ArticleScientific reports2023
Opportunistic assessment of ischemic heart disease risk using abdominopelvic computed tomography and medical record data: a multimodal explainable artificial intelligence approach.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- A Narrative Review of Multimodal Data Fusion Strategies for Precision Risk Prediction in Coronary Artery Disease: Advances, Challenges, and Future Informatics Directions.Rambam Maimonides medical journal · 2025Review
- Multimodal Integration in Health Care: Development With Applications in Disease Management.Journal of medical Internet research · 2025Review
- Revolutionizing Utility of Big Data Analytics in Personalized Cardiovascular Healthcare.Bioengineering (Basel, Switzerland) · 2025Review
- FairDiffusion: Enhancing equity in latent diffusion models via fair Bayesian perturbation.Science advances · 2025Article
- Association of CT-Derived Skeletal Muscle and Adipose Tissue Metrics with Frailty in Older Adults.Academic radiology · 2024Article
- Fusion Modeling: Combining Clinical and Imaging Data to Advance Cardiac Care.Circulation. Cardiovascular imaging · 2023Review
- Artificial Intelligence-Based Methods for Precision Cardiovascular Medicine.Journal of personalized medicine · 2023Review
- Predicting 30-Day All-Cause Hospital Readmission Using Multimodal Spatiotemporal Graph Neural Networks.IEEE journal of biomedical and health informatics · 2023Article
- Improving Data-Efficiency and Robustness of Medical Imaging Segmentation Using Inpainting-Based Self-Supervised Learning.Bioengineering (Basel, Switzerland) · 2023Article
- Applications of AI in multi-modal imaging for cardiovascular disease.Frontiers in radiology · 2023Review
- Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations.PloS one · 2023Article
- Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning.Sensors (Basel, Switzerland) · 2022Article
- Use of Multi-Modal Data and Machine Learning to Improve Cardiovascular Disease Care.Frontiers in cardiovascular medicine · 2022Review
- Harnessing Artificial Intelligence in Cardiology: Advancements in Diagnosis, Treatment, and Patient Care.Heart views : the official journal of the Gulf Heart AssociationReview
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Authors and funding
13 authors.
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Abstract
Current risk scores using clinical risk factors for predicting ischemic heart disease (IHD) events-the leading cause of global mortality-have known limitations and may be improved by imaging biomarkers. While body composition (BC) imaging biomarkers derived from abdominopelvic computed tomography (CT) correlate with IHD risk, they are impractical to measure manually. Here, in a retrospective cohort of 8139 contrast-enhanced abdominopelvic CT examinations undergoing up to 5 years of follow-up, we developed multimodal opportunistic risk assessment models for IHD by automatically extracting BC features from abdominal CT images and integrating these with features from each patient's electronic medical record (EMR). Our predictive methods match and, in some cases, outperform clinical risk scores currently used in IHD risk assessment. We provide clinical interpretability of our model using a new method of determining tissue-level contributions from CT along with weightings of EMR features contributing to IHD risk. We conclude that such a multimodal approach, which automatically integrates BC biomarkers and EMR data, can enhance IHD risk assessment and aid primary prevention efforts for IHD. To further promote research, we release the Opportunistic L3 Ischemic heart disease (OL3I) dataset, the first public multimodal dataset for opportunistic CT prediction of IHD.
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