基于内容的图像检索对通常间歇性肺炎CT模式分类的准确性和读者间一致性的影响
Sohee Park1, Hye Jeon Hwang2, Jihye Yun3
1Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
European radiology
|May 22, 2025
概括
基于内容的图像检索 (CBIR) 略有改善了通常间歇性肺炎 (UIP) CT 模式的分类. 该系统根据读者的经验显示出不同的影响,这表明它最能补充专家的解释.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 肺部医学 肺部医学
背景情况:
- 常见间歇性肺炎 (UIP) 的CT模式分类对于异常性肺纤维化 (IPF) 管理至关重要.
- 准确的UIP分类需要显著的放射学专业知识,并且容易发生读者间的变化.
- 基于内容的图像检索 (CBIR) 系统为图像分析提供了潜在的帮助.
研究的目的:
- 评估CBIR系统在改善UIP CT模式分类方面的有效性.
- 评估CBIR对不同经验水平的诊断准确性和读者间协议的影响.
主要方法:
- 对2013-2015年高分辨率胸部CT扫描的回顾性分析.
- 在CBIR中使用了深度学习算法来检索类似的UIP CT图像.
- 9名读者 (经验不同) 在获得CBIR协助之前和之后对CT模式进行了分类.
主要成果:
- CBIR导致整体诊断准确度略有改善,但在统计学上显著 (61.3%至67.1%).
- 读者间协议 (Fleiss Kappa) 也显示出显著的改善 (0.400至0.476).
- 根据读者的经验,准确度的提高有所不同;经验丰富的放射科医生受益最多,而住院医生和肺科医生显示出不同的结果.
结论:
- 对于UIP CT模式分类,CBIR在诊断准确度和读者间协议方面提供了适度的改进.
- 系统的实用性受到读者的体验水平的影响.
- 对于经验丰富的放射科医生来说,CBIR是一个有希望的补充工具,可以提高UIP诊断的一致性.
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