Biostatistics, College of Integrated Health Sciences and AI Plus Institute, University at Albany, SUNY, Albany, New York, USA; email: [email protected]
  2. 22Department of Biostatistics and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina, USA; email: [email protected]
  3. 33Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA; email: [email protected]
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Abstract

Recent advancements in artificial intelligence (AI) have significantly influenced the field of cardiovascular disease (CVD) analysis, particularly in image-based diagnostics. Our article presents an extensive review of AI applications in image-based CVD analysis, offering insights into its current state and future potential. We systematically categorize the literature based on the primary anatomical structures related to CVD, dividing them into nonvessel structures (such as ventricles and atria) and vessel structures (including the aorta and coronary arteries). This categorization provides a structured approach to explore various imaging modalities like computed tomography and magnetic resonance imaging, which are commonly used in CVD research. Our review encompasses these modalities, giving a broad perspective on the diverse imaging techniques integrated with AI for CVD analysis. We conclude with an examination of the challenges and limitations inherent in current AI-based CVD analysis methods and suggest directions for future research to overcome these hurdles.

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Cite

BibTeXRIS
BibTeX
@article{wang2026artificial,
  title = {{Artificial Intelligence in Image-Based Cardiovascular Disease Analysis}},
  author = {Xin Wang and Mingcheng Hu and Connie W. Tsao and Hongtu Zhu B},
  journal = {Annual Review of Biomedical Data Science},
  year = {2026},
  month = may,
  doi = {10.1146/annurev-biodatasci-092624-111837},
  url = {https://doi.org/10.1146/annurev-biodatasci-092624-111837},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Artificial Intelligence in Image-Based Cardiovascular Disease Analysis
AU  - Xin Wang
AU  - Mingcheng Hu
AU  - Connie W. Tsao
AU  - Hongtu Zhu B
PY  - 2026
DA  - 2026/05/28
JO  - Annual Review of Biomedical Data Science
DO  - 10.1146/annurev-biodatasci-092624-111837
UR  - https://doi.org/10.1146/annurev-biodatasci-092624-111837
AB  - Recent advancements in artificial intelligence (AI) have significantly influenced the field of cardiovascular disease (CVD) analysis, particularly in image-based diagnostics. Our article presents an extensive review of AI applications in image-based CVD analysis, offering insights into its current state and future potential. We systematically categorize the literature based on the primary anatomical structures related to CVD, dividing them into nonvessel structures (such as ventricles and atria) and vessel structures (including the aorta and coronary arteries). This categorization provides a structured approach to explore various imaging modalities like computed tomography and magnetic resonance imaging, which are commonly used in CVD research. Our review encompasses these modalities, giving a broad perspective on the diverse imaging techniques integrated with AI for CVD analysis. We conclude with an examination of the challenges and limitations inherent in current AI-based CVD analysis methods and suggest directions for future research to overcome these hurdles.
SN  - 2574-3414
ER  -