ArticleiScience2024
Deep learning model for diagnosis of venous thrombosis from lower extremity peripheral ultrasound imaging.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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
5 citing papers in PubMed.
- Ultrasound-Mediated Thrombolysis: From Mechanistic Insights to Advanced Nanoplatforms and Clinical Translation.Advanced healthcare materials · 2026Review
- Electronic medical record-based causal network modeling for acute myocardial infarction diagnosis in the emergency department.iScience · 2026Article
- Artificial intelligence risk stratification from dynamic digital subtraction angiography radiomics predicts pulmonary embolism and associates with clinical outcomes in deep vein thrombosis: A retrospective cohort study.Journal of vascular surgery. Venous and lymphatic disorders · 2026Article
- Deep learning algorithm enables lower limb venous thrombosis detection with CT venography.Frontiers in cardiovascular medicine · 2026Article
- StarMA Net: A star-shape multi-scale attention network for medical imaging classification.iScience · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep vein thrombosis (DVT) causes significant healthcare burdens worldwide. This study aims to establish a deep learning model for the diagnosis of DVT from the assessment of vein compressibility. Considering the complexity of ultrasound images, convolutional neural networks with UNet and residual neural network (ResNet) are established for image segmentation, from venous duplex ultrasonographic video images, obtained through standard and portable handheld ultrasound methods. To further evaluate the similarity between the predicted and ground truth images, the structural similarity index (SSIM) is employed. Our deep learning model achieves over 90% accuracy, providing an innovative tool for both images and videos. This study harnesses the power of machine learning to develop an automatic labeling tool that can diagnose DVT by analyzing ultrasonography images. To make the tool more accessible to front-line clinicians, a user-friendly application is created to quickly assess possible clinical severity and enable prompt medical intervention, reducing disease progression.
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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.