ArticleJACC. Cardiovascular imaging2024
AI-Defined Cardiac Anatomy Improves Risk Stratification of Hybrid Perfusion Imaging.
Article in JACC. Cardiovascular imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Revisiting Perfusion in the Forgotten Right Ventricle: Artificial Intelligence-Enhanced Quantification on PET/CT.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- Opportunistic CT Attenuation Biomarkers of Anemia Are Associated With Impaired Myocardial Flow Reserve and Cardiovascular Outcomes.medRxiv : the preprint server for health sciences · 2026Article
- Impact of disease-modifying therapy on [European journal of nuclear medicine and molecular imaging · 2026Article
- Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review.BMJ digital health & AI · 2026Article
- Ejection fraction quantification from ungated chest CT by AI.medRxiv : the preprint server for health sciences · 2025Article
- The REgistry of Flow and Perfusion Imaging for Artificial Intelligence with positron emission tomography (REFINE PET):Rationale and design.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2025Article
- Deep Learning-Derived Cardiac Chamber Volumes and Mass From PET/CT Attenuation Scans: Associations With Myocardial Flow Reserve and Heart Failure.Circulation. Cardiovascular imaging · 2025Article
- AI-enabled CT-guided end-to-end quantification of total cardiac activity in 18FDG cardiac PET/CT for detection of cardiac sarcoidosis.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2025Article
- Artificial Intelligence in Nuclear Cardiac Imaging: Novel Advances, Emerging Techniques, and Recent Clinical Trials.Journal of clinical medicine · 2025Review
- Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction.NPJ digital medicine · 2025Article
- Automated proximal coronary artery calcium identification using artificial intelligence: advancing cardiovascular risk assessment.European heart journal. Cardiovascular Imaging · 2025Article
- Cardiac Metastasis Presenting With ST-Segment Elevation: Correlation With Multimodality Imaging.JACC. Case reports · 2025Article
- The Accuracy of Technetium-99 m Pyrophosphate Imaging in Diagnosing Transthyretin Cardiac Amyloidosis and Its Impact on Patient Management.Current cardiology reports · 2025Review
- Artificial intelligence applied to coronary artery calcium scans (AI-CAC) significantly improves cardiovascular events prediction.NPJ digital medicine · 2024Article
- The Updated Registry of Fast Myocardial Perfusion Imaging with Next-Generation SPECT (REFINE SPECT 2.0).Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- AI-enabled CT-guided end-to-end quantification of total cardiac activity in 18FDG cardiac PET/CT for detection of cardiac sarcoidosis.medRxiv : the preprint server for health sciences · 2024Article
- Deep Learning-Enabled Quantification ofJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Article
- Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction.medRxiv : the preprint server for health sciences · 2024Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
backgroundComputed 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.
objectivesThe authors evaluated AI-based derivation of cardiac anatomy from CTAC and assessed its added prognostic utility.
methodsThe 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.
resultsIn 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%.
conclusionsAI 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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