在使用YOLOv11的标准化口内照片上检测多类缺陷
Ani Nebiaj1, Markus Mühling2, Bernd Freisleben2
1Department of Orthodontics, Johann-Wolfgang Goethe University, 60596 Frankfurt am Main, Germany.
Dentistry journal
|January 27, 2026
概括
一个深度学习模型从口腔内照片中准确地识别牙缺陷,提高查效率. 这种人工智能工具有助于标准化记录和诊断各种咬伤问题.
科学领域:
- 人工智能在牙科中的应用
- 计算机视觉用于医学成像.
- ортодонтическая 诊断 ортодонтическая 诊断 诊断 在牙上
背景情况:
- 从临床照片中识别牙缺陷是耗时的,容易变化.
- 自动化这个过程可以提高效率和诊断的一致性.
- 深度学习为准确的,自动化的缺陷检测提供了一个潜在的解决方案.
研究的目的:
- 评估基于YOLOv11的深度学习模型,用于在口内照片中自动检测缺陷.
- 测试一个结构化协议的训练是否能够可靠地检测多重缺陷.
- 评估模型在各种临床相关的缺陷类别中的表现.
主要方法:
- 5854张口腔内摄影的数据集根据"正牙治疗需求指数" (IOTN) 对17个缺陷类别的边界框进行了注释.
- 一个YOLOv11模型使用增强数据进行训练,并在一个持有测试集上进行评估.
- 使用平均精度 (mAP50),宏精度 (macro-P) 和宏回忆 (macro-R) 来测量性能.
主要成果:
- 在15个分析的缺陷类别中,YOLOv11模型实现了87.8%的mAP50,76.9%的宏P和86.1%的宏R.
- 对于深咬 (98.8%),透口 (97.9%) 和二级犬 (97.5%) 进行了高的每类表现.
- 性能在各个类别中各不相同,后侧交叉咬伤和大超喷射的检测率较低,可能是由于有限的示例和可视化约束.
结论:
- 一个基于YOLOv11的深度学习系统可以从常规的口内照片中准确地检测出多个临床上显著的缺陷.
- 该系统支持有效的查和标准化牙缺陷的文档.
- 通过更大的数据集,协议标准化和多式联接输入,可以进一步提高稳定性.
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