Trial reportJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2023
Fully automated deep learning powered calcium scoring in patients undergoing myocardial perfusion imaging.
Trial report in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03637231 (Ultra-low-dose Coronary Artery Calcium Scoring), which is not on this map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Ultra-low-dose Coronary Artery Calcium Scoring: Evaluation of Prognostic Performance and Impact of Physiological Factors on Quantification in a Large Population
Who cites it
10 citing papers in PubMed, 14 citations in OpenAlex.
- DINO-LG: Enhancing vision transformers with label guidance for coronary artery calcium detection.Medical & biological engineering & computing · 2026Article
- Prospective Human Validation of Artificial Intelligence Interventions in Cardiology: A Scoping Review.JACC. Advances · 2024Article
- Automated vessel-specific coronary artery calcification quantification with deep learning in a large multi-centre registry.European heart journal. Cardiovascular Imaging · 2024Article
- Artificial intelligence in coronary artery calcium score: rationale, different approaches, and outcomes.The international journal of cardiovascular imaging · 2024Review
- Fully automated coronary artery calcium quantification on electrocardiogram-gated non-contrast cardiac computed tomography using deep-learning with novel Heart-labelling method.European heart journal open · 2023Article
- Deep Learning Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2023Article
- Opportunistic deep learning powered calcium scoring in oncologic patients with very high coronary artery calcium (≥ 1000) undergoing 18F-FDG PET/CT.Scientific reports · 2022Article
- Diagnostic Value of Fully Automated Artificial Intelligence Powered Coronary Artery Calcium Scoring from 18F-FDG PET/CT.Diagnostics (Basel, Switzerland) · 2022Article
- Artificial intelligence for disease diagnosis and risk prediction in nuclear cardiology.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022Review
- Validation of deep learning-based fully automated coronary artery calcium scoring using non-ECG-gated chest CT in patients with cancer.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo assess the accuracy of fully automated deep learning (DL) based coronary artery calcium scoring (CACS) from non-contrast computed tomography (CT) as acquired for attenuation correction (AC) of cardiac single-photon-emission computed tomography myocardial perfusion imaging (SPECT-MPI). METHODS AND
resultsPatients were enrolled in this study as part of a larger prospective study (NCT03637231). In this study, 56 Patients who underwent cardiac SPECT-MPI due to suspected coronary artery disease (CAD) were prospectively enrolled. All patients underwent non-contrast CT for AC of SPECT-MPI twice. CACS was manually assessed (serving as standard of reference) on both CT datasets (n = 112) and by a cloud-based DL tool. The agreement in CAC scores and CAC score risk categories was quantified. For the 112 scans included in the analysis, interscore agreement between the CAC scores of the standard of reference and the DL tool was 0.986. The agreement in risk categories was 0.977 with a reclassification rate of 3.6%. Heart rate, image noise, body mass index (BMI), and scan did not significantly impact (p=0.09 - p=0.76) absolute percentage difference in CAC scores.
conclusionA DL tool enables a fully automated and accurate estimation of CAC scores in patients undergoing non-contrast CT for AC of SPECT-MPI.
Indexed as
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.