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OpenTrials
Completed

NCT Number: NCT05532345

Discrimination of DILI and AIH by Artificial Intelligence

A retrospective, multi-center, non-interventional cohort study has been going to explore whether artificial intelligence can discriminate Drug-induced liver injury and Autoimmune hepatitis.

A machine learning-based tool will be developed and validated to help clinicians to differentiate between Drug-induced liver injury and Autoimmune hepatitis

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Key information

About this study

Research Objectives:

  • To develop a machine learning-based model from retrospective data.
  • To validate the machine learning-based model from internal dataset and external datasets nationwide.
  • To setup a website or application based on the above model to discriminate Drug-induced liver injury and Autoimmune hepatitis.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Drug-induced liver injury
  • RUCAM ≥6 and met one of the following biochemical conditions: a.) ALT≥5 ULN, b.) or ALP ≥2 ULN, iii) or ALT≥3 ULN and TBil≥2 ULN.
  • RUCAM was between 3-5, the medical records were further reviewed by the three authors to determine the eligibility.
  • Autoimmune hepatitis
  • The revised International Autoimmune Hepatitis Group (IAIHG) diagnostic score≥6 points.
  • Liver biopsy available, which is compatible with typical features of AIH.
  • If liver biopsy was unavailable, patients who achieved biochemical resolution after sustained immunosuppressive therapy.

Exclusion criteria

  • Drug-induced liver injury
  • Hepatotropic viral infection: hepatitis A, B, C, D and E.
  • Non-hepatotropic viral infection: cytomegalovirus (CMV) and Epstein-Barr virus (EBV), etc.
  • Hypoxic ischemic hepatitis and congestive liver disease.
  • Alcohol consumption: male >40g/d, female >20g/d, and ≥5 years.
  • Biliary obstruction, primary biliary cholangitis; primary sclerosing cholangitis.
  • Autoimmune hepatitis.
  • Parasitic infection.
  • Sepsis.
  • Previous liver transplantation or bone marrow transplantation.
  • Pregnancy or lactation.
  • Genetic and metabolic liver diseases.
  • Autoimmune hepatitis
  • Hepatotropic viral infection: hepatitis A, B, C, D and E.
  • Non-hepatotropic viral infection: cytomegalovirus (CMV) and Epstein-Barr virus (EBV), etc.
  • Hypoxic ischemic hepatitis and congestive liver disease.
  • Alcohol consumption: male >40g/d, female >20g/d, and ≥5 years.
  • Biliary obstruction, primary biliary cholangitis; primary sclerosing cholangitis.
  • Drug-induced liver injury.
  • Parasitic infection.
  • Sepsis.
  • Previous liver transplantation or bone marrow transplantation.
  • Pregnancy or lactation.
  • Genetic and metabolic liver diseases.

Treatment and study plan

Primary outcomes

  1. Accuracy of the model in the differential diagnosis of DILI and AIH

    Time frame: May 31, 2023

    The ratio of the correct number of forecasts to the total number of forecasts

  2. The confidence of the model in the differential diagnosis of DILI and AIH

    Time frame: May 31, 2023

    The confidence and 95% confidence internal of the model in determining whether each case is DILI or AIH

Sponsors and collaborators

Lead sponsor

Beijing Friendship Hospital

Other

Registry information

Official study title

Development and Validation of a Machine Learning Model to Differentiate Drug-induced Liver Injury and Autoimmune Hepatitis

Important dates

Study start
2022
Primary completion
2023
Study completion
2023
First posted
Sep 8, 2022
Registry last updated
Jul 6, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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