ReviewJournal of digital imaging2021
A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework.
Review in Journal of digital imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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.
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Who cites it
34 citing papers in PubMed, 62 citations in OpenAlex.
- The effects of curcumin-piperine supplementation on inflammatory, oxidative stress and metabolic indices in patients with ischemic stroke in the rehabilitation phase: a randomized controlled trial.Nutrition journal · 2023Trial
- AI-Enhanced Super-Resolution Handheld Ultrasound for Carotid Plaque Detection in Community Screening.Annals of family medicine · 2026Article
- Prognostic significance of carotid intima-media thickness in patients with coronary slow flow: A systematic review and meta-analysis.International journal of cardiology. Cardiovascular risk and prevention · 2026Review
- Women's Cardiovascular Disease and Stroke Risk Stratification Using a Precision and Personalized Framework Embedded with an Explainable Artificial Intelligence Paradigm: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Development of a preoperative prediction model for neoplastic polyps of the gallbladder based on a convolutional neural network model using ultrasonic images.Translational gastroenterology and hepatology · 2026Article
- Ultrasound in cardiovascular care: a perspective on preventive, diagnostic, and monitoring applications.Frontiers in cardiovascular medicine · 2026Article
- Article
- Performance Comparison of U-Net and Its Variants for Carotid Intima-Media Segmentation in Ultrasound Images.Diagnostics (Basel, Switzerland) · 2025Article
- Application of Artificial Intelligence in Vulnerable Carotid Atherosclerotic Plaque Assessment-A Scoping Review.Medicina (Kaunas, Lithuania) · 2025Article
- Artificial intelligence in carotid computed tomography angiography plaque detection: Decade of progress and future perspectives.World journal of radiology · 2025Review
- (Apo)Lipoprotein Profiling with Multi-Omics Analysis Identified Medium-HDL-Targeting PSRC1 with Therapeutic Potential for Coronary Artery Disease.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Intelligent handheld ultrasound improving the ability of non-expert general practitioners in carotid examinations for community populations: a prospective and parallel controlled trial.Ultrasonography (Seoul, Korea) · 2025Article
- Association between lipoprotein(a) and atherosclerosis with different diabetic status: a cross-sectional study in a Chinese population.Cardiovascular diagnosis and therapy · 2025Article
- Artificial intelligence-based cardiovascular/stroke risk stratification in women affected by autoimmune disorders: a narrative survey.Rheumatology international · 2025Review
- Mildly elevated serum prolactin level may be a protective factor for preventing thickening of the carotid intima-media in patients with type 2 diabetes mellitus.Endocrine connections · 2025Article
- Risk prediction of recurrent ischemic stroke based on Carotid Plaque-RADS: construction and validation of a nomogram model.Frontiers in aging neuroscience · 2025Article
- Geometric consistency among atherosclerotic plaques in carotid arteries evaluated by multidimensional parameters.Heliyon · 2024Article
- Explore the value of carotid ultrasound radiomics nomogram in predicting ischemic stroke risk in patients with type 2 diabetes mellitus.Frontiers in endocrinology · 2024Article
- Artificial intelligence applied in cardiovascular disease: a bibliometric and visual analysis.Frontiers in cardiovascular medicine · 2024Article
- A deep learning algorithm to identify carotid plaques and assess their stability.Frontiers in artificial intelligence · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors at 17 institutions in 10 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular diseases (CVDs) are the top ten leading causes of death worldwide. Atherosclerosis disease in the arteries is the main cause of the CVD, leading to myocardial infarction and stroke. The two primary image-based phenotypes used for monitoring the atherosclerosis burden is carotid intima-media thickness (cIMT) and plaque area (PA). Earlier segmentation and measurement methods were based on ad hoc conventional and semi-automated digital imaging solutions, which are unreliable, tedious, slow, and not robust. This study reviews the modern and automated methods such as artificial intelligence (AI)-based. Machine learning (ML) and deep learning (DL) can provide automated techniques in the detection and measurement of cIMT and PA from carotid vascular images. Both ML and DL techniques are examples of supervised learning, i.e., learn from "ground truth" images and transformation of test images that are not part of the training. This review summarizes (1) the evolution and impact of the fast-changing AI technology on cIMT/PA measurement, (2) the mathematical representations of ML/DL methods, and (3) segmentation approaches for cIMT/PA regions in carotid scans based for (a) region-of-interest detection and (b) lumen-intima and media-adventitia interface detection using ML/DL frameworks. AI-based methods for cIMT/PA segmentation have emerged for CVD/stroke risk monitoring and may expand to the recommended parameters for atherosclerosis assessment by carotid ultrasound.
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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.