Artificial intelligence‑based quantitative analysis of hepatic fibrosis in carbon tetrachloride-induced mouse model of metabolic dysfunction-associated steatohepatitis.

Liver fibrosis, a major histopathological indicator of chronic liver injury, is also a key feature of metabolic dysfunction-associated steatohepatitis. Its quantitative assessment in preclinical toxicology is frequently inconsistent and subjective. This study aimed to develop and validate multi-scale, patch-based convolutional neural network classification algorithms for automated fibrosis quantification in a carbon tetrachloride (CCl(4))-induced mouse model. We sought to determine the optimal patch size for accurate predictions. Accordingly, male C57BL/6 mice (n = 19) were categorized into the following three groups: vehicle control (n = 5), high-fat diet (HFD) and CCl(4) positive control (n = 9), and HFD and CCl(4) with elafibranor (ELA) treatment (n = 5). Liver tissues were stained with Sirius-red, digitized as whole slide images, and cropped into patches of 32 × 32, 64 × 64, or 128 × 128 pixels. Each algorithm was trained, validated, and tested in an 8:1:1 ratio over 40 epochs with a batch size of 32 to classify fibrotic, normal, and background regions. All models performed robustly, with validation accuracies exceeding 98% and F1-scores above 0.96. Particularly, the 32 × 32 model exhibited the highest correlation with pathologist's measurements (Spearman's r = 0.9609; p < 0.05) and the most accurate estimation of absolute fibrotic area compared to expert assessments. This model also accurately detected the antifibrotic effects of ELA. These findings establish that the 32 × 32 patch-based classification approach provides a rapid, reproducible, and objective method for liver fibrosis quantification in preclinical toxicology, with strong potential for integration into digital pathology workflows. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s43188-025-00326-8.