从不一致的注释到基本真相:在牙科图像中对近端病变的注释的聚合策略
Vanessa Klein1, Martha Büttner2, Gerd Göstemeyer3
1Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany; Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, Munich, Germany.
Journal of dentistry
|April 1, 2025
概括
选择正确的数据聚合策略对于牙科人工智能至关重要. 多注释器能力估计 (MACE) 和Dawid-Skene (DS) 在牙损伤检测方面表现优于多数投票 (MV).
科学领域:
- 牙科成像分析分析 牙科成像分析
- 医疗保健中的人工智能
- 牙科诊断的准确性 牙科诊断的准确性
背景情况:
- 精确注释牙损是人工智能开发的挑战.
- 多审查员的注释通常使用诸如多数投票 (MV) 等方法进行汇总.
- 选择聚合策略显著影响AI模型的性能.
研究的目的:
- 为了比较不同标签聚合策略在检测牙损伤方面的有效性.
- 评估单模数据集 (射线图,近红外光转光 - - NILT) 和多模数据集的聚合方法.
- 确定标注近端病变的最佳策略.
主要方法:
- 五位牙医注释了1007个靠近牙的表面.
- 组织学分析作为分类的黄金标准 (声音,牙或牙损伤).
- 应用了四种聚合策略 (MV,权重多数投票 - WMV,Dawid-Skene - DS和多注释器能力估计 - MACE),并使用接收器操作特征曲线 (AUROC) 下的面积进行了比较.
主要成果:
- 多注释器能力估计 (MACE) 显著优于其他放射和NILT图像的方法.
- 对于多模式数据集,Dawid-Skene (DS) 在所有损伤深度上都表现出卓越的性能.
- 在几次比较中,大多数投票 (MV) 被发现是不理想的.
结论:
- 通常使用的多数投票 (MV) 策略可能不适合牙科图像注释.
- 特定的聚合策略,如MACE和DS,根据数据集特征提供了更高的准确性.
- 对聚合方法的知情选择对于牙科诊断中强大的AI应用至关重要.
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