[1]王小泽,李 肖.从评分到人工智能——经颈静脉肝内门体分流术后肝性脑病预测的演变与挑战[J].介入放射学杂志,2025,34(05):449-455.[doi:10.3969/j.issn.1008-794X.2025.05.001]
 WANG Xiaoze,LI Xiao.Department of Gastroenterology,West China Hospital,et al.From scoring to artificial intelligence:the evolution and challenges in predicting hepatic encephalopathy after TIPS[J].J Intervent Med,2025,34(05):449-455.[doi:10.3969/j.issn.1008-794X.2025.05.001]
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从评分到人工智能——经颈静脉肝内门体分流术后肝性脑病预测的演变与挑战()

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《介入放射学杂志》[ISSN:1008-794X/CN:31-1796/R]

卷:
34
期数:
2025年05
页码:
449-455
栏目:
专论
出版日期:
2025-05-25

文章信息/Info

Title:
From scoring to artificial intelligence:the evolution and challenges in predicting hepatic encephalopathy after TIPS
文章编号:
1008-794X(2025)-005-0449-07
作者:
王小泽 李 肖
610041 四川成都 四川大学华西医院消化内科(王小泽); 国家癌症中心/中国医学科学院北京协和医学院肿瘤医院介入治疗科(李 肖)
Author(s):
WANG XiaozeLI Xiao.Department of GastroenterologyWest China HospitalSichuan UniversityChengduSichuan Province 610041China
Corresponding author:LI Xiao,E-mail:lixiao@cicams.ac.cn
关键词:
经颈静脉肝内门体分流术 肝性脑病 人工智能 预测模型
分类号:
R654.3
DOI:
10.3969/j.issn.1008-794X.2025.05.001
文献标志码:
A
摘要:
该文探讨了经颈静脉肝内门体分流术(TIPS)后肝性脑病(hepatic encephalopathy,HE)的影响因素及预测方法,重点分析了人工智能(AI)技术在预测TIPS术后HE的应用潜力和挑战。具体系统梳理了包括患者基线特征、TIPS相关参数及其他相关因素,并评估传统评分系统如Child-Pugh评分、终末期肝病模型(model for end-stage liver disease,MELD)评分和白蛋白-胆红素(ALBI)评分预测HE的准确性。同时探讨了AI技术如何通过处理多维数据集提高预测准确性,并讨论AI模型在临床应用中的优势与挑战。该文强调构建全面动态预测模型的重要性,以改善TIPS术后患者生活质量和生存率。

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(收稿日期:2024-12-12)
(本文编辑:谷 珂)

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备注/Memo

备注/Memo:
通信作者: 李 肖 E-mail:lixiao@cicams.ac.cn
更新日期/Last Update: 2025-05-25