Key result
The PE-Mind deep learning model achieved an accuracy of 78.26% and an AUC of 0.8641 on the prospective test set, outperforming other machine learning models in predicting pulmonary embolism risk.
Why the study?
Current clinical tools for assessing pulmonary embolism risk in patients with acute deep vein thrombosis have limitations, necessitating a more precise, efficient, and targeted risk prediction model.
Does the PE-Mind deep learning model improve the prediction accuracy and reliability for assessing pulmonary embolism risk in patients with acute deep vein thrombosis compared to standard machine learning models?
Observational (n=424)
No
Does the PE-Mind deep learning model improve the prediction accuracy and reliability for assessing pulmonary embolism risk in patients with acute deep vein thrombosis compared to standard machine learning models?
The PE-Mind deep learning model provides a highly accurate and reliable tool for predicting the risk of pulmonary embolism in patients with acute deep vein thrombosis, potentially optimizing clinical treatment strategies.
May aid targeted PE risk assessment in acute DVT; leaves open prospective validation before clinical use.
Background: Pulmonary embolism (PE) is a common and potentially fatal condition. Timely and accurate risk assessment in patients with acute deep vein thrombosis (DVT) is crucial. This study aims to develop a deep learning-based, precise, and efficient PE risk prediction model (PE-Mind) to overcome the limitations of current clinical tools and provide a more targeted risk evaluation solution. Methods: We analyzed clinical data from patients by first simplifying and organizing the collected features. From these, 37 key clinical features were selected based on their importance. These features were categorized and analyzed to identify potential relationships. Our prediction model uses a convolutional neural network (CNN), enhanced with three custom-designed modules for better performance. To validate its effectiveness, we compared this model with five commonly used prediction models. Results: PE-Mind demonstrated the highest accuracy and reliability, achieving 0.7826 accuracy and an area under the receiver operating characteristic curve of 0.8641 on the prospective test set, surpassing other models. Based on this, we have also developed a Web server, PulmoRiskAI, for real-time clinician operation. Conclusion: The PE-Mind model improves prediction accuracy and reliability for assessing PE risk in acute DVT patients. Its convolutional architecture and residual modules substantially enhance predictive performance.
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Tian et al. (2025) conducted an observational in Acute lower extremity deep vein thrombosis (n=424). PE-Mind deep learning prediction model vs. Other machine learning models (LGBM, XGB, CatBoost, RF, MLP) was evaluated on Accuracy for predicting pulmonary embolism on the prospective test set. The PE-Mind deep learning model achieved an accuracy of 78.26% and an AUC of 0.8641 on the prospective test set, outperforming other machine learning models in predicting pulmonary embolism risk.
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