ArticleIEEE journal of biomedical and health informatics2017
A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound.
Article in IEEE journal of biomedical and health informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05836246 (The Development of Artificial Intelligence), which is not on this map. Cited by 57 papers, 2 of them syntheses that pooled it.
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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.
The Development of Artificial Intelligence (AI) Based High Performance Structural-functional and Quantitative Ultrasound Imaging Software Platform
Who cites it
57 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions.Computational and mathematical methods in medicine · 2022Pooled it
- Carotid Artery Wall Imaging: Perspective and Guidelines from the ASNR Vessel Wall Imaging Study Group and Expert Consensus Recommendations of the American Society of Neuroradiology.AJNR. American journal of neuroradiology · 2018Guideline
- Role of Cardiac Imaging in the Evaluation of Rheumatoid Arthiritis: A Systematic Analysis.Cureus · 2026Review
- Features of B-mode ultrasound and contrast-enhanced ultrasound of carotid plaque based on deep learning enhance the prediction of vulnerable plaques associated with acute ischemic stroke.European radiology · 2026Article
- Cascaded Deep Learning-Based Model for Classification and Segmentation of Plaques from Carotid Ultrasound Images.Bioengineering (Basel, Switzerland) · 2026Article
- 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
- Automated scheme of plaque classification based on segmentation in carotid ultrasound images using transformer approach.Journal of medical ultrasonics (2001) · 2025Article
- Carotid plaque segmentation and classification using MRI-based plaque texture analysis and convolutional neural network.Frontiers in medicine · 2025Article
- Transfer learning with class activation maps in compositions driving plaque classification in carotid ultrasound.Frontiers in digital health · 2025Article
- DualDistill: a dual-guided self-distillation approach for carotid plaque analysis.Frontiers in medicine · 2025Article
- Neural network-based arterial diameter estimation from ultrasound data.PLOS digital health · 2024Article
- Real-time carotid plaque recognition from dynamic ultrasound videos based on artificial neural network.Ultraschall in der Medizin (Stuttgart, Germany : 1980) · 2024Article
- Current Applications and Future Perspectives of Artificial and Biomimetic Intelligence in Vascular Surgery and Peripheral Artery Disease.Biomimetics (Basel, Switzerland) · 2024Review
- The new era of artificial intelligence in neuroradiology: current research and promising tools.Arquivos de neuro-psiquiatria · 2024Article
- From big data to big insights: statistical and bioinformatic approaches for exploring the lipidome.Analytical and bioanalytical chemistry · 2024Review
- AI: A New Solution for Old Issues of Carotid Atherosclerotic Plaque.Current medicinal chemistry · 2024Article
- Feasibility of Encord Artificial Intelligence Annotation of Arterial Duplex Ultrasound Images.Diagnostics (Basel, Switzerland) · 2023Article
- Cardiovascular disease/stroke risk stratification in deep learning framework: a review.Cardiovascular diagnosis and therapy · 2023Review
- Artificial intelligence in diagnostic ultrasonography.Diagnostic and interventional radiology (Ankara, Turkey) · 2023Review
- Utilizing machine learning algorithms for the prediction of carotid artery plaques in a Chinese population.Frontiers in physiology · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Characterization of carotid plaque composition, more specifically the amount of lipid core, fibrous tissue, and calcified tissue, is an important task for the identification of plaques that are prone to rupture, and thus for early risk estimation of cardiovascular and cerebrovascular events. Due to its low costs and wide availability, carotid ultrasound has the potential to become the modality of choice for plaque characterization in clinical practice. However, its significant image noise, coupled with the small size of the plaques and their complex appearance, makes it difficult for automated techniques to discriminate between the different plaque constituents. In this paper, we propose to address this challenging problem by exploiting the unique capabilities of the emerging deep learning framework. More specifically, and unlike existing works which require a priori definition of specific imaging features or thresholding values, we propose to build a convolutional neural network (CNN) that will automatically extract from the images the information that is optimal for the identification of the different plaque constituents. We used approximately 90 000 patches extracted from a database of images and corresponding expert plaque characterizations to train and to validate the proposed CNN. The results of cross-validation experiments show a correlation of about 0.90 with the clinical assessment for the estimation of lipid core, fibrous cap, and calcified tissue areas, indicating the potential of deep learning for the challenging task of automatic characterization of plaque composition in carotid ultrasound.
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