ArticleJournal of atherosclerosis and thrombosis2021
Development of Novel Artificial Intelligence to Detect the Presence of Clinically Meaningful Coronary Atherosclerotic Stenosis in Major Branch from Coronary Angiography Video.
Article in Journal of atherosclerosis and thrombosis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Accuracy of deep learning in the differential diagnosis of coronary artery stenosis: a systematic review and meta-analysis.BMC medical imaging · 2024Pooled it
- Cardiologist user experience of artificial intelligence-based quantitative coronary angiography.Cardiovascular diagnosis and therapy · 2025Article
- Deep Learning-Based Detection of Malignant Bile Duct Stenosis in Fluoroscopy Images of Endoscopic Retrograde Cholangiopancreatography.Digestion · 2025Article
- Spontaneous coronary artery dissection: a clinically oriented narrative review.NPJ cardiovascular health · 2024Review
- Contemporary Functional Coronary Angiography: An Update.Future cardiology · 2024Review
- Detection of the Relationship between the Multi-Dimensional Data Sets of Serially Measured Blood Pressure and the Future Risk of Death in Healthy Elderly Japanese Population.Journal of atherosclerosis and thrombosis · 2023Article
- Deep learning applications in coronary anatomy imaging: a systematic review and meta-analysis.Journal of medical artificial intelligence · 2022Article
- Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease.Current cardiology reports · 2022Review
- Article
- Artificial Intelligence-A Good Assistant to Multi-Modality Imaging in Managing Acute Coronary Syndrome.Frontiers in cardiovascular medicine · 2021Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimThe clinically meaningful coronary stenosis is diagnosed by trained interventional cardiologists. Whether artificial intelligence (AI) could detect coronary stenosis from CAG video is unclear.
methodsThe 199 consecutive patients who underwent coronary arteriography (CAG) with chest pain between December 2018 and May 2019 was enrolled. Each patient underwent CAG with multiple view resulting in total numbers of 1,838 videos. A multi-layer 3-dimensional convolution neural network (CNN) was trained as an AI to detect clinically meaningful coronary artery stenosis diagnosed by the expert interventional cardiologist, using data from 146 patients (resulted in 1,359 videos) randomly selected from the entire dataset (training dataset). This training dataset was further split into 109 patients (989 videos) for derivation and 37 patients (370 videos) for validation. The AI developed in derivation cohort was tuned in validation cohort to make final model.
resultsThe final model was selected as the model with best performance in validation dataset. Then, the predictive accuracy of final model was tested with the remaining 53 patients (479 videos) in test dataset. Our AI model showed a c-statistic of 0.61 in validation dataset and 0.61 in test dataset, respectively.
conclusionAn artificial intelligence applied to CAG videos could detect clinically meaningful coronary atherosclerotic stenosis diagnosed by expert cardiologists with modest predictive value. Further studies with improved AI at larger sample size is necessary.
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