Artificial intelligence for the noninvasive diagnosis of clinically significant portal hypertension
Cirrhosis is frequently associated with portal hypertension (PH), which can result in severe complications, including varices, ascites, and hepatic encephalopathy. The gold standard for diagnosing PH is the hepatic venous pressure gradient; however, its invasive nature necessitates the exploration o...
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| Language: | English |
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Elsevier
2025-06-01
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| Series: | EngMedicine |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2950489925000156 |
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| author | Zheyu Du Ling Yang Hongliang He Xiaofeng Wu Xiaolong Qi Yudong Zhang |
| author_facet | Zheyu Du Ling Yang Hongliang He Xiaofeng Wu Xiaolong Qi Yudong Zhang |
| author_sort | Zheyu Du |
| collection | DOAJ |
| description | Cirrhosis is frequently associated with portal hypertension (PH), which can result in severe complications, including varices, ascites, and hepatic encephalopathy. The gold standard for diagnosing PH is the hepatic venous pressure gradient; however, its invasive nature necessitates the exploration of noninvasive diagnostic alternatives. Imaging techniques such as ultrasound, computed tomography, and magnetic resonance imaging are frequently employed but encounter challenges in the early detection of clinically significant portal hypertension (CSPH). Recent advances in artificial intelligence (AI), particularly machine and deep learning, have provided promising solutions. AI can analyze complex medical images and enhance diagnostic accuracy by identifying early indicators of PH. Techniques such as radiomics and vascularomics have demonstrated high efficacy in predicting CSPH, thereby improving noninvasive assessment. AI integration with multimodal data may yield more comprehensive, precise, and noninvasive diagnostic tools, facilitating early detection and enhancing the treatment of cirrhosis-related complications. |
| format | Article |
| id | doaj-art-e6f0dc023f604c9eb614deac44cffc20 |
| institution | OA Journals |
| issn | 2950-4899 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EngMedicine |
| spelling | doaj-art-e6f0dc023f604c9eb614deac44cffc202025-08-20T01:51:48ZengElsevierEngMedicine2950-48992025-06-012210006910.1016/j.engmed.2025.100069Artificial intelligence for the noninvasive diagnosis of clinically significant portal hypertensionZheyu Du0Ling Yang1Hongliang He2Xiaofeng Wu3Xiaolong Qi4Yudong Zhang5Imaging Science and Technology Laboratory, School of Computer Science and Technology, Southeast University, Nanjing, ChinaLiver Disease Center of Integrated Traditional Chinese and Western Medicine, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Nanjing, China; Basic Medicine Research and Innovation Center of Ministry of Education, Zhongda Hospital, Southeast University, State Key Laboratory of Digital Medical Engineering, Nanjing, ChinaState Key Laboratory of Digital Medical Engineering, Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, ChinaState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaLiver Disease Center of Integrated Traditional Chinese and Western Medicine, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Nanjing, China; Basic Medicine Research and Innovation Center of Ministry of Education, Zhongda Hospital, Southeast University, State Key Laboratory of Digital Medical Engineering, Nanjing, ChinaImaging Science and Technology Laboratory, School of Computer Science and Technology, Southeast University, Nanjing, China; Corresponding author. School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China.Cirrhosis is frequently associated with portal hypertension (PH), which can result in severe complications, including varices, ascites, and hepatic encephalopathy. The gold standard for diagnosing PH is the hepatic venous pressure gradient; however, its invasive nature necessitates the exploration of noninvasive diagnostic alternatives. Imaging techniques such as ultrasound, computed tomography, and magnetic resonance imaging are frequently employed but encounter challenges in the early detection of clinically significant portal hypertension (CSPH). Recent advances in artificial intelligence (AI), particularly machine and deep learning, have provided promising solutions. AI can analyze complex medical images and enhance diagnostic accuracy by identifying early indicators of PH. Techniques such as radiomics and vascularomics have demonstrated high efficacy in predicting CSPH, thereby improving noninvasive assessment. AI integration with multimodal data may yield more comprehensive, precise, and noninvasive diagnostic tools, facilitating early detection and enhancing the treatment of cirrhosis-related complications.http://www.sciencedirect.com/science/article/pii/S2950489925000156Clinically significant portal hypertensionCirrhosisArtificial intelligenceMachine learningDeep learning |
| spellingShingle | Zheyu Du Ling Yang Hongliang He Xiaofeng Wu Xiaolong Qi Yudong Zhang Artificial intelligence for the noninvasive diagnosis of clinically significant portal hypertension EngMedicine Clinically significant portal hypertension Cirrhosis Artificial intelligence Machine learning Deep learning |
| title | Artificial intelligence for the noninvasive diagnosis of clinically significant portal hypertension |
| title_full | Artificial intelligence for the noninvasive diagnosis of clinically significant portal hypertension |
| title_fullStr | Artificial intelligence for the noninvasive diagnosis of clinically significant portal hypertension |
| title_full_unstemmed | Artificial intelligence for the noninvasive diagnosis of clinically significant portal hypertension |
| title_short | Artificial intelligence for the noninvasive diagnosis of clinically significant portal hypertension |
| title_sort | artificial intelligence for the noninvasive diagnosis of clinically significant portal hypertension |
| topic | Clinically significant portal hypertension Cirrhosis Artificial intelligence Machine learning Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2950489925000156 |
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