Artificial intelligence outperforms standard blood-based scores in identifying liver fibrosis patients in primary care.

ABSTRACT: For years, hepatologists have been seeking non-invasive methods able to detect significant liver fibrosis. However, no previous algorithm using routine blood markers has proven to be clinically appropriate in primary care. We present a novel approach based on artificial intelligence, able to predict significant liver fibrosis in low-prevalence populations using routinely available patient data. We built six ensemble learning models (LiverAID) with different complexities using a prospective screening cohort of 3352 asymptomatic subjects. 463 patients were at a significant risk that justified performing a liver biopsy. Using an unseen hold-out dataset, we conducted a head-to-head comparison with conventional methods: standard blood-based indices (FIB-4, Forns and APRI) and transient elastogr

SUBMITTER: Blanes-Vidal V 

PROVIDER: S-EPMC8861108 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature