Ultrasound-Based Prediction of Cirrhosis Decompensation Using Large-Scale Computer Vision Models

2026-09-03

AI Summary

本文提出了一种基于大规模计算机视觉模型分析腹部超声图像的方法，以非侵入方式预测肝硬化失代偿的发生，旨在提供比传统实验室评分更有效的风险评估手段。

Abstract

Decompensation represents a critical transition in the course of cirrhosis, yet clinicians have limited non-invasive tools to reliably predict its onset. In this study, we propose a novel imaging-based approach that leverages large-scale computer vision models to analyze routine abdominal ultrasound images and extract predictive features beyond those captured by traditional laboratory-based risk scores. Ultrasound is widely available, low cost, and suitable for longitudinal surveillance, making it an attractive modality for scalable risk stratification and long-term follow-up. Our framework integrates automated ultrasound data processing with modern deep learning architectures to identify patients at high risk of decompensation prior to the occurrence of clinical deterioration. This non-invasive strategy offers a practical complement to existing clinical scoring systems and may enable earlier, more proactive management of patients with compensated cirrhosis.

Problem

• cirrhosis
• decompensation
• ultrasound
• computer vision
• risk prediction

Innovation

• large-scale computer vision models
• ultrasound image analysis
• deep learning architectures
• non-invasive prediction