Welcome to the official repository for our research on using ultrasound in conjunction with AI models to screen for advanced liver fibrosis. This repository contains the code accompanying our published article.
This project aims to demonstrate the effectiveness of an AI-driven model combined with ultrasound imaging to identify and screen for advanced liver fibrosis. The approach is detailed in our paper, where we discuss the methodology, challenges, and outcomes of integrating AI with traditional imaging techniques.
We encourage other researchers to reference our work and use it as a foundation for further study. If you require additional test datasets or parameters for more robust validation, please contact the corresponding author of the paper.
We are eager to collaborate with more researchers interested in the field of liver fibrosis. If you are working on or planning to start a project in this domain, please feel free to reach out to us for potential collaboration opportunities.
If you find our work useful or incorporate our findings in your research, please cite our paper using the following format:
@article{doi:10.1148/radiol.231461,
author = {Li-Da Chen, Ze-Rong Huang, Hong Yang, Mei-Qing Cheng, Hang-Tong Hu, Xiao-Zhou Lu, Ming-De Li, Rui-Fang Lu, Dan-Ni He, Peng Lin, Qiu-Ping Ma, Hui Huang, Si-Min Ruan, Wei-Ping Ke, Bing Liao, Bi-Hui Zhong, Jie Ren, Ming-De Lu, Xiao-Yan Xie, Wei Wang},
title = {US-based Sequential Algorithm Integrating an AI Model for Advanced Liver Fibrosis Screening},
journal = {Radiology},
volume = {311},
number = {1},
pages = {e231461},
year = {2024},
doi = {10.1148/radiol.231461},
URL = {https://pubs.rsna.org/doi/abs/10.1148/radiol.231461},
eprint = {https://pubs.rsna.org/doi/pdf/10.1148/radiol.231461}
}