ArticleDiagnostics (Basel, Switzerland)2022
Comparison between Deep Learning and Conventional Machine Learning in Classifying Iliofemoral Deep Venous Thrombosis upon CT Venography.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 25 citations in OpenAlex.
- Privacy-aware deep vein thrombosis segmentation using a multi-model federated learning framework with the federated averaging algorithm.Scientific reports · 2026Article
- Deep learning algorithm enables lower limb venous thrombosis detection with CT venography.Frontiers in cardiovascular medicine · 2026Article
- Machine learning-based model for predicting recanalization in isolated distal deep vein thrombosis and analysis of predictors.PloS one · 2026Article
- Early prediction of antibiotic need and bacteremia risk in non-immunocompromised pediatric emergency patients using machine learning.Pediatric research · 2025Article
- Lightweight Statistical and Texture Feature Approach for Breast Thermogram Analysis.Journal of imaging · 2025Article
- Autoencoder-Assisted Stacked Ensemble Learning for Lymphoma Subtype Classification: A Hybrid Deep Learning and Machine Learning Approach.Tomography (Ann Arbor, Mich.) · 2025Article
- Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature.Japanese journal of radiology · 2025Review
- Innovative modified-net architecture: enhanced segmentation of deep vein thrombosis.Scientific reports · 2024Article
- Deep learning model for diagnosis of venous thrombosis from lower extremity peripheral ultrasound imaging.iScience · 2024Article
- Improving the radiological diagnosis of hepatic artery thrombosis after liver transplantation: Current approaches and future challenges.World journal of transplantation · 2024Article
- Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot.Bleeding, thrombosis and vascular biology · 2024Article
- Deep learning techniques for imaging diagnosis and treatment of aortic aneurysm.Frontiers in cardiovascular medicine · 2024Review
- MediNet: transfer learning approach with MediNet medical visual database.Multimedia tools and applications · 2023Article
- Artificial intelligence-based iliofemoral deep venous thrombosis detection using a clinical approach.Scientific reports · 2023Article
- Radiomics and artificial neural networks modelling for identification of high-risk carotid plaques.Frontiers in cardiovascular medicine · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we aimed to investigate quantitative differences in performance in terms of comparing the automated classification of deep vein thrombosis (DVT) using two categories of artificial intelligence algorithms: deep learning based on convolutional neural networks (CNNs) and conventional machine learning. We retrospectively enrolled 659 participants (DVT patients, 282; normal controls, 377) who were evaluated using contrast-enhanced lower extremity computed tomography (CT) venography. Conventional machine learning consists of logistic regression (LR), support vector machines (SVM), random forests (RF), and extreme gradient boosts (XGB). Deep learning based on CNN included the VGG16, VGG19, Resnet50, and Resnet152 models. According to the mean generated AUC values, we found that the CNN-based VGG16 model showed a 0.007 higher performance (0.982 ± 0.014) as compared with the XGB model (0.975 ± 0.010), which showed the highest performance among the conventional machine learning models. In the conventional machine learning-based classifications, we found that the radiomic features presenting a statistically significant effect were median values and skewness. We found that the VGG16 model within the deep learning algorithm distinguished deep vein thrombosis on CT images most accurately, with slightly higher AUC values as compared with the other AI algorithms used in this study. Our results guide research directions and medical practice.
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