A Deep Learning Model Based on High‐Frequency Ultrasound Images for Classification of Different Stages of Liver Fibrosis

作者：L.L. Zhang, Zhengbo Tan, Chunlei Li, Lichao Mou, Yufang Shi, Xiao Xiang Zhu, Yan Luo · 发表于：Liver International · 年份：2025 · DOI：10.1111/liv.70148 · 被引用次数：11 · 研究领域：Liver Disease Diagnosis and Treatment、Liver Disease and Transplantation、Ultrasound Imaging and Elastography

BACKGROUND AND AIMS: To develop a deep learning model based on high-frequency ultrasound images to classify different stages of liver fibrosis in chronic hepatitis B patients. METHODS: This retrospective multicentre study included chronic hepatitis B patients who underwent both high-frequency and low-frequency liver ultrasound examinations between January 2014 and August 2024 at six hospitals. Paired images were employed to train the HF-DL and the LF-DL models independently. Three binary tasks were conducted: (1) Significant Fibrosis (S0-1 vs. S2-4); (2) Advanced Fibrosis (S0-2 vs. S3-4); (3) Cirrhosis (S0-3 vs. S4). Hepatic pathological results constituted the ground truth for algorithm development and evaluation. The diagnostic value of high-frequency and low-frequency liver ultrasound images was compared across commonly used CNN networks. The HF-DL model performance was compared against the LF-DL model, FIB-4, APRI, and with SWE (external test set). The calibration of models was plotted. The clinical benefits were calculated. Subgroup analysis for patients with different characteristics (BMI, ALT, inflammation level, alcohol consumption level) was conducted. RESULTS: The HF-DL model demonstrated consistently superior diagnostic performance across all stages of liver fibrosis compared to the LF-DL model, FIB-4, APRI and SWE, particularly in classifying advanced fibrosis (0.93 [95% CI 0.90-0.95], 0.93 [95% CI 0.89-0.96], p < 0.01). The HF-DL model demonstrates significantly ...