弥合差距:计算机辅助检测和山田分类系统与专家的绩效相匹配.
Lin Qiu1, Jian Ding1, Chun-Xiao Lai2
1Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong Province, China.
World journal of gastroenterology
|November 3, 2025
概括
一个新的计算机辅助诊断 (CAD) 系统有效地检测和分类使用山田分类的结直肠多. 这种人工智能工具超越了非专家内镜师,改善了在医疗保健环境中检测和分类的聚.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 胃肠病学 胃肠病学
背景情况:
- 结肠直肠癌 (CRC) 的检测依赖于结肠镜检查.
- 计算机辅助诊断 (CAD) 可以增强多的识别和分类.
- 精确的聚类别对CRC管理至关重要.
研究的目的:
- 开发一个用于多检测和山田分类的CAD系统.
- 为了评估CAD系统的性能与内镜的基准.
主要方法:
- 一个YOLOv7神经网络模型被训练在24,045个多体和72,367个非多体图像上.
- 该系统进行了多体检测和山田分类.
- 使用基于框架和基于图像的评估指标来评估性能.
主要成果:
- 在多检测方面,CAD获得了96.2%的F1分数,超过了内镜师.
- 对于山田分类,CAD获得了80.2%的F1分数,也超过了内镜师.
- 基于图像的评估显示了高准确度 (检测99.2%,分类97.2%).
结论:
- 一个基于深度神经网络的新型CAD系统被开发用于多体检测和山田分类.
- 与非专家内镜医生相比,CAD系统表现出优越的性能.
- 这种CAD系统有潜力改善社区医院的多胞体检测和分类.
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