有效的增强智能框架用于膀损伤检测
Okyaz Eminaga1,2, Timothy Jiyong Lee3,4, Mark Laurie3,5
1AI Vobis, Palo Alto, CA.
JCO clinical cancer informatics
|September 29, 2023
概括
开发人工智能用于膀癌的检测是昂贵的. 这项研究表明,在教育图谱上训练有效的深度学习模型可以实现实时的膀损伤识别.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 开发用于膀病变检测的智能系统是昂贵的.
- 需要有效的策略来创建这些系统.
研究的目的:
- 评估深度学习模型对于膀病变检测的有效性.
- 为了确定实时应用的计算效率高的模型.
主要方法:
- 四个深度学习模型 (ConvNeXt,PlexusNet,MobileNet,SwinTransformer) 在一个教育型囊镜图谱 (312张图像) 上受过训练.
- 模型在68个囊镜视频上进行了外部验证,其中有病理确认的感兴趣区域 (ROI).
- 在框架,块和ROI级别上使用特异性和灵敏度来评估性能.
主要成果:
- 在框架 (30.0%-44.8%) 和块级 (56%-67%) 的模型中,特异性是可比的.
- 在区块 (100%) 和ROI (100%) 级别的模型中,灵敏度很高.
- 移动网络和PlexusNet在实时ROI检测方面展示了更高的计算效率.
结论:
- 一个教育性囊透视图集可以帮助开发智能系统.
- 有效的深度学习模型有助于创建实时膀病变检测系统.
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