ArticleBiomedical optics express2019
Automated plaque characterization using deep learning on coronary intravascular optical coherence tomographic images.
Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction.Tomography (Ann Arbor, Mich.) · 2022Pooled it
- Deep-learning-aided sarcoma biopsy guidance using forward-view endoscopic optical coherence tomography.Biomedical optics express · 2026Article
- Computational Analysis of Intravascular OCT Images for Future Clinical Support: A Comprehensive Review.IEEE reviews in biomedical engineering · 2026Review
- Artificial Intelligence-based Approaches for Characterizing Plaque Components From Intravascular Optical Coherence Tomography Imaging: Integration Into Clinical Decision Support Systems.Reviews in cardiovascular medicine · 2025Review
- 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
- Examination of the performance of machine learning-based automated coronary plaque characterization by near-infrared spectroscopy-intravascular ultrasound and optical coherence tomography with histology.European heart journal. Digital health · 2025Article
- Automated comprehensive evaluation of coronary artery plaque in IVOCT using deep learning.iScience · 2025Article
- Histology-Grounded Automated Plaque Subtype Segmentation in Intravascular Optical Coherence Tomography.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Article
- A plaque recognition algorithm for coronary OCT images by Dense Atrous Convolution and attention mechanism.PloS one · 2025Article
- Review
- Plaque Characteristics Derived from Intravascular Optical Coherence Tomography That Predict Cardiovascular Death.Bioengineering (Basel, Switzerland) · 2024Article
- Coronary artery calcification and cardiovascular outcome as assessed by intravascular OCT and artificial intelligence.Biomedical optics express · 2024Article
- Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images.Scientific reports · 2024Article
- Validation of biomechanical assessment of coronary plaque vulnerability based on intravascular optical coherence tomography and digital subtraction angiography.Quantitative imaging in medicine and surgery · 2024Article
- 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
- Prediction of stent under-expansion in calcified coronary arteries using machine learning on intravascular optical coherence tomography images.Scientific reports · 2023Article
- Pericoronary Adipose Tissue Radiomics from Coronary Computed Tomography Angiography Identifies Vulnerable Plaques.Bioengineering (Basel, Switzerland) · 2023Article
- Toward reliable calcification detection: calibration of uncertainty in object detection from coronary optical coherence tomography images.Journal of biomedical optics · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Accurate identification of coronary plaque is very important for cardiologists when treating patients with advanced atherosclerosis. We developed fully-automated semantic segmentation of plaque in intravascular OCT images. We trained/tested a deep learning model on a folded, large, manually annotated clinical dataset. The sensitivities/specificities were 87.4%/89.5% and 85.1%/94.2% for pixel-wise classification of lipidous and calcified plaque, respectively. Automated clinical lesion metrics, potentially useful for treatment planning and research, compared favorably (<4%) with those derived from ground-truth labels. When we converted the results to A-line classification, they were significantly better (p < 0.05) than those obtained previously by using deep learning classifications of A-lines.
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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.