基于大规模计算机视觉模型的超声预测肝硬化失代偿
Ultrasound-Based Prediction of Cirrhosis Decompensation Using Large-Scale Computer Vision Models
- Massachusetts General Hospital（马萨诸塞综合医院）
- Harvard Medical School（哈佛医学院）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本研究提出基于大规模计算机视觉模型的超声成像方法，结合自动化处理与深度学习架构，可在临床恶化前预测肝硬化失代偿，为代偿期患者提供更主动的管理方案。
中文摘要 AI 辅助
失代偿是肝硬化病程中的关键转折点，但临床医生缺乏可靠的非侵入性工具来预测其发作。本研究提出一种新型的基于影像学的方法，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无法捕捉的预测特征。超声具有广泛可及、低成本、适合纵向监测的优势，是可扩展风险分层与长期随访的理想模态。本框架将自动化超声数据处理与现代深度学习架构相结合，在临床恶化发生前识别高失代偿风险患者。该非侵入性策略可作为现有临床评分系统的实用补充，或能对代偿期肝硬化患者开展更早、更主动的管理。
英文摘要
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.
