ArticleEuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology2021
Artificial intelligence and optical coherence tomography for the automatic characterisation of human atherosclerotic plaques.
Article in EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
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Who cites it
81 citing papers in PubMed, 133 citations in OpenAlex.
- Study on the potential clinical significance of subclinical stent edge effects after drug-eluting stent implantation.Scientific reports · 2025Trial
- Near real-time multi-class segmentation for intravascular optical coherence tomography using knowledge distillation.European heart journal. Digital health · 2026Article
- From intravascular imaging to adaptive vascular care: intelligent photonics and digital twins in panvascular disease.Light, science & applications · 2026Review
- Artificial Intelligence-Assisted Intravascular Imaging in Percutaneous Coronary Intervention: Current Status and Advances.Reviews in cardiovascular medicine · 2026Review
- From Electrocardiography to the Catheterization Laboratory: A Multimodal Artificial Intelligence Framework for Acute Coronary Syndrome Detection and Risk Stratification.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Vascular Surgery: A Literature Review Focusing on Current Applications, Imaging Advances and Future Prospects.Journal of clinical medicine · 2026Review
- Artificial intelligence-powered automatic coronary computed tomography angiography plaque quantification: comparison against optical coherence tomography.European heart journal. Digital health · 2026Article
- Precision percutaneous coronary intervention.NPJ cardiovascular health · 2026Review
- Precision cardiovascular medicine with big data and AI.NPJ digital medicine · 2026Review
- AI-driven virtual histology with optical coherence tomography for comparing acute and short-trem performance of bioresorbable scaffolds and drug-eluting stents.BMC cardiovascular disorders · 2026Observational
- Artificial Intelligence in Coronary Plaque Characterization: Clinical Implications, Evidence Gaps, and Future Directions.Journal of clinical medicine · 2026Review
- Computational Analysis of Intravascular OCT Images for Future Clinical Support: A Comprehensive Review.IEEE reviews in biomedical engineering · 2026Review
- Modern Imaging Techniques for Percutaneous Coronary Intervention Guidance: A Focus on Intravascular Ultrasound and Optical Coherence Tomography.Journal of clinical medicine · 2025Review
- Automatic measuring of coronary atherosclerosis from medicolegal autopsy photographs based on deep learning techniques.Forensic science, medicine, and pathology · 2025Article
- Automated Artificial Intelligence Mapping of Coronary Plaque Calcification: A Comparison with Manual Intravascular Image Analysis.Journal of clinical medicine · 2025Article
- Artificial Intelligence-Led Whole Coronary Artery OCT Analysis; Validation and Identification of Drug Efficacy and Higher-Risk Plaques.Circulation. Cardiovascular imaging · 2025Article
- Plaques Do Not Act Alone: Time to Redefine Coronary Vulnerability from Lesion to Phenotype.Journal of clinical medicine · 2025Review
- Observational
- Validation of artificial intelligence-enabled versus visual quantification of calcification by optical coherence tomography.International journal of cardiology. Heart & vasculature · 2025Article
- The Rise of Optical Coherent Tomography in Intracoronary Imaging: An Overview of Current Technology, Limitations, and Future Perspectives.Reviews in cardiovascular medicine · 2025Review
21 more citing papers are in PubMed but not listed here.
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Authors and funding
19 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIntravascular optical coherence tomography (IVOCT) enables detailed plaque characterisation in vivo, but visual assessment is time-consuming and subjective.
aimsThis study aimed to develop and validate an automatic framework for IVOCT plaque characterisation using artificial intelligence (AI).
methodsIVOCT pullbacks from five international centres were analysed in a core lab, annotating basic plaque components, inflammatory markers and other structures. A deep convolutional network with encoding-decoding architecture and pseudo-3D input was developed and trained using hybrid loss. The proposed network was integrated into commercial software to be externally validated on additional IVOCT pullbacks from three international core labs, taking the consensus among core labs as reference.
resultsAnnotated images from 509 pullbacks (391 patients) were divided into 10,517 and 1,156 cross-sections for the training and testing data sets, respectively. The Dice coefficient of the model was 0.906 for fibrous plaque, 0.848 for calcium and 0.772 for lipid in the testing data set. Excellent agreement in plaque burden quantification was observed between the model and manual measurements (R2=0.98). In the external validation, the software correctly identified 518 out of 598 plaque regions from 300 IVOCT cross-sections, with a diagnostic accuracy of 97.6% (95% CI: 93.4-99.3%) in fibrous plaque, 90.5% (95% CI: 85.2-94.1%) in lipid and 88.5% (95% CI: 82.4-92.7%) in calcium. The median time required for analysis was 21.4 (18.6-25.0) seconds per pullback.
conclusionsA novel AI framework for automatic plaque characterisation in IVOCT was developed, providing excellent diagnostic accuracy in both internal and external validation. This model might reduce subjectivity in image interpretation and facilitate IVOCT quantification of plaque composition, with potential applications in research and IVOCT-guided PCI.
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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.