Deep learning radiomics of elastography for diagnosing compensated advanced chronic liver disease: an international multicenter study

Deep learning radiomics of elastography for diagnosing compensated advanced chronic liver disease: an international multicenter study

Abstract Accurate, noninvasive diagnosis of compensated advanced chronic liver disease (cACLD) is essential for effective clinical management but remains challenging. This study aimed to develop a deep learning-based radiomics model using international multicenter data and to evaluate its performanc...

Full description

Saved in:
Bibliographic Details
Main Authors: Xue Lu, Haoyan Zhang, Hidekatsu Kuroda, Matteo Garcovich, Victor de Ledinghen, Ivica Grgurević, Runze Linghu, Hong Ding, Jiandong Chang, Min Wu, Cheng Feng, Xinping Ren, Changzhu Liu, Tao Song, Fankun Meng, Yao Zhang, Ye Fang, Sumei Ma, Jinfen Wang, Xiaolong Qi, Jie Tian, Xin Yang, Jie Ren, Ping Liang, Kun Wang
Format: Article
Language:English
Published: SpringerOpen 2025-08-01
Series:Visual Computing for Industry, Biomedicine, and Art
Subjects:
Deep learning
Elastography
International multicenter study
Compensated advanced chronic liver disease
Online Access:https://doi.org/10.1186/s42492-025-00199-6
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1186/s42492-025-00199-6

Similar Items