ArticleFrontiers in aging neuroscience2021
Performance Analysis of Machine Learning and Deep Learning Architectures on Early Stroke Detection Using Carotid Artery Ultrasound Images.
Article in Frontiers in aging neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Diagnostic Performance of Deep Learning and Radiomics in Extracranial Carotid Plaque Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- A 25-Year Retrospective of the Use of AI for Diagnosing Acute Stroke: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Predictive Performance of Artificial Intelligence Models for Stroke Risk Stratification: A Systematic Review and Meta-Analysis.Inquiry : a journal of medical care organization, provision and financingPooled it
- Multi-Omics and Machine Learning Integration Identifies Key Endothelial Modules in Carotid Artery Stenosis.Journal of clinical laboratory analysis · 2026Article
- Computer vision applications in vascular surgery: a systematic review and critical appraisal.NPJ digital medicine · 2026Article
- Predictive etiological classification of acute ischemic stroke through interpretable machine learning algorithms: a multicenter, prospective cohort study.BMC medical research methodology · 2024Article
- A review of emergent intelligent systems for the detection of Parkinson's disease.Biomedical engineering letters · 2023Review
- Machine learning approaches for biomarker discovery to predict large-artery atherosclerosis.Scientific reports · 2023Article
- Cardiovascular disease/stroke risk stratification in deep learning framework: a review.Cardiovascular diagnosis and therapy · 2023Review
- Using Ultrasound Image Augmentation and Ensemble Predictions to Prevent Machine-Learning Model Overfitting.Diagnostics (Basel, Switzerland) · 2023Article
- Utilizing machine learning algorithms for the prediction of carotid artery plaques in a Chinese population.Frontiers in physiology · 2023Article
- Artificial intelligence in atherosclerotic disease: Applications and trends.Frontiers in cardiovascular medicine · 2022Review
- Emerging Feature Extraction Techniques for Machine Learning-Based Classification of Carotid Artery Ultrasound Images.Computational intelligence and neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atherosclerotic plaque deposit in the carotid artery is used as an early estimate to identify the presence of cardiovascular diseases. Ultrasound images of the carotid artery are used to provide the extent of stenosis by examining the intima-media thickness and plaque diameter. A total of 361 images were classified using machine learning and deep learning approaches to recognize whether the person is symptomatic or asymptomatic. CART decision tree, random forest, and logistic regression machine learning algorithms, convolutional neural network (CNN), Mobilenet, and Capsulenet deep learning algorithms were applied in 202 normal images and 159 images with carotid plaque. Random forest provided a competitive accuracy of 91.41% and Capsulenet transfer learning approach gave 96.7% accuracy in classifying the carotid artery ultrasound image database.
Indexed as
Identifiers
What OpenQuestion holds
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.