在数字化胃活检上基于深度学习检测和量化肠道代谢:多专家对比研究
Fabian Cano1, Mauricio Caviedes1, Andres Siabatto1
1Computer Imaging and Medical Applications Laboratory-Cim@lab, Universidad Nacional de Colombia, Bogotá D.C., Colombia.
Scientific reports
|February 25, 2026
概括
这项研究引入了一种深度学习方法,可以在胃活检中自动检测肠道代谢,提高对胃癌风险分层主观视觉评估的准确性.
科学领域:
- 胃肠病学 胃肠病学
- 计算病理学计算病理学
- 人工智能在医学中的应用
背景情况:
- 目前的胃癌 (GCa) 风险评估依赖于肠道代谢的视觉估计,这是主观的,容易出现错误.
- 精确量化肠道代谢对于分层GCa风险和指导患者管理至关重要.
研究的目的:
- 开发和验证一种自动化的深度学习方法,用于检测和量化胃粘膜中的肠道代谢.
- 将深度学习模型的性能与经验丰富的病理学家的视觉估计进行比较.
主要方法:
- 深层卷积神经网络受过训练,从两个队伍的胃组织病理学图像中对肠道代谢形成进行分类.
- 该模型的预测被用来估计肠道代谢的百分比,并分配一个适应的OLGIM阶段.
- 通过将模型预测与三个病理学家的盲目评估进行比较来评估性能,使用F1-Score,AUC和Fleiss' Kappa等指标.
主要成果:
- 最好的深度学习模型实现了肠道转化症的高分类性能 (F1-Score [公式:见文本],AUC [公式:见文本]).
- 病理学家观察者之间的一致性是中度的 (弗莱斯卡帕0.200.48),表明了显著的主观性.
- 深度学习模型与病理学家之间的一致性低于病理学家间的一致性 (0.120.35),这突显了该模型的一致,客观的输出.
结论:
- 深度学习模型显示出可靠和客观地检测和量化肠道代谢的巨大潜力.
- 使用深度学习的自动化分析可以减少GCa风险分层的主观性,比目前的视觉估计方法提供更一致的方法.
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