Development and validation of a deep learning-enhanced prediction model for the likelihood of pulmonary embolism

Development and validation of a deep learning-enhanced prediction model for the likelihood of pulmonary embolism

BackgroundPulmonary 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...

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Bibliographic Details
Main Authors: Yu Tian, Jingjie Liu, Shan Wu, Yucong Zheng, Rongye Han, Qianhui Bao, Lei Li, Tao Yang
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-02-01
Series:Frontiers in Medicine
Subjects:
pulmonary embolism
deep learning
deep venous thrombosis
risk assessments
clinical tool
Online Access:https://www.frontiersin.org/articles/10.3389/fmed.2025.1506363/full
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