在使用深度学习的周围放射图片中自动检测和分类牙创伤:基于安德烈森分类的研究
Derya Sarıoğlu1, Zehra Güner1, Alican Kuran2
1Department of Paediatric Dentistry, Faculty of Dentistry, Gaziantep University, Gaziantep, Türkiye.
Oral surgery, oral medicine, oral pathology and oral radiology
|February 13, 2026
概括
这项研究表明,一个深度学习 (DL) 模型可以在周周放射图上识别牙创伤. 在简化创伤类别后,性能显著提高,突出显示了DL在牙科诊断中的潜力.
科学领域:
- 人工智能在牙科中的应用
- 放射性解读是一种放射性解读技术.
- 牙创伤的诊断 诊断 牙创伤的诊断
背景情况:
- 准确的识别牙创伤在周围的X射线图是有效的治疗至关重要的.
- 现有的分类牙科创伤的方法可能是耗时和主观的.
- 深度学习 (DL) 为自动化放射分析提供了一个潜在的解决方案.
研究的目的:
- 评估深度学习 (DL) 模型的性能,用于自动识别牙科创伤类型.
- 用安德里亚森系统对周周放射图进行牙创伤的分类.
- 评估标签整合对DL模型性能的影响.
主要方法:
- 周围的X线图根据安德里亚森分类进行了注释.
- 基于YOLOv8的深度学习模型被开发用于创伤分类.
- 最初的模型培训使用了详细的子类型;在将标签整合成两个主要类别后,培训了第二个模型.
- 用灵敏度,精度和F1分数来评估模型性能.
主要成果:
- 最初的DL模型表现出低性能 (F1得分:0.31).
- 排泄是表现最好的亚型 (F1分数:0.83).
- 将标签整合成两个主要类别显著改善了整体性能 (F1分数:0.76).
- 该模型在硬牙组织和肉损伤 (F1得分:0.82) 方面表现更好,而不是牙周组织损伤 (F1得分:0.44).
结论:
- 深度学习模型显示出在周周放射图上识别牙创伤的巨大潜力.
- 自动检测断裂线是DL模型的一个显著优势.
- 简化分类类别可以提高DL模型在牙科创伤识别中的性能.
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