检测和双标签分类牙数量和牙状况在牙科全景放射图中,包括落叶牙
Koki Sakai1, Chisako Muramatsu2, Yuta Seino3
1Graduate School of Natural Science and Technology, Gifu University, Gifu, Japan.
Radiological physics and technology
|July 9, 2025
概括
这项研究引入了一种AI模型,用于自动检测牙,并从牙科放射图片进行条件分类,实现高精度. 这项技术旨在减少牙医的工作量,提高诊断精度.
科学领域:
- 人工智能在牙科中的应用
- 医学图像分析 医学图像分析
- 牙科信息学 牙科信息学
背景情况:
- 手动记录牙科X线图的图表是耗时的.
- 准确的牙识别和状况评估对于牙科治疗计划至关重要.
研究的目的:
- 开发一种自动化系统,从全景放射图中提取牙信息.
- 为了减少牙医记录牙科图表的手工工作量.
主要方法:
- 采用YOLOv7模型,同时检测落叶牙和永久牙,并对牙状况进行分类 (自然,部分修复,假牙冠,植入物,pontic).
- 应用基于规则的后处理来完善检测到的对象.
- 使用精度,回忆和F1得分评估的性能,与0.5.5的交叉超过联盟 (IoU) 值.
主要成果:
- 在牙编号方面取得了高精度,精度,回忆和F1分数分别为98.51%,98.38%和98.45%.
- 在五个类别的牙状况分类中获得了95.47%的平均F1分数.
结论:
- 开发的AI方法有效地检测和分类牙的数量和条件从牙科全景X射线图.
- 这种自动化方法预计将大大减少牙医的工作量,并提高牙科记录的准确性.
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