基于机器学习的检测肠道转化症识别了临床失踪病例
Archives of pathology & laboratory medicine
|January 22, 2026
概括
人工智能管道可以在活检样本中识别胃肠转化症 (胃癌的前体) 的遗漏病例. 这种人工智能工具还可以准确量化病情,从而有可能改善患者管理.
科学领域:
- 胃肠病学 胃肠病学
- 计算病理学计算病理学
- 在瘤学瘤学.
背景情况:
- 胃肠转化症 (GIM) 是胃癌的一个重要危险因素.
- 准确诊断GIM对于临床决策和患者管理至关重要.
- 当前的诊断方法可能会导致错过诊断.
研究的目的:
- 开发和评估人工智能 (AI) 管道,用于检测错过的GIM诊断.
- 创建自动化方法从病理学数据量化GIM.
- 评估AI在审查整个幻灯片图像和病理学报告方面的表现.
主要方法:
- 一个人工智能管道,包括一个基于补丁的分类器,被开发用于GIM检测.
- 该管道分析了1935年的胃活检样本,将AI发现与临床病理学报告进行比较.
- 三位独立的病理学家审查了不一致的病例; 一个大型语言模型评估了报告文本.
主要成果:
- 人工智能管道在297个审查案例中的30个 (大约1.5%的标本) 中发现了以前未被检测到的GIM.
- 人工智能对GIM的量化显示出与人类得分的强烈一致 (斯皮尔曼相关系数=0.90).
- 一个大型语言模型在对GIM存在的病理学报告进行分类时实现了99.3%的准确性.
结论:
- 基于AI的质量保证管道可以检测错过的GIM,解决诊断缺口.
- 自动化AI分析提供了定量见解,可能有助于GIM管理.
- 人工智能工具在提高GIM诊断的准确性和效率方面表现有前途.
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