深度学习方法用于牙编号和修复检测在混合牙的儿科周围牙放射图上
1Department of Paediatric Dentistry, Faculty of Dentistry, Gaziantep University, Gaziantep, Turkiye, Turkey. busrayctrk@outlook.com.
Clinical oral investigations
|October 11, 2025
概括
这项研究表明,YOLOv8模型准确地计算牙数量,并检测儿科牙X射线中的修复. 这种人工智能方法提高了牙医的诊断效率和准确性.
科学领域:
- 人工智能在牙科中的应用
- 对于医学成像的深度学习
- 儿科牙科放射学 儿科牙科放射学
背景情况:
- 准确的牙识别和修复检测对于儿科牙科治疗计划至关重要.
- 自动化这些过程可以显著提高诊断速度和准确性.
研究的目的:
- 为了评估YOLOv8模型在周周X射线图中对初级牙和永久牙进行编号的有效性.
- 评估YOLOv8在检测这些X光片上的牙修复的性能.
主要方法:
- 在6至12岁的儿童中,利用了1,504张牙周放射图进行牙编号和1,599张用于恢复检测.
- 采用了Python和YOLOv8深度学习模型.
- 通过使用准确度,精度,回忆,F1分数和IoU交叉点等指标评估模型性能.
主要成果:
- 在牙编号方面,YOLOv8模型实现了高精度 (回忆率:0.915,精度:0.979,F1得分:0.946,精度:0.89).
- 该模型在检测修复方面表现出强的性能 (回忆力:0.954,精度:0.871,F1得分:0.911,准确度:0.83).
结论:
- YOLOv8深度学习模型显示,在儿科混合牙放射图中,对精确的牙编号和修复检测有显著的希望.
- 人工智能驱动的牙科放射分析自动化节省了时间和劳动力,提高了临床实践的效率和准确性.
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