ArticleScientific reports2022
Artificial intelligence outperforms standard blood-based scores in identifying liver fibrosis patients in primary care.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Diagnostic Prediction Models for Primary Care, Based on AI and Electronic Health Records: Systematic Review.JMIR medical informatics · 2025Pooled it
- A precision-constrained framework for evaluating noninvasive biomarkers in MASLD and beyond.Nature communications · 2026Article
- Insulin Resistance as a Dynamic Correlate of Fibrosis Status in Chronic Hepatitis B: A Visit-Level Longitudinal Risk Stratification Framework.Life (Basel, Switzerland) · 2026Article
- A comprehensive narrative review of artificial intelligence use in the diagnosis and management of metabolic dysfunction-associated steatotic liver disease.Translational gastroenterology and hepatology · 2026Review
- Optimising non-invasive screening for hepatic fibrosis in people living with HIV and intermediate FIB-4 scores.Frontiers in health services · 2026Article
- Artificial intelligence in hepatopathy diagnosis and treatment: Big data analytics, deep learning, and clinical prediction models.World journal of gastroenterology · 2025Review
- AI and Primary Care: Scoping Review.Journal of medical Internet research · 2025Article
- Machine learning-based disease risk stratification and prediction of metabolic dysfunction-associated fatty liver disease using vibration-controlled transient elastography: Result from NHANES 2021-2023.BMC gastroenterology · 2025Article
- The Perceptions of Potential Prerequisites for Artificial Intelligence in Danish General Practice: Vignette-Based Interview Study Among General Practitioners.JMIR medical informatics · 2025Article
- Machine Learning Models for Predicting Significant Liver Fibrosis in Patients with Severe Obesity and Nonalcoholic Fatty Liver Disease.Obesity surgery · 2024Article
- Esophageal varices detection and bleeding risk assessment with artificial intelligence: a systematic review.iGIE : innovation, investigation and insights · 2024Article
- The impact of hepatic and splenic volumetric assessment in imaging for chronic liver disease: a narrative review.Insights into imaging · 2024Review
- Digital solutions for decision support in general practice - a rapid review focused on systems developed for the universal healthcare setting in Denmark.BMC primary care · 2023Review
- Deep Learning for Computed Tomography Assessment of Hepatic Fibrosis and Cirrhosis: A Systematic Review.Mayo Clinic proceedings. Digital health · 2023Review
- Noninvasive serum biomarkers for liver fibrosis in NAFLD: current and future.Clinical and molecular hepatology · 2023Review
- Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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