An imaging-based artificial intelligence model for non-invasive grading of hepatic venous pressure gradient in cirrhotic portal hypertension
Qian Yu, Yifei Huang, Xiaoguo Li, Michael Pavlides, Dengxiang Liu, Hongwu Luo, Huiguo Ding, Weimin An, Fuquan Liu, Changzeng Zuo, Chun‐Qiang Lu, Tianyu Tang, Yuancheng Wang, Shan Huang, Chuan Liu, Tianlei Zheng, Kang Ning, Changchun Liu, Jitao Wang, Seray Akçalar, Emrecan Çelebioğlu, Evren Üstüner, Sadık Bilgiç, Fang Qu, Chi-Cheng Fu, Ruiping Zhang, Chengyan Wang, Jingwei Wei, Jie Tian, Necati Örmecı, Zeynep Melekoğlu Ellik, Özgün Ömer Asiller, Shenghong Ju, Xiaolong Qi
Abstract
HVPG model achieves AUCs over 0.80 and outperforms other non-invasive tools for assessing HVPG. The model shows performance improvement in identifying the severity of PHT, which may help non-invasive HVPG primary prophylaxis when transjugular HVPG measurements are not available.