ArticleScientific reports2018
An Automatic Classification Method on Chronic Venous Insufficiency Images.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- A deep learning framework for recognizing skin changes secondary to chronic venous insufficiency in clinical photographs: a multicentre validation study.Annals of medicine · 2026Article
- Development and validation of the IVENUS artificial intelligence model for automated CEAP classification of early-stage chronic venous disease using lower limb photographs.Journal of vascular surgery. Venous and lymphatic disorders · 2026Article
- Machine learning-based prediction models to assess 30-day outcomes following intervention for superficial venous incompetence of the lower limbs.Journal of vascular surgery cases and innovative techniques · 2026Article
- Machine Learning Applications for Venous Ulcer Assessment and Wound Care: A Review.Diagnostics (Basel, Switzerland) · 2026Review
- Does Artificial Intelligence Bring New Insights in Diagnosing Phlebological Diseases?-A Systematic Review.Biomedicines · 2025Review
- Computer Science meets Vascular Surgery: Keeping a pulse on artificial intelligence.Seminars in vascular surgery · 2023Review
- A multi-task convolutional neural network for classification and segmentation of chronic venous disorders.Scientific reports · 2023Article
- Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management.Journal of personalized medicine · 2021Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Chronic venous insufficiency (CVI) affect a large population, and it cannot heal without doctors' interventions. However, many patients do not get the medical advisory service in time. At the same time, the doctors also need an assistant tool to classify the patients according to the severity level of CVI. We propose an automatic classification method, named CVI-classifier to help doctors and patients. In this approach, first, low-level image features are mapped into middle-level semantic features by a concept classifier, and a multi-scale semantic model is constructed to form the image representation with rich semantics. Second, a scene classifier is trained using an optimized feature subset calculated by the high-order dependency based feature selection approach, and is used to estimate CVI's severity. At last, classification accuracy, kappa coefficient, F1-score are used to evaluate classification performance. Experiments on the CVI images from 217 patients' medical records demonstrated superior performance and efficiency for CVI-classifier, with classification accuracy up to 90.92%, kappa coefficient of 0.8735 and F1score of 0.9006. This method also outperformed doctors' diagnosis (doctors rely solely on images to make judgments) with accuracy, kappa and F1-score improved by 9.11%, 0.1250 and 0.0955 respectively.
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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.