ArticleProceedings of SPIE--the International Society for Optical Engineering2018
Automated Agatston Score Computation in non-ECG Gated CT Scans Using Deep Learning.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Development and application of artificial intelligence in cardiac imaging.The British journal of radiology · 2020Pooled it
- DINO-LG: Enhancing vision transformers with label guidance for coronary artery calcium detection.Medical & biological engineering & computing · 2026Article
- Automated opportunistic cardiovascular risk assessment in non-small cell lung cancer patients on routine chest CT using an optimised nnU-net framework.BMC medical imaging · 2026Article
- Artificial Intelligence in Biomedicine: A Systematic Review from Nanomedicine to Neurology and Hepatology.Pharmaceutics · 2025Review
- Early detection of cardiovascular disease in chest population screening: challenges for a rapidly emerging cardiac CT application.The British journal of radiology · 2025Review
- Automatic joint inflammation estimation based on regression neural networks.Medical physics · 2025Article
- Article
- Review
- 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
- Explainable fully automated CT scoring of interstitial lung disease for patients suspected of systemic sclerosis by cascaded regression neural networks and its comparison with experts.Scientific reports · 2024Article
- Automated Plaque Detection and Agatston Score Estimation on Non-Contrast CT Scans: A Multicenter Study.ArXiv · 2024Article
- Artificial Intelligence in Coronary Artery Calcium Scoring Detection and Quantification.Diagnostics (Basel, Switzerland) · 2024Review
- Role of Deep Learning in Computed Tomography.Cureus · 2023Review
- Artificial Intelligence in Cardiovascular CT and MR Imaging.Life (Basel, Switzerland) · 2023Review
- Role and progress of artificial intelligence in radiodiagnosing vascular calcification: a narrative review.Annals of translational medicine · 2023Review
- Generative models for reproducible coronary calcium scoring.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Artificial Intelligence in Cardiology-A Narrative Review of Current Status.Journal of clinical medicine · 2022Review
- Artificial intelligence and machine learning in precision and genomic medicine.Medical oncology (Northwood, London, England) · 2022Review
- Automatic coronary artery calcium scoring on routine chest computed tomography (CT): comparison of a deep learning algorithm and a dedicated calcium scoring CT.Quantitative imaging in medicine and surgery · 2022Article
- Fully Automatic Coronary Calcium Score Software Empowered by Artificial Intelligence Technology: Validation Study Using Three CT Cohorts.Korean journal of radiology · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
introductionThe Agatston score is a well-established metric of cardiovascular disease related to clinical outcomes. It is computed from CT scans by a) measuring the volume and intensity of the atherosclerotic plaques and b) aggregating such information in an index.
objectiveTo generate a convolutional neural network that inputs a non-contrast chest CT scan and outputs the Agatston score associated with it directly, without a prior segmentation of Coronary Artery Calcifications (CAC). MATERIALS AND
methodsWe use a database of 5973 non-contrast non-ECG gated chest CT scans where the Agatston score has been manually computed. The heart of each scan is cropped automatically using an object detector. The database is split in 4973 cases for training and 1000 for testing. We train a 3D deep convolutional neural network to regress the Agatston score directly from the extracted hearts.
resultsThe proposed method yields a Pearson correlation coefficient of
conclusionsA convolutional neural network can regress the Agatston score from the image of the heart directly, without a prior segmentation of the CACs. This is a new and simpler paradigm in the Agatston score computation that yields similar results to the state-of-the-art literature.
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