ArticleScientific reports2022
Automated analysis of fibrous cap in intravascular optical coherence tomography images of coronary arteries.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 22 citations in OpenAlex.
- Precision cardiovascular medicine with big data and AI.NPJ digital medicine · 2026Review
- Computational Analysis of Intravascular OCT Images for Future Clinical Support: A Comprehensive Review.IEEE reviews in biomedical engineering · 2026Review
- Polarization-sensitive optical coherence tomography-based fully-automated volumetric coronary fibrous cap characterization.European heart journal. Imaging methods and practice · 2026Article
- Review and recommendations for using artificial intelligence in intracoronary optical coherence tomography analysis.European heart journal. Digital health · 2025Review
- Comprehensive full-vessel segmentation and volumetric plaque quantification for intracoronary optical coherence tomography using deep learning.European heart journal. Digital health · 2025Article
- Harnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Artificial Intelligence in Coronary Artery Interventions: Preprocedural Planning and Procedural Assistance.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Plaque Characteristics Derived from Intravascular Optical Coherence Tomography That Predict Cardiovascular Death.Bioengineering (Basel, Switzerland) · 2024Article
- Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images.Scientific reports · 2024Article
- Artificial Intelligence for the Interventional Cardiologist: Powering and Enabling OCT Image Interpretation.Interventional cardiology (London, England) · 2024Review
- Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries.Nature reviews. Cardiology · 2024Review
- Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine.Journal of personalized medicine · 2023Review
- Pericoronary Adipose Tissue Radiomics from Coronary Computed Tomography Angiography Identifies Vulnerable Plaques.Bioengineering (Basel, Switzerland) · 2023Article
- Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning.Bioengineering (Basel, Switzerland) · 2022Article
- Neoatherosclerosis prediction using plaque markers in intravascular optical coherence tomography images.Frontiers in cardiovascular medicine · 2022Article
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
Abstract
Thin-cap fibroatheroma (TCFA) and plaque rupture have been recognized as the most frequent risk factor for thrombosis and acute coronary syndrome. Intravascular optical coherence tomography (IVOCT) can identify TCFA and assess cap thickness, which provides an opportunity to assess plaque vulnerability. We developed an automated method that can detect lipidous plaque and assess fibrous cap thickness in IVOCT images. This study analyzed a total of 4360 IVOCT image frames of 77 lesions among 41 patients. Expert cardiologists manually labeled lipidous plaque based on established criteria. To improve segmentation performance, preprocessing included lumen segmentation, pixel-shifting, and noise filtering on the raw polar (r, θ) IVOCT images. We used the DeepLab-v3 plus deep learning model to classify lipidous plaque pixels. After lipid detection, we automatically detected the outer border of the fibrous cap using a special dynamic programming algorithm and assessed the cap thickness. Our method provided excellent discriminability of lipid plaque with a sensitivity of 85.8% and A-line Dice coefficient of 0.837. By comparing lipid angle measurements between two analysts following editing of our automated software, we found good agreement by Bland-Altman analysis (difference 6.7° ± 17°; mean ~ 196°). Our method accurately detected the fibrous cap from the detected lipid plaque. Automated analysis required a significant modification for only 5.5% frames. Furthermore, our method showed a good agreement of fibrous cap thickness between two analysts with Bland-Altman analysis (4.2 ± 14.6 µm; mean ~ 175 µm), indicating little bias between users and good reproducibility of the measurement. We developed a fully automated method for fibrous cap quantification in IVOCT images, resulting in good agreement with determinations by analysts. The method has great potential to enable highly automated, repeatable, and comprehensive evaluations of TCFAs.
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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 Socratic graph.