MASLDNCT07305324
Improving Liver Fibrosis Diagnosis in Primary Care Using FibroX AI
A study testing FibroX in people with MASLD and Fibrosis of Liver, run by Yale University.
Trial 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). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- MASLD, Fibrosis of Liver
- What is being tested?
- FibroX
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 40 participants
- Where
- No sites listed yet
- Study dates
- 15 Jun 2026to 15 Jun 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Validation of an AI Tool for Improving MASLD Advanced Liver Fibrosis Diagnosis in Primary Care: A Provider-Level Crossover Randomized Controlled Trial Pilot
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:
Read more
- 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.
Read the detailed 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:
- 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
- MASLD, Fibrosis of Liver
- Being tested
- FibroX, Usual Care
- 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
- Medical subject headings
- Liver Cirrhosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
FibroXDevice
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 CareOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. Receives: Other: Usual Care |
What is being measured
The primary outcome is the main question the study is designed to answer.
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)
Measured: 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)
Measured: 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)
Measured: 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
Measured: Immediately after each simulation period, up to 24 weeks
Secondary outcomes (9)
Appropriate Referral Rate
Proportion of simulated cases where provider referral decisions (e.g., hepatology referral, VCTE order) align with guideline-concordant triage rules for MASLD risk stratification.
Unit of Measure: Proportion (%)
Measured: Immediately after each simulation period, up to 24 weeks
Net Reclassification Improvement (NRI)
Change in classification accuracy for MASLD risk categories (rule-out, indeterminate, rule-in) when using FibroX compared to usual care.
Unit of Measure: NRI score (unitless)
Measured: Immediately after each simulation period, up to 24 weeks
Calibration of Risk Predictions
Calibration of FibroX predictions compared to observed outcomes, assessed using calibration intercept, slope, and calibration plot.
Unit of Measure: Intercept and slope (unitless)
Measured: Immediately after each simulation period, up to 24 weeks
Provider Confidence in Decision-Making
Provider-reported confidence in MASLD management decisions during simulated cases, measured on a 5-point Likert scale (1 = very low confidence; 5 = very high confidence).
Unit of Measure: Score (range: 1-5; higher scores = greater confidence)
Measured: Immediately after each simulation period, up to 24 weeks
Cognitive Load During Case Review
Provider cognitive workload assessed using NASA Task Load Index (NASA-TLX), which provides an overall workload score from 0 to 100 across six dimensions.
Unit of Measure: Score (range: 0-100; higher scores = greater workload)
Measured: Immediately after each simulation period, up to 24 weeks
Intended Downstream Testing Burden
Number and type of additional tests or referrals (e.g., VCTE, hepatology consult) that providers intend to order after each simulated case.
Unit of Measure: Count (number of tests/referrals per case)
Measured: Immediately after each simulation period, up to 24 weeks
Adoption and Fidelity to Triage Recommendations
Proportion of cases where providers follow FibroX triage recommendations (e.g., rule-out, indeterminate, rule-in) without override.
Unit of Measure: Proportion (%)
Measured: Immediately after each simulation period, up to 24 weeks
Override Rate and Reasons
Proportion of cases where providers override FibroX recommendations and the documented reasons for override.
Unit of Measure: Proportion (%)
Measured: Immediately after each simulation period, up to 24 weeks
Fairness Analysis Across Subgroups
Performance of FibroX (sensitivity, specificity, calibration) across demographic subgroups (age, sex, BMI, race/ethnicity).
Unit of Measure: Proportion (%) and AUROC (unitless)
Measured: Immediately after each simulation period, up to 24 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Dec 2025
- Study start15 Jun 2026 (estimated)
- Primary completion15 May 2027 (estimated)
- Study completion15 Jun 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (14)Papers the sponsor has linked to this study.
- Other trials for MASLDStudies of the same condition, open and under way.
- More trials run by Yale UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Crossover
- Model description
- 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.
- Masking
- None (open label)
- Enrolment
- 40 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Yale University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Basile Njei, Yale University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07305324
- Sponsor's protocol ID
- 2000027433
- Status last verified
- Dec 2025
- First submitted
- 1 Dec 2025
- Last updated
- 26 Dec 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: This pilot study involves simulated case reviews by primary care providers. No patient-level data is collected, and there is no current plan to share individual provider-level data with other researchers.
Publications
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.
PubMed 38615137Ratziu 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.
PubMed 15940625Decharatanachart 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. Ther Adv Gastroenterol. 2021 Dec 21;14:17562848211062807. doi: 10.1177/17562848211062807. eCollection 2021.
PubMed 34987607Meng 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.
PubMed 25715361Boursier 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.
PubMed 21898504Wong 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.
PubMed 20101745Yoon 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.
PubMed 25007047Mondal 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.
PubMed 38143613Estes 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.
PubMed 29886156Targher 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.
PubMed 38228377Maher 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.
PubMed 39389571Younossi 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.
PubMed 38727678Njei B, Kanmounye US. An Explainable AI Tool (FibroX) for Detecting Advanced Liver Fibrosis in Adults With Type 2 Diabetes: Protocol for a Pilot Crossover Trial. JMIR Res Protoc. 2026 May 21;15:e90456. doi: 10.2196/90456.
PubMed 41974444
Study at a glance
From the registry record, last updated by the sponsor on 26 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Validation of an AI Tool for Improving MASLD Advanced Liver Fibrosis Diagnosis in Primary Care: A Provider-Level Crossover Randomized Controlled Trial Pilot
- ClinicalTrials.gov ID
- NCT07305324
- Acronym / sponsor ID
- 2000027433
- Conditions
- MASLD, Fibrosis of Liver
- 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
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 40 participants (estimated)
- Lead sponsor
- Yale UniversityUniversity, hospital or other
- Study start
- 15 Jun 2026 (estimated)
- Primary completion
- 15 May 2027 (estimated)
- Study completion
- 15 Jun 2027 (estimated)
- First posted
- 26 Dec 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed 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:
- 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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- 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.
| Arm | Type | Receives |
|---|---|---|
| FibroX-Enabled Care | Experimental | Device: FibroXIn 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. |
| Usual Care | Active comparator | Other: Usual CareIn 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- 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
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| FibroXFibroX 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. | Device | FibroX-Enabled Care |
| Usual CareIn 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. | Other | Usual Care |
Primary outcome
Diagnostic Accuracy for Significant Liver Fibrosis (≥F2) and Clinically Significant Portal Hypertension Using FibroX Compared to Usual Care
Time frame: Immediately after each simulation period, up to 24 weeks
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)
System Usability Scale (SUS) Score for FibroX Integration
Time frame: Immediately after each simulation period, up to 24 weeks
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)
Provider Trust in AI Tool (FibroX)
Time frame: Immediately after the FibroX-enabled simulation period, up to 24 weeks
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)
Median Decision Time per Case
Time frame: Immediately after each simulation period, up to 24 weeks
Median time (in minutes) taken by providers to complete management decisions for simulated MASLD cases using FibroX versus usual care. Unit of Measure: Minutes
9 secondary outcomes
Appropriate Referral Rate
Time frame: Immediately after each simulation period, up to 24 weeks
Net Reclassification Improvement (NRI)
Time frame: Immediately after each simulation period, up to 24 weeks
Calibration of Risk Predictions
Time frame: Immediately after each simulation period, up to 24 weeks
Provider Confidence in Decision-Making
Time frame: Immediately after each simulation period, up to 24 weeks
Cognitive Load During Case Review
Time frame: Immediately after each simulation period, up to 24 weeks
Intended Downstream Testing Burden
Time frame: Immediately after each simulation period, up to 24 weeks
Adoption and Fidelity to Triage Recommendations
Time frame: Immediately after each simulation period, up to 24 weeks
Override Rate and Reasons
Time frame: Immediately after each simulation period, up to 24 weeks
Fairness Analysis Across Subgroups
Time frame: Immediately after each simulation period, up to 24 weeks
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Dec 2025.
- Results publicationNjei, 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.
- Results publication: PubMed 38615137Njei 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.
- Results publication: PubMed 15940625Ratziu 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.
- Results publication: PubMed 34987607Decharatanachart 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. Ther Adv Gastroenterol. 2021 Dec 21;14:17562848211062807. doi: 10.1177/17562848211062807. eCollection 2021.
- Results publication: PubMed 25715361Meng 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.
- Results publication: PubMed 21898504Boursier 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.
- Results publication: PubMed 20101745Wong 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.
- Results publication: PubMed 25007047Yoon 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.
- Results publication: PubMed 38143613Mondal 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.
- Results publication: PubMed 29886156Estes 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.
- Results publication: PubMed 38228377Targher 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.
- Results publication: PubMed 39389571Maher 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.
- FibroX WebAppLink provided by the sponsor.
- Individual participant dataSharing planned: No. This pilot study involves simulated case reviews by primary care providers. No patient-level data is collected, and there is no current plan to share individual provider-level data with other researchers.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
A study testing FibroX in people with MASLD and Fibrosis of Liver, run by Yale University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 26 Dec 2025. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Basile Njei, MD, MPH, PhD, FRCP (475-227-5537, basile.njei@yale.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Accuracy for Significant Liver Fibrosis (≥F2) and Clinically Significant Portal Hypertension Using FibroX Compared to Usual Care (time frame: Immediately after each simulation period, up to 24 weeks). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07305324, which also lists 14 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Dec 2025.