An artificial intelligence interpretable tool to predict risk of deep vein thrombosis after endovenous thermal ablation
We developed and validated an interpretable model that enables physicians to predict which patients with superficial venous insufficiency has higher risk of developing DVT within 30 days following EVTA.
Authors
- Azadeh Tabari (ORCID: https://orcid.org/0000-0002-5685-6401)
- Dimitris Bertsimas (ORCID: https://orcid.org/0000-0002-1985-1003)
- Haytham M.A. Kaafarani (ORCID: https://orcid.org/0000-0002-4682-9135)
- Anthony Gebran (ORCID: https://orcid.org/0000-0003-3311-9908)
- Dania Daye (ORCID: https://orcid.org/0000-0002-2230-7199)
- Yu Ma (ORCID: https://orcid.org/0000-0001-8409-7222)
- José Alfonso
Institutions
- Massachusetts General Hospital (US)
- Massachusetts Institute of Technology (US)
Publication Details
- Journal
- Journal of Vascular Surgery Venous and Lymphatic Disorders
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1016/j.jvsv.2025.102253
- Citations
- 1
- Primary Topic
- Venous Thromboembolism Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 1.87