使用数字眼神双胞胎模拟放射科医生的认知过程,以增强放射学培训
Akash Awasthi1,2, Anh Mai Vu3, Ngan Le4
1Department of Electrical and Computer Engineering, University of Houston, Houston, USA. akashcseklu123@gmail.com.
Scientific reports
|April 21, 2025
概括
一个新的系统MedGaze,通过模拟认知过程,预测放射科医生在胸部X射线上的目光行为. 这种人工智能工具通过模拟专家视觉搜索模式来提高诊断准确度和培训.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 认知科学 认知科学
背景情况:
- 预测人类的凝视行为对于推进交互式系统和提高医学成像诊断精度至关重要.
- 了解放射科医生在图像解释过程中的认知过程是提高诊断工具的关键.
研究的目的:
- 介绍MedGaze,一个由"数字凝视双胞胎"概念启发的新系统.
- 为了模拟放射科医生的认知过程,并预测胸部X射线 (CXR) 图像中的扫描路径.
- 评估MedGaze的性能与最先进的方法以及其评估临床工作负载的能力.
主要方法:
- MedGaze采用两阶段的培训方法:视觉到放射学报告学习 (VR2) 和视觉语言认知学习 (VLC).
- 该系统将视觉特征与放射学报告相结合,利用像MIMIC这样的大数据集来复制放射科医生的视觉搜索模式.
- 在EGD-CXR和REFLACX数据集上使用IoU,相关系数 (CC) 和多对应得分来评估性能. 通过固定时间和斯皮尔曼等级相关性来评估临床工作量.
主要成果:
- 在EGD-CXR和REFLACX数据集上,MedGaze显著超过了最先进的方法,实现了更高的IoU,CC和Multimatch得分.
- 固定持续时间分析显示与临床工作量有显著的相关性,表明MedGaze在工作量评估中的实用性.
- 人类评估表明,预测的扫描路径与专家模式非常相似,并覆盖了关键诊断区域.
结论:
- MedGaze有效地预测放射科医生对CXR图像的凝视行为,模拟专家视觉搜索模式.
- 该系统为放射科医生的决策提供了宝贵的见解,提高了培训和诊断准确度.
- 通过最大限度地减少冗余和优化诊断工作流程,MedGaze有可能改善临床结果.
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