ArticleNPJ digital medicine2021
Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 32 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Benefit of Machine Learning to Diagnose Deep Vein Thrombosis Compared to Gold Standard Ultrasound
Determining the Validity of ThinkSono Guidance for Ultrasound Image Acquisition and Remote Detection
Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it, 78 citations in OpenAlex.
- Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.Haematologica · 2023Pooled it
- Quantitative Compression Ultrasound via a Force-Area Digital Twin for Mechanistic Assessment of Venous Thrombosis.Annals of biomedical engineering · 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
- Artificial intelligence to support ultrasound in the detection of developmental dysplasia of the hip : a scoping review.Bone & joint open · 2026Article
- Smart, automated junctional tourniquets leveraging AI-driven ultrasound guidance.Scientific reports · 2026Article
- Integrated multi-omics and machine learning identify an interaction between SLC39A11 and phosphoinositide metabolism in deep vein thrombosis.BMC medical informatics and decision making · 2026Article
- Article
- ThrombUS+ Project: Toward Wearable Continuous Point-of-Care Monitoring for Deep Vein Thrombosis of the Lower Limb.Computational and structural biotechnology journal · 2026Article
- Cellular and molecular mechanisms of thrombosis and thrombus-targeted thrombolytic strategies.Materials today. Bio · 2025Review
- Characteristics and outcomes of thromboembolism following cardiac transplant: a multi-institutional retrospective study.Research and practice in thrombosis and haemostasis · 2025Article
- Acoustic Wave Propagation Behaviors and Energy Loss Mechanisms in Agar Gels with Small Particles.Polymers · 2025Article
- Artificial intelligence in clinical thrombosis and hemostasis: A review.Research and practice in thrombosis and haemostasis · 2025Review
- Does Artificial Intelligence Bring New Insights in Diagnosing Phlebological Diseases?-A Systematic Review.Biomedicines · 2025Review
- Value of clinical review for AI-guided deep vein thrombosis diagnosis with ultrasound imaging by non-expert operators.NPJ digital medicine · 2025Article
- Non-Invasive Biomarkers in the Era of Big Data and Machine Learning.Sensors (Basel, Switzerland) · 2025Review
- Sonothrombolysis Using Microfluidically Produced Microbubbles in a Murine Model of Deep Vein Thrombosis.Annals of biomedical engineering · 2025Article
- Deep learning model for diagnosis of venous thrombosis from lower extremity peripheral ultrasound imaging.iScience · 2024Article
- Evaluating the benefits of machine learning for diagnosing deep vein thrombosis compared with gold standard ultrasound: a feasibility study.BJGP open · 2024Article
- A Quantitative Method for the Evaluation of Deep Vein Thrombosis in a Murine Model Using Three-Dimensional Ultrasound Imaging.Biomedicines · 2024Article
- Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot.Bleeding, thrombosis and vascular biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep vein thrombosis (DVT) is a blood clot most commonly found in the leg, which can lead to fatal pulmonary embolism (PE). Compression ultrasound of the legs is the diagnostic gold standard, leading to a definitive diagnosis. However, many patients with possible symptoms are not found to have a DVT, resulting in long referral waiting times for patients and a large clinical burden for specialists. Thus, diagnosis at the point of care by non-specialists is desired. We collect images in a pre-clinical study and investigate a deep learning approach for the automatic interpretation of compression ultrasound images. Our method provides guidance for free-hand ultrasound and aids non-specialists in detecting DVT. We train a deep learning algorithm on ultrasound videos from 255 volunteers and evaluate on a sample size of 53 prospectively enrolled patients from an NHS DVT diagnostic clinic and 30 prospectively enrolled patients from a German DVT clinic. Algorithmic DVT diagnosis performance results in a sensitivity within a 95% CI range of (0.82, 0.94), specificity of (0.70, 0.82), a positive predictive value of (0.65, 0.89), and a negative predictive value of (0.99, 1.00) when compared to the clinical gold standard. To assess the potential benefits of this technology in healthcare we evaluate the entire clinical DVT decision algorithm and provide cost analysis when integrating our approach into diagnostic pathways for DVT. Our approach is estimated to generate a positive net monetary benefit at costs up to £72 to £175 per software-supported examination, assuming a willingness to pay of £20,000/QALY.
Identifiers
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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.