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.
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More From: Ultraschall in der Medizin - European Journal of Ultrasound
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