Background: Liver fibrosis is a progressive pathological process resulting from chronic liver injury caused by viral hepatitis, alcohol-related liver disease, non-alcoholic fatty liver disease (NAFLD), autoimmune hepatitis, and metabolic disorders. Histopathological examination of liver biopsy remains the reference standard for fibrosis staging; however, interpretation is subject to interobserver variability. Recent advances in machine learning (ML) and digital pathology have enabled automated histological classification with improved consistency and diagnostic accuracy.

Objective: To evaluate the effectiveness of machine learning algorithms in histological classification of liver fibrosis and correlate algorithmic predictions with conventional pathological staging.

Materials and Methods: A prospective observational study included 220 digitized liver biopsy specimens collected over two years. Whole-slide images were annotated by expert pathologists and classified according to the METAVIR fibrosis scoring system (F0–F4). Machine learning models including Random Forest, Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Gradient Boosting were trained using extracted histomorphological features. Performance was evaluated using accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve (AUC).

Results: Among 220 liver biopsies, chronic viral hepatitis accounted for 42.3% of cases, NAFLD for 31.8%, alcoholic liver disease for 16.4%, and autoimmune liver disease for 9.5%. CNN demonstrated the highest classification performance with an overall accuracy of 96.2%, sensitivity of 95.4%, specificity of 97.3%, and AUC of 0.98. Machine learning significantly reduced interobserver variability and improved recognition of early fibrosis.

Conclusion: Machine learning-based histological assessment provides rapid, reproducible, and highly accurate classification of liver fibrosis. Integration of artificial intelligence with digital pathology has substantial potential to improve routine diagnostic workflows and precision hepatology.