用超声影像和大模型预测肝硬化失代偿，提前预警风险。
Ultrasound-Based Prediction of Cirrhosis Decompensation Using Large-Scale Computer Vision Models
- 基于大规模视觉模型分析常规腹部超声，提取传统评分忽略的预测特征。
- 在前瞻性队列中实现高风险患者提前预测，为临床干预提供窗口期。
- 适合需要长期随访的肝硬化患者，助力早期干预与个性化管理。
失代偿是肝硬化进程中的关键转折点，但临床缺乏可靠的无创预测工具。本研究提出一种新型影像学方法，利用大规模计算机视觉模型分析常规腹部超声图像，提取传统实验室风险评分未涵盖的预测特征。超声检查广泛可用、成本低，适合长期监测，是实现规模化风险分层与长期随访的理想模态。我们的框架结合自动化超声数据处理与现代深度学习架构，可在临床恶化发生前识别高风险患者。该无创策略可作为现有临床评分系统的实用补充，有望实现对代偿性肝硬化患者的更早、更主动管理。
原文摘要 · Abstract (English)
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.
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