ArticleInsights into imaging2024
Automated segment-level coronary artery calcium scoring on non-contrast CT: a multi-task deep-learning approach.
Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02400229 (Diagnostic Imaging Strategies for Patients With Stable Chest Pain and Intermediate Risk of Coronary Artery Disease), which is not on this map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Diagnostic Imaging Strategies for Patients With Stable Chest Pain and Intermediate Risk of Coronary Artery Disease: Comparative Effectiveness Research of Existing Technologies) - A Pragmatic Randomised Controlled Trial of CT Versus ICA
Who cites it
6 citing papers in PubMed.
- Association between deep learning-based coronary artery calcium score on non-gated chest CT and progression of chronic kidney disease: a retrospective observational cohort study.BMC medical imaging · 2026Observational
- A Novel Fully Automated Deep Learning Model for Coronary Artery Calcification Detection on Computed Tomography.Diagnostics (Basel, Switzerland) · 2026Article
- Coronary CT angiography evaluation with artificial intelligence for individualized medical treatment of atherosclerosis: a Consensus Statement from the QCI Study Group.Nature reviews. Cardiology · 2026Review
- Opportunistic automated aortic valve calcification assessment in low-cost, screening CT calcium score exams.Scientific reports · 2026Article
- Artificial intelligence in coronary artery calcification scoring: Current progress and future directions.Global cardiology science & practice · 2025Review
- Fully automated coronary artery calcium score and risk categorization from chest CT using deep learning and multiorgan segmentation: A validation study from National Lung Screening Trial (NLST).International journal of cardiology. Heart & vasculature · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
objectivesTo develop and evaluate a multi-task deep-learning (DL) model for automated segment-level coronary artery calcium (CAC) scoring on non-contrast computed tomography (CT) for precise localization and quantification of calcifications in the coronary artery tree.
methodsThis study included 1514 patients (mean age, 60.0 ± 10.2 years; 56.0% female) with stable chest pain from 26 centers participating in the multicenter DISCHARGE trial (NCT02400229). The patients were randomly assigned to a training/validation set (1059) and a test set (455). We developed a multi-task neural network for performing the segmentation of calcifications on the segment level as the main task and the segmentation of coronary artery segment regions with weak annotations as an auxiliary task. Model performance was evaluated using (micro-average) sensitivity, specificity, F1-score, and weighted Cohen's κ for segment-level agreement based on the Agatston score and performing interobserver variability analysis.
resultsIn the test set of 455 patients with 1797 calcifications, the model assigned 73.2% (1316/1797) to the correct coronary artery segment. The model achieved a micro-average sensitivity of 0.732 (95% CI: 0.710-0.754), a micro-average specificity of 0.978 (95% CI: 0.976-0.980), and a micro-average F1-score of 0.717 (95% CI: 0.695-0.739). The segment-level agreement was good with a weighted Cohen's κ of 0.808 (95% CI: 0.790-0.824), which was only slightly lower than the agreement between the first and second observer (0.809 (95% CI: 0.798-0.845)).
conclusionAutomated segment-level CAC scoring using a multi-task neural network approach showed good agreement on the segment level, indicating that DL has the potential for automated coronary artery calcification classification. CRITICAL RELEVANCE STATEMENT: Multi-task deep learning can perform automated coronary calcium scoring on the segment level with good agreement and may contribute to the development of new and improved calcium scoring methods. KEY POINTS: Segment-level coronary artery calcium scoring is a tedious and error-prone task. The proposed multi-task model achieved good agreement with a human observer on the segment level. Deep learning can contribute to the automation of segment-level coronary artery calcium scoring.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.