AutoFibroNet: A deep learning and multi‐photon microscopy‐derived automated network for liver fibrosis quantification in MAFLD

Summary Background Liver fibrosis is the strongest histological risk factor for liver‐related complications and mortality in metabolic dysfunction‐associated fatty liver disease (MAFLD). Second harmonic generation/two‐photon excitation fluorescence (SHG/TPEF) is a powerful tool for label‐free two‐dimensional and three‐dimensional tissue visualisation that shows promise in liver fibrosis assessment. Aim To investigate combining multi‐photon microscopy (MPM) and deep learning techniques to develop and validate a new automated quantitative histological classification tool, named AutoFibroNet (Automated Liver Fibrosis Grading Network), for accurately staging liver fibrosis in MAFLD. Methods AutoFibroNet was developed in a training cohort that consisted of 203 Chinese adults with biopsy‐confirmed MAFLD. Three deep learning models (VGG16, ResNet34, and MobileNet V3) were used to train pre‐processed images and test data sets. Multi‐layer perceptrons were used to fuse data (deep learning features, clinical features, and manual features) to build a joint model. This model was then validated in two further independent cohorts. Results AutoFibroNet showed good discrimination in the training set. For F0, F1, F2 and F3‐4 fibrosis stages, the area under the receiver operating characteristic curves (AUROC) of AutoFibroNet were 1.00, 0.99, 0.98 and 0.98. The AUROCs of F0, F1, F2 and F3‐4 fibrosis stages for AutoFibroNet in the two validation cohorts were 0.99, 0.83, 0.80 and 0.90 and 1.00, 0.83, 0.80 and 0.94, respectively, showing a good discriminatory ability in different cohorts. Conclusion AutoFibroNet is an automated quantitative tool that accurately identifies histological stages of liver fibrosis in Chinese individuals with MAFLD.