ObjectiveTo develop a recognition model for the pathological features of non-alcoholic fatty liver disease (NAFLD) based on artificial intelligence (AI) algorithm, and to explore whether the model can recognize and visualize the pathological features such as steatosis cells, inflammatory cells, and fibrosis.MethodsSixty-five hematoxylin-eosin (H-E) stained and 65 picrosirius red stained pathological sections of liver tissues of 65 NAFLD mice were selected, and digital pathological sections were obtained by digital scanning. For H-E stained sections, 2 images of the lesion site were taken after 200, 300 and 400 times magnification using CaseViewer 2.3 software, and 390 pathological images of steatosis cells and 390 pathological images of inflammatory cells were obtained; the images were uploaded to the Horizope annotation platform for manual annotation, and then 2 340 images of steatosis cells and 2 340 images of inflammatory cells were obtained after data enhancement; and they were divided into training set, validation set and test set according to 4∶1∶1, of which training set (1 560 images) and validation set (390 images) were used for learning training and parameter iteration of U-Net deep learning model, and test set (390 images) was used for identification and analysis. Dice's similarity coefficient (DSC), mean intersection over union (MIoU), mean accuracy (MA), and sensitivity were used to evaluate the performance of the model. For the picrosirius red stained sections, CaseViewer 2.3 software was used to perform full field interception after 50 times magnification, and color feature extraction algorithm was used to identify fibrosis. Artificial NAFLD activity score (NAS) and machine score were performed on 130 digital pathological sections, and the proportion of fatty degeneration cell area (PFA), density of inflammatory cell (DIC), and ratio of fibrotic area (RFA) were calculated and analyzed.ResultsThe DSC, MIoU, MA and sensitivity of the NAFLD pathological feature recognition model based on AI algorithm were 0.87, 0.80, 0.88 and 0.84 for identifying steatosis cells, respectively; and 0.84, 0.78, 0.85 and 0.80 for identifying inflammatory cells, respectively. The PFA, DIC and RFA of the 65 digital pathological sections were 0.371 (0.013-0.743), 288 (19-894)/mm² and 0.048 5±0.025 4, respectively. PFA, DIC and RFA were all positively correlated with the machine score and artificial NAS score (r_s＝0.953 and 0.928, 0.883 and 0.869, and 0.887 and 0.749, all P < 0.001).ConclusionNAFLD pathological feature recognition model based on AI algorithm has good performance, and it can help pathologists identifying pathological features, so as to improve recognition efficiency and accuracy.