Abstract

Background and aims: The gold standard in evaluating fibrosis stage in chronic hepatitis C (CHC) patients is liver biopsy, a costly and invasive procedure. Alternatively, transient elastography (FibroScan®) performs well in identifying severe fibrosis or cirrhosis, but is less accurate in identifying lower degrees of fibrosis. We recently built a predictive model based on artificial intelligence for staging liver fibrosis in CHC patients, using several non-invasive approaches – routine laboratory tests and basic ultrasonographic parameters – and liver stiffness measurement (LSM). The accuracy of the model was 100%. In this paper, our aim was to investigate if it is possible to reach the same accuracy without LSM.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.