AI-Defined Cardiac Anatomy Improves Risk Stratification of Hybrid Perfusion Imaging
Open accessAbstract
Background Computed tomography attenuation correction (CTAC) improves perfusion quantification of hybrid myocardial perfusion imaging by correcting for attenuation artifacts. Artificial intelligence (AI) can automatically measure coronary artery calcium (CAC) from CTAC to improve risk prediction but could potentially derive additional anatomic features. Objectives The authors evaluated AI-based derivation of cardiac anatomy from CTAC and assessed its added prognostic utility. Methods The authors considered consecutive patients without known coronary artery disease who underwent single-photon emission computed tomography/computed tomography (CT) myocardial perfusion imaging at 3 separate centers. Previously validated AI models were used to segment CAC and cardiac structures (left atrium, left ventricle, right atrium, right ventricular volume, and left ventricular [LV] mass) from CTAC. They evaluated associations with major adverse cardiovascular events (MACEs), which included death, myocardial infarction, unstable angina, or revascularization. Results In total, 7,613 patients were included with a median age of 64 years. During a median follow-up of 2.4 years (IQR: 1.3-3.4 years), MACEs occurred in 1,045 (13.7%) patients. Fully automated AI processing took an average of 6.2 ± 0.2 seconds for CAC and 15.8 ± 3.2 seconds for cardiac volumes and LV mass. Patients in the highest quartile of LV mass and left atrium, LV, right atrium, and right ventricular volume were at significantly increased risk of MACEs compared to patients in the lowest quartile, with HR ranging from 1.46 to 3.31. The addition of all CT-based volumes and CT-based LV mass improved the continuous net reclassification index by 23.1%. Conclusions AI can automatically derive LV mass and cardiac chamber volumes from CT attenuation imaging, significantly improving cardiovascular risk assessment for hybrid perfusion imaging.
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One framework referenced the PRIME checklist for cardiovascular imaging AI as an emerging reporting standard, reflecting increasing awareness of the need for standardised evaluation and transparent reporting.
“One framework referenced the Proposed Requirements for Cardiovascular Imaging–Related Machine Learning Evaluation (PRIME) checklist for cardiovascular imaging AI as an emerging reporting standard, reflecting increasing awareness of the need for standardised evaluation and transparent reporting.47”
From Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review (2026) · cites this paper as [47]
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- Authors
- Miller, R. J. H., Shanbhag, A., Killekar, A., Lemley, M., Bednarski, B., Kavanagh, P. B., Feher, A., Miller, E. J., Bateman, T., Builoff, V., Liang, J. X., Newby, D. E., Dey, D., Berman, D. S., & Slomka, P. J.
- Affiliations
- Cedars-Sinai Medical Center (US)
- University of Southern California (US)
- Yale University (US)
- British Heart Foundation Centre for Cardiovascular Science (GB)
- Corresponding author
- Miller, Robert J.H.
- Shanbhag, Aakash
- Slomka, Piotr J.
- ORCID
- Venue
- JACC: Cardiovascular Imaging
- Publisher
- Elsevier BV
- Published
- 2024-07
- Volume
- 17
- Issue
- 7
- Pages
- 780-791
- ISSN
- 1876-7591 (ISSN-L)
- 1936-878X
- PMC
- PMC11222053
- OpenAlex
- W4392503039
- Cited by
- 33
- References
- 35
- Citations by year
- 2026: 4 · 2025: 18 · 2024: 11
- FWCI
- 8.41
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- JACC. Cardiovascular imaging (publishedVersion)
- PubMed (publishedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart Lung and Blood Institute
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence, CT attenuation correction, myocardial perfusion imaging, coronary artery calcium, left ventricular mass, cardiac chamber volumes, major adverse cardiovascular events, risk stratification, hybrid SPECT/CT
- MeSH terms
- Single Photon Emission Computed Tomography Computed Tomography, Computed Tomography Angiography, Aged, Artificial Intelligence, Coronary Artery Disease, Coronary Circulation, Coronary Vessels, Female, Humans, Male, Middle Aged, Predictive Value of Tests, Prognosis, Radiographic Image Interpretation, Computer-Assisted, Retrospective Studies, Risk Factors, Time Factors, Reproducibility of Results, Coronary Angiography, Risk Assessment, Myocardial Perfusion Imaging, Vascular Calcification
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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