ArticleJournal of cardiovascular development and disease2023
Deep Learning-Based Automated Quantification of Coronary Artery Calcification for Contrast-Enhanced Coronary Computed Tomographic Angiography.
Article in Journal of cardiovascular development and disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A deep learning-based algorithm for detecting significant in-stent restenosis in coronary CT angiography.The international journal of cardiovascular imaging · 2026Article
- A novel hybrid segmentation method coupled with deep learning for coronary artery extraction from coronary CT angiography.The international journal of cardiovascular imaging · 2026Article
- Artificial Intelligence in Cardiovascular Imaging: From Automated Acquisition to Precision Diagnostics and Clinical Decision Support.Medical sciences (Basel, Switzerland) · 2026Review
- Deep learning based automatic quantification of aortic valve calcification on contrast enhanced coronary CT angiography.Scientific reports · 2025Article
- Decalcify cardiac CT: unveiling clearer images with deep convolutional neural networks.Frontiers in medicine · 2025Article
- A comparative analysis of deep learning-based location-adaptive threshold method software against other commercially available software.The international journal of cardiovascular imaging · 2024Article
- Cardiac Allograft Vasculopathy: Challenges and Advances in Invasive and Non-Invasive Diagnostic Modalities.Journal of cardiovascular development and disease · 2024Review
- The Application of Deep Learning for the Segmentation and Classification of Coronary Arteries.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundWe evaluated the accuracy of a deep learning-based automated quantification algorithm for coronary artery calcium (CAC) based on enhanced ECG-gated coronary CT angiography (CCTA) with dedicated coronary calcium scoring CT (CSCT) as the reference.
methodsThis retrospective study included 315 patients who underwent CSCT and CCTA on the same day, with 200 in the internal and 115 in the external validation sets. The calcium volume and Agatston scores were calculated using both the automated algorithm in CCTA and the conventional method in CSCT. The time required for computing calcium scores using the automated algorithm was also evaluated.
resultsOur automated algorithm extracted CACs in less than five minutes on average with a failure rate of 1.3%. The volume and Agatston scores by the model showed high agreement with those from CSCT with concordance correlation coefficients of 0.90-0.97 for the internal and 0.76-0.94 for the external. The accuracy for classification was 92% with a 0.94 weighted kappa for the internal and 86% with a 0.91 weighted kappa for the external set.
conclusionsThe deep learning-based and fully automated algorithm efficiently extracted CACs from CCTA and reliably assigned categorical classification for Agatston scores without additional radiation exposure.
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