ArticleKorean journal of radiology2021
Fully Automatic Coronary Calcium Score Software Empowered by Artificial Intelligence Technology: Validation Study Using Three CT Cohorts.
Article in Korean journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Fully automated deep learning powered calcium scoring in patients undergoing myocardial perfusion imaging.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Trial
- Clinical Applications of Artificial Intelligence in Cardiac CT: From Coronary CT Angiography to CT-Derived Fractional Flow Reserve and Myocardial Perfusion Imaging.Korean journal of radiology · 2026Review
- Exploring the prevalence and clinical impact of carotid plaque burden by Doppler ultrasound in lung cancer screening participants with limited coronary artery calcification.European radiology · 2026Article
- Deep learning-based automated segmentation and quantification of aortic arch calcification at chest radiograph.BMC geriatrics · 2026Article
- Real-World Implementation of Artificial Intelligence: Fully Automated Coronary Artery Calcium Scoring.Korean journal of radiology · 2026Article
- Consolidation ICIs Alter cardiac subregion radiosensitivity in NSCLC patients treated with Chemo-Radiotherapy.Clinical and translational radiation oncology · 2026Article
- When AI Meets Coronary CT: Overcoming Challenges and Enhancing Accuracy in CAD-RADS Reporting.Korean journal of radiology · 2025Article
- Artificial intelligence in coronary artery calcification scoring: Current progress and future directions.Global cardiology science & practice · 2025Review
- Impact of Deep Learning-Based Image Conversion on Fully Automated Coronary Artery Calcium Scoring Using Thin-Slice, Sharp-Kernel, Non-Gated, Low-Dose Chest CT Scans: A Multi-Center Study.Korean journal of radiology · 2025Article
- Computed Tomography Advancements in Plaque Analysis: From Histology to Comprehensive Plaque Burden Assessment.Echocardiography (Mount Kisco, N.Y.) · 2025Review
- Automated AI-based coronary calcium scoring using retrospective CT data from SCAPIS is accurate and correlates with expert scoring.European radiology · 2025Article
- Coronary artery calcium measurement on attenuation correction computed tomography using artificial intelligence: correlation with coronary flow capacity and prognosis.European journal of nuclear medicine and molecular imaging · 2025Article
- Automatic identification of coronary stent in coronary calcium scoring CT using deep learning.Scientific reports · 2024Article
- Performance assessment of an artificial intelligence-based coronary artery calcium scoring algorithm in non-gated chest CT scans of different slice thickness.Quantitative imaging in medicine and surgery · 2024Article
- Application of Quantitative Assessment of Coronary Atherosclerosis by Coronary Computed Tomographic Angiography.Korean journal of radiology · 2024Review
- Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes.Korean journal of radiology · 2024Article
- Reporting Quality of Research Studies on AI Applications in Medical Images According to the CLAIM Guidelines in a Radiology Journal With a Strong Prominence in Asia.Korean journal of radiology · 2023Review
- Use of Artificial Intelligence in Coronary Artery Calcium Scoring.Oman medical journal · 2023Review
- An automated segmentation of coronary artery calcification using deep learning in specific region limitation.Medical & biological engineering & computing · 2023Article
- Influence of computed tomography slice thickness on deep learning-based, automatic coronary artery calcium scoring software performance.Quantitative imaging in medicine and surgery · 2023Article
Corrections and comments
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Authors and funding
7 authors.
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Abstract
objectiveThis study aimed to validate a deep learning-based fully automatic calcium scoring (coronary artery calcium [CAC]_auto) system using previously published cardiac computed tomography (CT) cohort data with the manually segmented coronary calcium scoring (CAC_hand) system as the reference standard. MATERIALS AND
methodsWe developed the CAC_auto system using 100 co-registered, non-enhanced and contrast-enhanced CT scans. For the validation of the CAC_auto system, three previously published CT cohorts (n = 2985) were chosen to represent different clinical scenarios (i.e., 2647 asymptomatic, 220 symptomatic, 118 valve disease) and four CT models. The performance of the CAC_auto system in detecting coronary calcium was determined. The reliability of the system in measuring the Agatston score as compared with CAC_hand was also evaluated per vessel and per patient using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. The agreement between CAC_auto and CAC_hand based on the cardiovascular risk stratification categories (Agatston score: 0, 1-10, 11-100, 101-400, > 400) was evaluated.
resultsIn 2985 patients, 6218 coronary calcium lesions were identified using CAC_hand. The per-lesion sensitivity and false-positive rate of the CAC_auto system in detecting coronary calcium were 93.3% (5800 of 6218) and 0.11 false-positive lesions per patient, respectively. The CAC_auto system, in measuring the Agatston score, yielded ICCs of 0.99 for all the vessels (left main 0.91, left anterior descending 0.99, left circumflex 0.96, right coronary 0.99). The limits of agreement between CAC_auto and CAC_hand were 1.6 ± 52.2. The linearly weighted kappa value for the Agatston score categorization was 0.94. The main causes of false-positive results were image noise (29.1%, 97/333 lesions), aortic wall calcification (25.5%, 85/333 lesions), and pericardial calcification (24.3%, 81/333 lesions).
conclusionThe atlas-based CAC_auto empowered by deep learning provided accurate calcium score measurement as compared with manual method and risk category classification, which could potentially streamline CAC imaging workflows.
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