Abstract
BACKGROUND AND AIM: Noninvasive tests (NITs), such as platelet-based indices and ultrasound/MRI elastography, are widely used to assess liver fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD). However, platelet counts are not routinely included in Japanese health check-ups, limiting their utility in large-scale screenings. Additionally, elastography, while effective, is costly and less accessible in routine practice. Most existing AI-based models incorporate these markers, restricting their applicability. This study aimed to develop a simple yet accurate AI model for liver fibrosis staging using only routine demographic and biochemical markers. METHODS: This retrospective study analyzed biopsy-proven data from 463 Japanese MASLD patients. Patients were randomly assigned to training (N = 370, 80%) and test (N = 93, 20%) cohorts. The AI model incorporated age, sex, BMI, diabetes, hypertension, hyperlipidemia, and routine blood markers (AST, ALT, γ-GTP, HbA1c, glucose, triglycerides, cholesterol). RESULTS: The Support Vector Machine model demonstrated high diagnostic performance, with an area under the curve (AUC) of 0.886 for detecting significant fibrosis (≥ F2). The AUCs for advanced fibrosis (≥ F3) and cirrhosis (F4) were 0.882 and 0.916, respectively. Compared to FIB-4, APRI, and FAST score (0.80-0.96), SVM achieved comparable accuracy while eliminating the need for platelet count or elastography. CONCLUSION: This AI model accurately assesses liver fibrosis in MASLD patients without requiring platelet count or elastography. Its simplicity, cost-effectiveness, and strong diagnostic performance make it well-suited for large-scale health screenings and routine clinical use.