Improving Liver Fibrosis Diagnosis in Primary Care Using FibroX AI

NCT07305324 | Not Yet Recruiting

• 1 other identifier: 2000027433
• Study Type: interventional
• Target: 40
• Locations: 0 countries
• Sites: N/A

Brief Summary

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:

• Can FibroX improve the accuracy of diagnosing significant liver fibrosis (≥F2) and clinically significant portal hypertension compared to usual care?
• Is FibroX easy to use and acceptable to primary care providers in simulated clinical settings?
• Do providers trust FibroX as a decision-support tool? 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:
• Be primary care providers (MDs, DOs, NPs, PAs) from diverse clinics
• Review simulated patient cases with MASLD risk factors
• Use either usual care tools (standard labs and optional FIB-4 calculator) or FibroX (AI-generated risk score, triage band, and explainability panel)
• Make diagnostic and referral decisions for each case
• Complete surveys on usability, trust in AI, confidence, and cognitive workload 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.

Conditions

Fibrosis of Liver; MASLD

Keywords

Metabolic Dysfunction-Associated Steatotic Liver Disease; Advanced Liver Fibrosis; Primary Care Providers; Explainable AI; Provider-Level Crossover Trial; Simulation-Based Clinical Trial; Pilot Study

Outcome Measures

Primary Outcomes (4)

• Diagnostic Accuracy for Significant Liver Fibrosis (≥F2) and Clinically Significant Portal Hypertension Using FibroX Compared to Usual Care

  Within-provider diagnostic accuracy for detecting significant liver fibrosis (≥F2) and clinically significant portal hypertension in simulated primary care encounters. Accuracy will be assessed using sensitivity, specificity, and AUROC at clinically relevant thresholds. Ground truth for fibrosis stage and portal hypertension will be derived from biopsy, Vibration-Controlled Transient Elastography (VCTE)-based expert consensus, and guideline-defined criteria. Unit of Measure: Proportion (sensitivity and specificity in %, AUROC as a unitless value)

  Immediately after each simulation period, up to 24 weeks

• System Usability Scale (SUS) Score for FibroX Integration

  Usability of FibroX assessed using the System Usability Scale (SUS), a validated 10-item questionnaire scored from 0 to 100, where higher scores indicate better usability. Unit of Measure: Score (range: 0-100; higher scores = better usability)

  Immediately after each simulation period, up to 24 weeks

• Provider Trust in AI Tool (FibroX)

  Provider trust in FibroX assessed using the validated AI-Trust Scale, which includes 12 items scored on a Likert scale. Higher scores indicate greater trust in the AI tool. Unit of Measure: Score (range: 12-60; higher scores = greater trust)

  Immediately after the FibroX-enabled simulation period, up to 24 weeks

• Median Decision Time per Case

  Median time (in minutes) taken by providers to complete management decisions for simulated MASLD cases using FibroX versus usual care. Unit of Measure: Minutes

  Immediately after each simulation period, up to 24 weeks

Secondary Outcomes (9)

• Appropriate Referral Rate

  Immediately after each simulation period, up to 24 weeks

• Net Reclassification Improvement (NRI)

  Immediately after each simulation period, up to 24 weeks

• Calibration of Risk Predictions

  Immediately after each simulation period, up to 24 weeks

• Provider Confidence in Decision-Making

  Immediately after each simulation period, up to 24 weeks

• Cognitive Load During Case Review

  Immediately after each simulation period, up to 24 weeks

Study Arms (2)

FibroX-Enabled Care

EXPERIMENTAL

In this arm, primary care providers use FibroX, an AI-powered clinical decision support tool, to assess simulated patient cases for significant liver fibrosis (≥F2) and clinically significant portal hypertension. FibroX displays a risk probability score, a triage band (rule-out, indeterminate, rule-in), and a one-line explainability panel showing which clinical factors most influenced the prediction. Providers use this information to make diagnostic and referral decisions (e.g., order VCTE, refer to hepatology, initiate guideline-based therapy). Each provider reviews 16 cases during this intervention period. The goal is to evaluate FibroX's impact on diagnostic accuracy, provider trust, usability, and workflow efficiency compared to usual care.

Device: FibroX

Usual Care

ACTIVE COMPARATOR

In this arm, primary care providers assess simulated patient cases using standard clinical tools available in routine practice. These include laboratory results, vital signs, problem lists, medications, and prior imaging. Providers may optionally use the FIB-4 calculator to estimate liver fibrosis risk. Each provider reviews 16 cases during this period. No AI decision support is provided. This arm serves as the comparator to evaluate whether FibroX improves diagnostic accuracy for significant liver fibrosis (≥F2) and clinically significant portal hypertension, as well as provider trust, usability, and workflow efficiency over usual care.

Other: Usual Care

Interventions

FibroX DEVICE

FibroX is an explainable artificial intelligence (AI) tool designed to assist primary care providers in diagnosing significant liver fibrosis (≥F2) and clinically significant portal hypertension in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). It uses routinely available clinical data (e.g., age, AST, ALT, platelets, BMI, HbA1c, creatinine) to generate a risk probability score, a triage band (rule-out, indeterminate, rule-in), and a one-line explainability panel using Shapley Additive Explanations (SHAP). Providers use FibroX during simulated patient encounters to guide diagnostic and referral decisions (e.g., order VCTE, refer to hepatology, initiate guideline-based therapy). The tool aims to improve diagnostic accuracy, increase provider trust, reduce missed diagnoses, and support guideline-concordant triage in primary care.

FibroX-Enabled Care

Usual Care OTHER

In the usual care condition, primary care providers assess simulated patient cases using standard clinical tools available in routine practice. These include laboratory results, vital signs, problem lists, medications, and prior imaging. Providers may optionally use the FIB-4 calculator to estimate liver fibrosis risk. No AI decision support is provided. This intervention serves as the comparator to evaluate whether FibroX improves diagnostic accuracy for significant liver fibrosis (≥F2) and clinically significant portal hypertension, as well as provider trust, decision-making quality, and workflow efficiency compared to usual care.

Usual Care

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Licensed primary care providers (MD, DO, NP, or PA)
• Currently practicing in adult primary care (≥0.5 Full-Time Equivalent)
• Affiliated with one of the participating clinics (academic, community, or Federally Qualified Health Center)
• Willing and able to participate in simulated electronic health record (EHR)-based case reviews
• Able to provide informed consent

You may not qualify if:

• Providers not actively practicing in adult primary care
• Providers with less than 0.5 FTE in clinical practice
• Prior involvement in the development or validation of the FibroX tool
• Inability to complete both simulation periods due to scheduling or other constraints

Sponsors & Collaborators

• Yale University: lead

Related Publications (13)

• Njei, B., et al., FIBROX: an explainable AI model for accurate prediction of advanced liver fibrosis and cardiovascular mortality in MASLD. Gastroenterology, 2024. 169(1): p. S-131-S-132.

  RESULT

• Njei B, Osta E, Njei N, Al-Ajlouni YA, Lim JK. An explainable machine learning model for prediction of high-risk nonalcoholic steatohepatitis. Sci Rep. 2024 Apr 13;14(1):8589. doi: 10.1038/s41598-024-59183-4.

  PMID: 38615137 RESULT

• Ratziu V, Charlotte F, Heurtier A, Gombert S, Giral P, Bruckert E, Grimaldi A, Capron F, Poynard T; LIDO Study Group. Sampling variability of liver biopsy in nonalcoholic fatty liver disease. Gastroenterology. 2005 Jun;128(7):1898-906. doi: 10.1053/j.gastro.2005.03.084.

  PMID: 15940625 RESULT

• Decharatanachart P, Chaiteerakij R, Tiyarattanachai T, Treeprasertsuk S. Application of artificial intelligence in non-alcoholic fatty liver disease and liver fibrosis: a systematic review and meta-analysis. Therap Adv Gastroenterol. 2021 Dec 21;14:17562848211062807. doi: 10.1177/17562848211062807. eCollection 2021.

  PMID: 34987607 RESULT

• Meng F, Zheng Y, Zhang Q, Mu X, Xu X, Zhang H, Ding L. Noninvasive evaluation of liver fibrosis using real-time tissue elastography and transient elastography (FibroScan). J Ultrasound Med. 2015 Mar;34(3):403-10. doi: 10.7863/ultra.34.3.403.

  PMID: 25715361 RESULT

• Boursier J, de Ledinghen V, Zarski JP, Fouchard-Hubert I, Gallois Y, Oberti F, Cales P; multicentric groups from SNIFF 32, VINDIAG 7, and ANRS/HC/EP23 FIBROSTAR studies. Comparison of eight diagnostic algorithms for liver fibrosis in hepatitis C: new algorithms are more precise and entirely noninvasive. Hepatology. 2012 Jan;55(1):58-67. doi: 10.1002/hep.24654.

  PMID: 21898504 RESULT

• Wong VW, Vergniol J, Wong GL, Foucher J, Chan HL, Le Bail B, Choi PC, Kowo M, Chan AW, Merrouche W, Sung JJ, de Ledinghen V. Diagnosis of fibrosis and cirrhosis using liver stiffness measurement in nonalcoholic fatty liver disease. Hepatology. 2010 Feb;51(2):454-62. doi: 10.1002/hep.23312.

  PMID: 20101745 RESULT

• Yoon JH, Lee JM, Joo I, Lee ES, Sohn JY, Jang SK, Lee KB, Han JK, Choi BI. Hepatic fibrosis: prospective comparison of MR elastography and US shear-wave elastography for evaluation. Radiology. 2014 Dec;273(3):772-82. doi: 10.1148/radiol.14132000. Epub 2014 Jul 7.

  PMID: 25007047 RESULT

• Mondal A, Debnath A, Dhandapani G, Sharma A, Lukhmana S, Yadav G. Prevalence of High and Moderate Risk of Liver Fibrosis Among Patients With Diabetes at a Noncommunicable Diseases (NCD) Clinic in a Primary Healthcare Center in Northern India. Cureus. 2023 Nov 23;15(11):e49286. doi: 10.7759/cureus.49286. eCollection 2023 Nov.

  PMID: 38143613 RESULT

• Estes C, Anstee QM, Arias-Loste MT, Bantel H, Bellentani S, Caballeria J, Colombo M, Craxi A, Crespo J, Day CP, Eguchi Y, Geier A, Kondili LA, Kroy DC, Lazarus JV, Loomba R, Manns MP, Marchesini G, Nakajima A, Negro F, Petta S, Ratziu V, Romero-Gomez M, Sanyal A, Schattenberg JM, Tacke F, Tanaka J, Trautwein C, Wei L, Zeuzem S, Razavi H. Modeling NAFLD disease burden in China, France, Germany, Italy, Japan, Spain, United Kingdom, and United States for the period 2016-2030. J Hepatol. 2018 Oct;69(4):896-904. doi: 10.1016/j.jhep.2018.05.036. Epub 2018 Jun 8.

  PMID: 29886156 RESULT

• Targher G, Byrne CD, Tilg H. MASLD: a systemic metabolic disorder with cardiovascular and malignant complications. Gut. 2024 Mar 7;73(4):691-702. doi: 10.1136/gutjnl-2023-330595.

  PMID: 38228377 RESULT

• Maher S, Rajapakse J, El-Omar E, Zekry A. Role of the Gut Microbiome in Metabolic Dysfunction-Associated Steatotic Liver Disease. Semin Liver Dis. 2024 Nov;44(4):457-473. doi: 10.1055/a-2438-4383. Epub 2024 Oct 10.

  PMID: 39389571 RESULT

• Younossi ZM, Mangla KK, Berentzen TL, Grau K, Kjaer MS, Ladelund S, Nitze LM, Coolbaugh C, Hsu CY, Hagstrom H. Liver histology is associated with long-term clinical outcomes in patients with metabolic dysfunction-associated steatohepatitis. Hepatol Commun. 2024 May 10;8(6):e0423. doi: 10.1097/HC9.0000000000000423. eCollection 2024 Jun 1.

  PMID: 38727678 RESULT

Related Links

• FibroX WebApp

MeSH Terms

Conditions

Liver Cirrhosis

Condition Hierarchy (Ancestors)

Liver Diseases; Digestive System Diseases; Fibrosis; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Basile Njei, MD, MPH, PhD, FRCP

CONTACT

475-227-5537; basile.njei@yale.edu

Ulrick S Kanmounye, MD, MPH, MSc

CONTACT

5705404973; ulricksidney@gmail.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Director (Bioinformatics), Yale Liver Center

Model Details: This study uses a provider-level randomized crossover design in simulated clinical settings. Each primary care provider will complete two intervention periods in random order: one using usual care tools (standard labs and optional FIB-4 calculator), and one using FibroX, an AI-based decision support tool. Each period includes 16 simulated patient cases with MASLD risk factors. A one-week washout separates the periods. This crossover model allows within-provider comparison of diagnostic accuracy, usability, and decision-making between FibroX-assisted care and usual care, minimizing inter-provider variability and enhancing internal validity.

Study Record Dates

• First Submitted: December 1, 2025
• First Posted: December 26, 2025
• Study Start (Estimated): June 15, 2026
• Primary Completion (Estimated): May 15, 2027
• Study Completion (Estimated): June 15, 2027
• Last Updated: December 26, 2025

Record last verified: 2025-12

Data Sharing

• IPD Sharing: Will not share