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Background & Aims  The evaluation of the stage of liver fibrosis is essential in patients with chronic liver disease. However, due to the low quality of ultrasound images, the non‐invasive diagnosis of liver fibrosis based on ultrasound images is still an outstanding question. This study aimed to investigate the diagnostic accuracy of a deep learning‐based method in ultrasound images for liver fibrosis staging in multicentre patients.    Methods  In this study, we proposed a novel deep learning‐based approach, named multi‐scale texture network (MSTNet), to assess liver fibrosis, which extracted multi‐scale texture features from constructed image pyramid patches. Its diagnostic accuracy was investigated by comparing it with APRI, FIB‐4, Forns and sonographers. Data of 508 patients who underwent liver biopsy were included from 4 hospitals. The area‐under‐the ROC curve (AUC) was determined by receiver operating characteristics (ROC) curves for significant fibrosis (≥F2) and cirrhosis (F4).    Results  The AUCs (95% confidence interval) of MSTNet were 0.92 (0.87‐0.96) for ≥F2 and 0.89 (0.83‐0.95) for F4 on the validation group, which significantly outperformed APRI, FIB‐4 and Forns. The sensitivity and specificity of MSTNet (85.1% (74.5%‐92.0%) and 87.6% (78.0%‐93.6%)) were better than those of three sonographers in assessing ≥F2.    Conclusions  The proposed MSTNet is a promising ultrasound image‐based method for the non‐invasive grading of liver fibrosis in patients with chronic HBV infection.

An ultrasound image‐based deep multi‐scale texture network for liver fibrosis grading in patients with chronic HBV infection