ArticleJournal of medical systems2019
Classification of Carotid Artery Intima Media Thickness Ultrasound Images with Deep Learning.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers.
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
17 citing papers in PubMed, 54 citations in OpenAlex.
- Evaluation of retinal microcirculation in hypertensive patients with obstructive sleep apnea: a subgroup analysis based on asymptomatic organ damage.International ophthalmology · 2026Article
- A Dirichlet Distribution-Based Complex Ensemble Approach for Breast Cancer Classification from Ultrasound Images with Transfer Learning and Multiphase Spaced Repetition Method.Journal of imaging informatics in medicine · 2026Article
- 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
- 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
- Ultrasound-Based Image Analysis for Predicting Carotid Artery Stenosis Risk: A Comprehensive Review of the Problem, Techniques, Datasets, and Future Directions.Diagnostics (Basel, Switzerland) · 2023Review
- Cardiovascular disease/stroke risk stratification in deep learning framework: a review.Cardiovascular diagnosis and therapy · 2023Review
- Deep Learning Methods for Interpretation of Pulmonary CT and X-ray Images in Patients with COVID-19-Related Lung Involvement: A Systematic Review.Journal of clinical medicine · 2023Review
- Improving the Segmentation Accuracy of Ovarian-Tumor Ultrasound Images Using Image Inpainting.Bioengineering (Basel, Switzerland) · 2023Article
- Stroke risk prediction by color Doppler ultrasound of carotid artery-based deep learning using Inception V3 and VGG-16.Frontiers in neurology · 2023Article
- A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet.Bioengineering (Basel, Switzerland) · 2022Article
- CapsCovNet: A Modified Capsule Network to Diagnose COVID-19 From Multimodal Medical Imaging.IEEE transactions on artificial intelligence · 2021Article
- Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement.Sensors (Basel, Switzerland) · 2021Article
- Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application.Annals of translational medicine · 2021Review
- Review
- [Detection of carotid intima and media thicknesses based on ultrasound B-mode images clustered with Gaussian mixture model].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2020Article
- The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Different Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review.Diagnostics (Basel, Switzerland) · 2019Review
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cerebrovascular accident due to carotid artery disease is the most common cause of death in developed countries following heart disease and cancer. For a reliable early detection of atherosclerosis, Intima Media Thickness (IMT) measurement and classification are important. A new method for decision support purpose for the classification of IMT was proposed in this study. Ultrasound images are used for IMT measurements. Images are classified and evaluated by experts. This is a manual procedure, so it causes subjectivity and variability in the IMT classification. Instead, this article proposes a methodology based on artificial intelligence methods for IMT classification. For this purpose, a deep learning strategy with multiple hidden layers has been developed. In order to create the proposed model, convolutional neural network algorithm, which is frequently used in image classification problems, is used. 501 ultrasound images from 153 patients were used to test the model. The images are classified by two specialists, then the model is trained and tested on the images, and the results are explained. The deep learning model in the study achieved an accuracy of 89.1% in the IMT classification with 89% sensitivity and 88% specificity. Thus, the assessments in this paper have shown that this methodology performs reasonable results for IMT classification.
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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.