A Deep Learning Method Enables Quantitative and Automatic Measurement of Rat Liver Histology in NAFLD

Abstract

Nonalcoholic fatty liver disease (NAFLD) is a prevalent liver disorder affecting approximately 25.2% of the global population, posing risks of liver fibrosis, cancer, and metabolic disturbances. Despite its increasing prevalence, many facets of NAFLD's pathogenesis remain elusive, and there are currently no approved therapeutic drugs, underscoring the critical need for a comprehensive understanding of its pathophysiology to enable early diagnosis and intervention. Experimental animal studies play a pivotal role in elucidating the mechanisms underlying NAFLD and in the exploration of novel pharmacotherapies. Despite the widespread integration of deep learning techniques in human histopathology, their application to scrutinize histological features in animal models warrants exploration. This study presents a pioneering NAFLD assessment system leveraging IFNet and ResNet34 architectures. This automated system adeptly identifies inflammatory cell foci and hepatic steatosis in histopathology sections of rat livers. Remarkably, our approach achieved an impressive 95.6% accuracy in the assessment of hepatic steatosis and 77.7% in the evaluation of inflammation cell foci. By introducing a novel histopathology scoring system, our methodology mitigated subjective variations inherent in traditional pathologist evaluations, concurrently streamlining time and labor costs. This system ensured a standardized and precise assessment of rat liver histology in NAFLD and represented a significant stride toward enhancing the efficiency and objectivity of experimental outcomes.