Development and Evaluation of Ultrasound Image Learning Pipelines for MASLD Risk Stratification

2026-09-03

AI Summary

研究开发并评估了基于B模式和剪切波弹性成像(SWE)的超声图像学习流程，以改善代谢功能障碍相关脂肪肝病(MASLD)的风险分层，SWE方法在纤维化分期上优于B模式。

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD) affects approximately 30% of the general population. Ultrasound-based imaging, including B-mode imaging and shear wave elastography (SWE), is widely used for noninvasive fibrosis assessment; however, the role of deep learning-based ultrasound image learning for MASLD risk stratification remains insufficiently characterized. In this study, we developed and evaluated ultrasound image learning pipelines using B-mode and SWE images for fibrosis staging and identification of patients with at-risk metabolic dysfunction-associated steatohepatitis (MASH). A total of 250 ultrasound examinations, one exam per subject, were included. Model performance was evaluated using 3-fold cross-validation with area under the receiver operating characteristic curve (AUROC). End-to-end SWE image learning achieved performance comparable to operator-guided SWE across fibrosis stages. Overall, SWE-based learning consistently outperformed B-mode image learning in fibrosis staging, with AUROC improvements from 0.64 (95%CI: [0.56, 0.72]) to 0.72 (95% CI: [0.65, 0.79]) for F>=2 (significant fibrosis, p=0.11), from 0.67 (95%CI: [0.58, 0.75]) to 0.78 (95% CI:[0.72, 0.85]) for F>=3 (advanced fibrosis, p=0.02), and from 0.69 (95%CI: [0.56, 0.82]) to 0.80 (95%CI: [0.72, 0.89]) for F4 (cirrhosis, p=0.10). These findings highlight the potential of SWE image learning for MASLD risk stratification.

Problem

• MASLD
• ultrasound imaging
• risk stratification
• fibrosis assessment
• SWE

Innovation

• Ultrasound Image Learning
• Shear Wave Elastography (SWE)
• Fibrosis Staging
• MASLD Risk Stratification
• End-to-End SWE Image Learning