医学专家和非专家读者在使用PET图像的强制选择病变检测任务中表现一致
Craig K Abbey1, Sangtae Ahn2, Muhan Shao2
1Department of Psychological and Brain Sciences, University of California, Santa Barbara, California, USA.
Medical physics
|October 23, 2025
概括
医学专家和非专家在简单的PET图像检测任务中表现相似. 这表明,非医疗观察员可以用于训练人工智能模型,如深度学习模型观察员 (DLMO),用于图像质量评估.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 观察者的表现 观察者的表现
背景情况:
- 标记数据对于在医学成像中培训和验证深度学习模型观察员 (DLMO) 和线性模型观察员 (CHO) 是至关重要的.
- 临床阅读器通常难以访问数据注释.
- 对于医学成像中简单检测任务的临床专业知识的必要性仍然不清楚.
研究的目的:
- 评估非医疗观察员的使用,以培训DLMOs和CHO在简单的检测任务.
- 使用PET图像,比较核医学医生的表现与医学上天真的读者.
主要方法:
- 采用两种替代性强制选择任务,对 PET 图像进行模拟低对比度病变.
- 读者组包括经过董事会认证的核医学医生和非医生读者.
- 深度学习模型观察员 (CNN,CNN-SwinT) 和道化Hotelling观察员 (CHO) 在专家和非专家数据上接受了培训,以预测专家的表现.
主要成果:
- 专家和非专家读者之间在一致性或科恩的卡帕中没有发现显著差异.
- 读者专业知识显示边际显著性 (p=0.07),与非显著的相互作用.
- 经过专家和非专家数据培训的DLMO和CHO在预测专家读者方面表现相似.
结论:
- 在这些简单的检测任务中,没有观察到医学专家和非专家之间的实质性绩效差异.
- 在特定的简单图像检测场景中培训模型可能不需要临床经验.
- 非医疗观察员可能会替代医生在训练人工智能模型的某些图像分析任务.
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