放射科医生经验对人工智能的影响 胸部放射图中的注释质量:比较分析
Malte Michel Multusch1, Lasse Hansen2, Mattias Paul Heinrich3
1Department of Radiology and Nuclear Medicine, UKSH, 23538 Lübeck, Germany.
Diagnostics (Basel, Switzerland)
|March 28, 2025
概括
放射科医生的经验显著影响AI注释质量. 高级专家在复杂的案件中表现出色,而混合专业的方法可以优化AI培训数据集用于医学成像.
科学领域:
- 医疗成像医学成像
- 人工智能 (AI) 是一种人工智能.
- 放射学 放射学是指放射学
背景情况:
- 高质量的注释对于训练医疗成像中的AI模型至关重要.
- 标注是耗时的,限制了大型数据集的可用性.
- 放射科医生的经验是影响注释质量的关键因素.
研究的目的:
- 调查放射科医生经验对医学图像注释质量的影响.
- 为了比较不同经验水平的注释性能:医学学生,初级专业人员和高级专业人员.
主要方法:
- 53张匿名的胸部X射线被15位不同专业知识的读者注释.
- 标注包括解剖结构,肺不透明度和中央静脉导管 (CVC).
- 对注释质量进行了评估,使用子系数 (DSC) 和豪斯多夫距离 (HD) 与黄金标准对比.
主要成果:
- 高级专业人员通常获得更好的注释质量,特别是在复杂的结构.
- 医学学生在他们的注释中表现出更高的变化.
- 经验与CVC和肺结构的注释质量有正相关.
结论:
- 放射科医生的经验显著影响了医学图像注释的准确性和一致性.
- 建议采用混合专业知识的方法,利用高级放射科医生进行复杂的任务,并利用经验较少的放射科医生进行简单的注释.
- 优化注释过程对于放射学中高效的AI模型开发至关重要.
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