AutoFibroNet: A deep learning and multi-photon microscopy-derived automated network for liver fibrosis quantification in MAFLD.
Aliment Pharmacol Ther
; 58(6): 573-584, 2023 09.
Article
em En
| MEDLINE
| ID: mdl-37403450
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Hepatopatia Gordurosa não Alcoólica
/
Aprendizado Profundo
Tipo de estudo:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Adult
/
Humans
Idioma:
En
Revista:
Aliment Pharmacol Ther
Assunto da revista:
FARMACOLOGIA
/
GASTROENTEROLOGIA
/
TERAPIA POR MEDICAMENTOS
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China