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 ( = 370, 80%) and test ( = 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.