Ignite Creation Date:
2026-03-26 @ 3:20 PM
Ignite Modification Date:
2026-03-31 @ 12:25 PM
Study NCT ID:
NCT07378202
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2026-01-30
First Post:
2026-01-05
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Machine Learning for Prediction of Therapy Response in Autoimmune Hepatitis
Sponsor:
Hannover Medical School
Organization:
Study Overview
Official Title:
Machine Learning for Prediction of Therapy Response in Autoimmune Hepatitis
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2026-01
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The 5th International Autoimmune Hepatitis Group (IAIHG) research workshop emphasized the integration of large clinical cohorts with artificial intelligence (AI) for enhanced prediction of therapy responses and outcomes in Autoimmune Hepatitis (AIH). This project aims to develop and validate machine learning (ML) models using data from the R-Liver registry and other international cohorts. After rigorous preprocessing to ensure data uniformity and quality, the investigators will identify and characterize factors influencing therapy response. They will then implement ML models to predict complete biochemical response (CBR) at 6 and 12 months, using five-fold cross-validation, and validate these models in external cohorts from Spain, Canada, and the international AIH group, ensuring robustness and generalizability.
Finally, the investigators will prospectively validate the models in newly registered cases, assessing both short-term and long-term outcomes. This project seeks to advance personalized treatment strategies in AIH, facilitating timely adjustments in therapy and improving patient prognosis through AI-driven decision support. This projects' interdisciplinary team, with expertise in clinical AI and hepatology, is well-equipped to address these challenges and enhance the clinical management of AIH.
Finally, the investigators will prospectively validate the models in newly registered cases, assessing both short-term and long-term outcomes. This project seeks to advance personalized treatment strategies in AIH, facilitating timely adjustments in therapy and improving patient prognosis through AI-driven decision support. This projects' interdisciplinary team, with expertise in clinical AI and hepatology, is well-equipped to address these challenges and enhance the clinical management of AIH.
Detailed Description:
None
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: