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Related Experiment Video

Updated: Feb 17, 2026

Deep Vein Thrombosis Induced by Stasis in Mice Monitored by High Frequency Ultrasonography
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Prediction model for deep vein thrombosis stability based on multiple machine learning methods.

Yaxi Yu1, Min Wang1, Jianxia Song1

  • 1Hebei North University, Zhangjiakou, China.

Phlebology
|February 16, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning models effectively predict deep vein thrombosis (DVT) stability using clinical data and computed tomography (CT) texture features. The logistic regression model demonstrated superior performance in identifying DVT stability, aiding clinical decision-making.

Keywords:
deep vein thrombosismachine learningprediction modelstabilitytexture analysis

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Vascular Medicine

Background:

  • Deep vein thrombosis (DVT) poses a significant risk, with stability being a crucial factor in predicting outcomes like acute pulmonary embolism (APE).
  • Accurate prediction of DVT stability is essential for timely and appropriate clinical intervention.

Purpose of the Study:

  • To develop and compare multiple machine learning (ML) models for predicting DVT stability.
  • To identify the optimal model utilizing clinical and computed tomography (CT) texture features for DVT stability prediction.

Main Methods:

  • 108 patients with DVT were analyzed, categorizing them into stable (no APE) and unstable (with APE) groups.
  • CT texture features were extracted from thrombus regions, and clinical data were collected.
  • Four ML algorithms (logistic regression, SVM, KNN, XGBoost) were trained and validated using a combination of clinical and CT texture features.

Main Results:

  • The logistic regression (LR) model achieved the highest prediction performance.
  • LR model metrics included an AUC of 0.87, accuracy of 0.79, precision of 0.75, recall of 0.80, and specificity of 0.87.
  • The LR model demonstrated superior predictive capability compared to SVM, KNN, and XGBoost models.

Conclusions:

  • Machine learning models integrating clinical and CT texture features can reliably predict DVT stability.
  • The developed ML models, particularly the LR model, offer a promising tool for assessing DVT stability and guiding patient management.