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Updated: Jun 5, 2025
Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
Published on: July 21, 2023
An exploratory machine learning model for predicting advanced liver fibrosis in autoimmune hepatitis patients: A
Qinglin Wei1, Wen Li2, Shubei He3
1Department of Gastroenterology, The Seventh Medical Center of Chinese PLA General Hospital, Beijing 100700, China; Department of Cadre Diagnosis and Treatment, The Seventh Medical Center of Chinese PLA General Hospital, Beijing 100700, China; Department of Gastroenterology, Xinqiao Hospital of Third Military Medical University (Army Medical University), Chongqing 400038, China.
Introduction And Objectives:
Advanced fibrosis is a crucial stage in the progression of autoimmune hepatitis (AIH), where fibrosis can either regress or advance. This study aims to leverage machine learning (ML) models for the assessment of advanced liver fibrosis in AIH patients using routine clinical features.
Patients And Methods:
A total of 233 patients diagnosed with AIH and underwent liver biopsy were included in the discovery cohort. The dataset was randomly split into training and testing sets. Patients were categorized into groups with no/minimal/moderate fibrosis and advanced fibrosis. Six ML models were employed to identify the optimal model. Subsequently, the predictive capability of the best ML model was validated in an additional cohort (n = 33) and compared with conventional noninvasive fibrosis scores.
Results:
Three key clinical features, including prothrombin time (PT), albumin (ALB), and ultrasound spleen thickness (UTST), were analyzed by least absolute shrinkage and selection operator (LASSO) regression. In the training set, the random forest (RF) model showed the highest diagnostic performance in predicting advanced fibrosis stage (AUC=0.951). In the testing cohort and validation cohort, the RF model maintained high accuracy (AUC = 0.863 and AUC = 0.843). Additionally, the random forest model outperformed the conventional noninvasive fibrosis scores.
Conclusions:
ML models, particularly the RF model, can help improve the discrimination of advanced liver fibrosis in patients with AIH.
