ArticleEuropean journal of radiology2021
Fully automatic framework for comprehensive coronary artery calcium scores analysis on non-contrast cardiac-gated CT scan: Total and vessel-specific quantifications.
Article in European journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- An Automated Machine Learning Based Framework for Scan Level Coronary Artery Calcium Risk Stratification.Journal of imaging informatics in medicine · 2026Article
- AI-driven radiomics in vascular disease: a narrative review of image analysis and clinical translation.Cardiovascular diagnosis and therapy · 2026Review
- Vascular calcifications in early-breast radiotherapy planning-CT: Opportunistic detection and cardiovascular risk assessment.Clinical and translational radiation oncology · 2026Article
- Chest Computed Tomography-Based Radiomics for the Diagnosis and Prognosis of Pulmonary Hypertension.Journal of the American Heart Association · 2025Article
- Automated AI-based coronary calcium scoring using retrospective CT data from SCAPIS is accurate and correlates with expert scoring.European radiology · 2025Article
- Artificial Intelligence in Ischemic Heart Disease Prevention.Current cardiology reports · 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
- Artificial intelligence-derived coronary artery calcium scoring saves time and achieves close to radiologist-level accuracy accuracy on routine ECG-gated CT.The international journal of cardiovascular imaging · 2025Article
- Correlation Between Coronary Arterial Dominance and the Degree of Coronary Artery Disease Using Computed Tomography Angiography.Journal of multidisciplinary healthcare · 2025Article
- Performance of artificial intelligence in detecting the chronic total occlusive lesions of coronary artery based on coronary computed tomographic angiography.Cardiovascular diagnosis and therapy · 2024Article
- Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?Current atherosclerosis reports · 2024Review
- Artificial intelligence in coronary artery calcium score: rationale, different approaches, and outcomes.The international journal of cardiovascular imaging · 2024Review
- Artificial Intelligence in Coronary Artery Calcium Scoring Detection and Quantification.Diagnostics (Basel, Switzerland) · 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
- The Value of Coronary Computed Tomography Angiography in Patients with a High Calcium Score.The journal of Tehran Heart Center · 2023Article
- Role and progress of artificial intelligence in radiodiagnosing vascular calcification: a narrative review.Annals of translational medicine · 2023Review
- A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges.Current neuropharmacology · 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
- Automated coronary artery calcium scoring using nested U-Net and focal loss.Computational and structural biotechnology journal · 2022Article
- Automatic COVID-19 Lung Infection Segmentation through Modified Unet Model.Journal of healthcare engineering · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectivesTo develop a fully automatic multiview shape constraint framework for comprehensive coronary artery calcium scores (CACS) quantification via deep learning on nonenhanced cardiac CT images.
methodsIn this retrospective single-centre study, a multi-task deep learning framework was proposed to detect and quantify coronary artery calcification from CT images collected between October 2018 and March 2019. A total of 232 non-contrast cardiac-gated CT scans were retrieved and studied (80 % for model training and 20 % for testing). CACS results of testing datasets (n = 46), including Agatston score, calcium volume score, calcium mass score, were calculated fully automatically and manually at total and vessel-specific levels, respectively.
resultsNo significant differences were found in CACS quantification obtained using automatic or manual methods at total and vessel-specific levels (Agatston score: automatic 535.3 vs. manual 542.0, P = 0.993; calcium volume score: automatic 454.2 vs. manual 460.6, P = 0.990; calcium mass score: automatic 128.9 vs. manual 129.5, P = 0.992). Compared to the ground truth, the number of calcified vessels can be accurate recognized automatically (total: automatic 107 vs. manual 102, P = 0.125; left main artery: automatic 15 vs. manual 14, P = 1.000 ; left ascending artery: automatic 37 vs. manual 37, P = 1.000; left circumflex artery: automatic 22 vs. manual 20, P = 0.625; right coronary artery: automatic 33 vs. manual 31, P = 0.500). At the patient's level, there was no statistic difference existed in the classification of Agatston scoring (P = 0.317) and the number of calcified vessels (P = 0.102) between the automatic and manual results.
conclusionsThe proposed framework can achieve reliable and comprehensive quantification for the CACS, including the calcified extent and distribution indicators at both total and vessel-specific levels.
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