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

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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 elastography (TE). LiverAID models appropriately identified patients with significant liver stiffness (> 8 kPa) (AUC of 0.86, 0.89, 0.91, 0.92, 0.92 and 0.94, and NPV ≥ 0.98), and had a significantly superior discriminative ability (p < 0.01) than conventional blood-based indices (AUC = 0.60–0.76). Compared to TE, LiverAID models showed a good ability to rule out significant biopsy-assessed fibrosis stages. Given the ready availability of the required data and the relatively high performance, our artificial intelligence-based models are valuable screening tools that could be used clinically for early identification of patients with asymptomatic chronic liver diseases in primary care.

Original languageEnglish
Article number2914
JournalScientific Reports
Volume12
Number of pages11
ISSN2045-2322
DOIs
Publication statusPublished - 21. Feb 2022

Keywords

  • Adult
  • Artificial Intelligence
  • Asymptomatic Diseases
  • Biomarkers/blood
  • Biopsy
  • Chronic Disease
  • Elasticity Imaging Techniques
  • Female
  • Humans
  • Liver Cirrhosis/diagnosis
  • Male
  • Middle Aged
  • Primary Health Care/methods
  • Prospective Studies

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