Improving Liver Fibrosis Diagnosis in Primary Care Using FibroX AI
NCT ID: NCT07305324
Last Updated: 2025-12-26
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
NA
40 participants
INTERVENTIONAL
2026-06-15
2027-06-15
Brief Summary
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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.
Detailed Description
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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:
* FibroX-Enabled Care: Providers will receive FibroX's risk probability, triage band, and explainability panel.
* Usual Care: Providers will use standard labs and vitals, with optional access to the FIB-4 calculator.
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
* Feasibility: Recruitment rate ≥70%, completion rate ≥85%, median decision time ≤3.5 minutes.
* Usability and Acceptability: System Usability Scale (SUS) score ≥70.
* Provider Trust: AI-Trust Scale score ≥6.
* Effectiveness: Within-provider diagnostic accuracy for significant fibrosis (≥F2) and clinically significant portal hypertension.
Secondary Outcomes
* Appropriate referral rates
* Net reclassification improvement (NRI)
* Calibration metrics (intercept, slope)
* Provider confidence and cognitive load (NASA-TLX)
* Intended downstream testing burden
* Adoption and fidelity to triage recommendations
* Override rates and reasons
* Fairness analysis across subgroups (age, sex, BMI, race/ethnicity)
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.
Conditions
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Keywords
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Study Design
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RANDOMIZED
CROSSOVER
DIAGNOSTIC
NONE
Study Groups
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FibroX-Enabled Care
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.
FibroX
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.
Usual Care
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.
Usual Care
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.
Interventions
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FibroX
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.
Usual Care
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.
Eligibility Criteria
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Inclusion Criteria
* 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
Exclusion Criteria
* 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
18 Years
ALL
No
Sponsors
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Yale University
OTHER
Responsible Party
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Basile Njei
Associate Director (Bioinformatics), Yale Liver Center
Central Contacts
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References
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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Related Links
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FibroX WebApp
Other Identifiers
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2000027433
Identifier Type: -
Identifier Source: org_study_id