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. 2023 Sep;58(6):573-584.
doi: 10.1111/apt.17635. Epub 2023 Jul 4.

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

Huiling Zhan  1 , Siyu Chen  2 , Feng Gao  3 , Guangxing Wang  1 , Sui-Dan Chen  4 , Gangqin Xi  1 , Hai-Yang Yuan  5 , Xiaolu Li  1 , Wen-Yue Liu  6 , Christopher D Byrne  7 , Giovanni Targher  8 , Miao-Yang Chen  9 , Yong-Feng Yang  9 , Jun Chen  10 , Zhiwen Fan  10 , Xitai Sun  11 , Guorong Cai  2 , Ming-Hua Zheng  5   12   13 , Shuangmu Zhuo  1 ; CHESS-MAFLD consortium
Affiliations

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

Huiling Zhan et al. Aliment Pharmacol Ther. 2023 Sep.

Abstract

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.

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REFERENCES

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