未爆发的上犬的部门分类:基于深度学习的自动化框架,使用全景射线图
Marzio Galdi1, Davide Cannatà1, Flavia Celentano1
1Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, SA, Italy.
Orthodontics & craniofacial research
|March 3, 2026
概括
一个新的深度学习框架自动化了未爆发的上犬 (UMC) 部门分类. 这种人工智能方法的准确性与人类牙医相美,但在分类UMC位置方面具有更高的可靠性.
科学领域:
- 牙科 牙科是指牙科的专业.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 精确分类未爆发的上 (UMCs) 对于正牙治疗规划至关重要.
- 目前的方法依赖于牙科放射图的手动解释,这可能是主观和耗时的.
- 使用人工智能 (AI) 自动化这个过程可以提高效率和一致性.
研究的目的:
- 开发和评估基于深度学习的框架,用于UMC的自动化部门分类.
- 将人工智能框架的准确性和可靠性与人类牙科医生进行比较.
- 为了确定UMC部门分类中表现最好的AI模型.
主要方法:
- 使用来自数字全景放射图的1528个UMC的数据集.
- 六名牙医根据金的系统将UMC分为三个部门,并在四周后重复评估.
- 科恩的卡帕统计被用来评估审查员之间的和内部的协议.
- 训练和测试了几种人工智能模型,根据灵敏度,精度,准确性和可重复性选择了最佳模型.
主要成果:
- 对于UMC部门分类的人类考官间的一致性为0.78,考官内部的一致性为0.85.
- DenseNet121模型表现出最高的性能,总体准确率为76.8%,可重复性为95.3%.
- 人工智能框架在部门分类中的准确性与人类的表现相当.
结论:
- 开发的深度学习框架为UMC部门分类提供了一个自动化解决方案.
- 人工智能方法提供了与人类专家可比的准确性.
- 自动化系统的可靠性比牙医手动分类更高.
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