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The goal of this clinical trial is to learn whether an artificial intelligence (AI) tool called FibroX can help primary care providers better diagnose significant liver fibrosis (≥F2) and clinically significant portal hypertension in adults with metabolic dysfunction-associated steatotic liver disease (MASLD).
The main questions it aims to answer are:
Researchers will compare FibroX-assisted care to usual care to see if FibroX improves diagnostic accuracy, provider trust, and supports better decision-making.
Participants will:
This study will help determine whether FibroX can be integrated into real-world primary care workflows to support earlier and more accurate detection of liver fibrosis and portal hypertension, potentially reducing missed diagnoses, unnecessary referrals, and improving patient outcomes.
Full description
This study is a 12-month pilot clinical trial designed to evaluate the feasibility, usability, provider trust, and preliminary effectiveness of FibroX, an explainable artificial intelligence (AI) tool developed to improve the diagnosis of significant liver fibrosis (≥F2) and clinically significant portal hypertension in adults with metabolic dysfunction-associated steatotic liver disease (MASLD). MASLD is a common and progressive liver condition that can lead to cirrhosis, liver failure, and increased cardiovascular risk. Early detection of these conditions is critical because current guidelines recommend initiating therapy (e.g., resmetirom or semaglutide for ≥F2 fibrosis and beta-blockers for portal hypertension). However, existing tools like FIB-4 often lack accuracy and usability in routine primary care.
FibroX addresses these limitations by using routinely available clinical data-such as age, liver enzymes, platelet count, BMI, and kidney function-to estimate the probability of significant fibrosis and portal hypertension. It provides a triage band (rule-out, indeterminate, rule-in) and a one-line explanation of which clinical factors most influenced the prediction. This transparency is achieved using Shapley Additive Explanations (SHAP), which helps clinicians understand how the AI reached its conclusion.
In retrospective studies, FibroX demonstrated superior diagnostic performance compared to FIB-4 (AUROC 0.97 vs. 0.62) and was associated with long-term mortality risk, suggesting prognostic value beyond diagnostic utility.
This pilot trial will simulate real-world primary care workflows to test whether FibroX can be effectively used by clinicians. The study will recruit 30-40 primary care providers (MDs, DOs, NPs, PAs) from 4-6 diverse clinics. Each provider will participate in two simulation periods, each involving 16 synthetic or de-identified patient cases reflecting adults with MASLD risk factors. Ground truth for fibrosis stage and portal hypertension will be determined by biopsy or expert consensus using Vibration-Controlled Transient Elastography (VCTE) and guideline-based criteria.
Providers will be randomly assigned to review cases in one of two sequences:
After a one-week washout period, providers will switch to the other condition. For each case, providers will make a management decision (e.g., no action, order VCTE, refer to hepatology), record their confidence level, and complete surveys on usability, trust in AI, and cognitive workload.
Primary Outcomes
Secondary Outcomes
All provider actions and decision times will be automatically logged. Post-period surveys and qualitative debriefs will explore barriers and facilitators to using FibroX.
Study Significance This pilot study will generate critical data to support a future multi-center trial and potential integration of FibroX into electronic health records. If successful, FibroX could enable scalable, guideline-concordant screening for significant liver fibrosis and portal hypertension in primary care, reducing missed diagnoses and unnecessary referrals. This aligns with national priorities for precision medicine and responsible AI implementation in healthcare.
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40 participants in 2 patient groups
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Basile Njei, MD, MPH, PhD, FRCP; Ulrick S Kanmounye, MD, MPH, MSc
Data sourced from clinicaltrials.gov
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