ArticleThe international journal of cardiovascular imaging2025
Diagnostic performance of deep learning-based coronary computed tomography angiography in detecting coronary artery stenosis.
Article in The international journal of cardiovascular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06172985 (A CCTA Image Assisted Triage Software for the Assessment of Patients With Suspected Coronary Artery Disease), which is not on this map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A CCTA Image Assisted Triage Software for the Assessment of Patients With Suspected Coronary Artery Disease: a Retrospective, Multi-center, Blind-evaluation Study
Who cites it
2 citing papers in PubMed.
- Exploring double low-dose CT technology for achieving coronary mixed reality radiation dose reduction: a preliminary study in coronary artery disease patients.Quantitative imaging in medicine and surgery · 2026Article
- Deep learning predicts stent implantation in borderline coronary lesions from angiography.NPJ digital medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
purposeTo validate a fully automated, deep learning model based on coronary computed tomography angiography (CCTA) for the diagnosis of obstructive coronary artery disease (CAD) with stenosis ≥ 50%, which is commonly used as a clinical threshold for further testing and management. This model aims to improve diagnostic efficiency by automating the identification of significant coronary stenosis(≥ 50%).
methodsThis multicenter clinical trial included patients been undergone CCTA from October 13, 2022, to February 28, 2023. CCTA data from suspected coronary artery disease (CAD) patients were retrospectively analyzed using deep learning-based software for comprehensive assessment, including coronary segmentation, lumen, and stenosis determination with comparison to the reference standard of consensus by three experts. This study utilized a multi-stage deep learning framework for coronary artery segmentation and stenosis analysis from CCTA images, consisting of several key components, including the 3D Multi-resolution Cascade Convolutional Neural Network (CNN), 3D Cascade-Locally Optimized Network, and Stenosis Analysis Network. The clinical trial registry number was NCT06172985.
resultsA total of 1090 patients (mean age: 59.90 ± 11.51 years, 47.3% female) were included in this multicenter study. Artificial intelligence (AI) demonstrated excellent performance at the patient level, accurately diagnosing ≥ 50% stenosis by assessing each patient's coronary artery condition. The AI system showed high values for accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The values of the above statistics were 92.8%, 95.3%, 91.4%, 85.6%, and 97.3%, respectively. Excellent agreement was seen between expert readers and deep learning-determined maximal diameter stenosis for per-patient (kappa coefficients: 0.84, 95%CI: 0.81-0.88). Regarding diagnostic efficiency, comparing the AI with expert readers, the average reading time decreased from 5.94 min to 2.01 min (p < 0.001).
conclusionA novel AI-based assessment of CCTA can accurately and rapidly identify patients with coronary artery stenosis ≥ 50%, aiding in effective triage within the defined study population.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.