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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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1506363/full |
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| author | Yu Tian Yu Tian Jingjie Liu Shan Wu Yucong Zheng Rongye Han Qianhui Bao Lei Li Lei Li Tao Yang |
| author_facet | Yu Tian Yu Tian Jingjie Liu Shan Wu Yucong Zheng Rongye Han Qianhui Bao Lei Li Lei Li Tao Yang |
| author_sort | Yu Tian |
| collection | DOAJ |
| description | 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 limitations of current clinical tools and provide a more targeted risk evaluation solution.MethodsWe 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.ResultsPE-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.ConclusionThe 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. |
| format | Article |
| id | doaj-art-bddd08afa1f342a5b4173b4d7dff9b2b |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-bddd08afa1f342a5b4173b4d7dff9b2b2025-02-06T07:10:15ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-02-011210.3389/fmed.2025.15063631506363Development and validation of a deep learning-enhanced prediction model for the likelihood of pulmonary embolismYu Tian0Yu Tian1Jingjie Liu2Shan Wu3Yucong Zheng4Rongye Han5Qianhui Bao6Lei Li7Lei Li8Tao Yang9Vascular Surgery Department, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, ChinaSchool of Clinical Medicine, Tsinghua University, Beijing, ChinaInstitute of Cardiovascular Diseases, The First Affiliated Hospital of Dalian Medical University, Dalian, ChinaRadiology Department, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, ChinaRadiology Department, Tsinghua University Hospital, Tsinghua University, Beijing, ChinaClinical Laboratory Department, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, ChinaSchool of Clinical Medicine, Tsinghua University, Beijing, ChinaSchool of Clinical Medicine, Tsinghua University, Beijing, ChinaVascular Department, Beijing Hua Xin Hospital (1st Hospital of Tsinghua University), Beijing, ChinaVascular Surgery Department, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, ChinaBackgroundPulmonary 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.MethodsWe 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.ResultsPE-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.ConclusionThe 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.https://www.frontiersin.org/articles/10.3389/fmed.2025.1506363/fullpulmonary embolismdeep learningdeep venous thrombosisrisk assessmentsclinical tool |
| spellingShingle | Yu Tian Yu Tian Jingjie Liu Shan Wu Yucong Zheng Rongye Han Qianhui Bao Lei Li Lei Li Tao Yang Development and validation of a deep learning-enhanced prediction model for the likelihood of pulmonary embolism Frontiers in Medicine pulmonary embolism deep learning deep venous thrombosis risk assessments clinical tool |
| title | Development and validation of a deep learning-enhanced prediction model for the likelihood of pulmonary embolism |
| title_full | Development and validation of a deep learning-enhanced prediction model for the likelihood of pulmonary embolism |
| title_fullStr | Development and validation of a deep learning-enhanced prediction model for the likelihood of pulmonary embolism |
| title_full_unstemmed | Development and validation of a deep learning-enhanced prediction model for the likelihood of pulmonary embolism |
| title_short | Development and validation of a deep learning-enhanced prediction model for the likelihood of pulmonary embolism |
| title_sort | development and validation of a deep learning enhanced prediction model for the likelihood of pulmonary embolism |
| topic | pulmonary embolism deep learning deep venous thrombosis risk assessments clinical tool |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1506363/full |
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