构建用于溃疡性结肠炎实时诊断的AI辅助工具
Building an AI Support Tool for Real-time Ulcerative Colitis Diagnosis
AI总结：
针对溃疡性结肠炎内镜诊断中MES评分观察者间差异大的问题，提出基于机器学习的实时MES分类辅助系统，完成了非临床测试与首例真实患者临床测试，可辅助医生内镜决策。
摘要：
溃疡性结肠炎（Ulcerative Colitis, UC）是一种慢性炎症性肠病，会通过血便、腹痛等症状降低患者生活质量。内镜检查是UC诊断与病情监测的核心手段，Mayo内镜下亚评分（Mayo endoscopic subscore, MES）指数是内镜评估中衡量UC严重程度的标准。然而MES存在较高的观察者间差异，会导致误诊与治疗效果不佳。我们提出使用基于机器学习的MES分类系统来辅助内镜检查流程，缓解观察者差异问题。该系统可在临床环境中实时运行，在内镜检查过程中辅助医生决策。本项目报告概述了系统的设计、构建与评估流程，介绍了我们的初步评估：结合了标准非临床模型测试，以及该系统在真实患者身上开展的首次临床测试。
英文摘要：
Ulcerative Colitis (UC) is a chronic inflammatory bowel disease decreasing life quality through symptoms such as bloody diarrhoea and abdominal pain. Endoscopy is a cornerstone of diagnosis and monitoring of UC. The Mayo endoscopic subscore (MES) index is the standard for measuring UC severity during endoscopic evaluation. However, the MES is subject to high inter-observer variability leading to misdiagnosis and suboptimal treatment. We propose using a machine-learning based MES classification system to support the endoscopic process and to mitigate the observer-variability. The system runs real-time in the clinic and augments doctors' decision-making during the endoscopy. This project report outlines the process of designing, creating and evaluating our system. We describe our initial evaluation, which is a combination of a standard non-clinical model test and a first clinical test of the system on a real patient.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
