基于深度学习的评估,用YOLO v8对初级牙中的肉皮参与
Aydin Sohrabi1,2, Nazila Ameli2, Masoud Mirimoghaddam2
1Department of Orthodontics, Faculty of Dentistry, Tabriz University of Medical Sciences, Tabriz, Iran.
PLOS digital health
|April 8, 2025
概括
一个新的机器学习模型使用牙科放射图准确地诊断了母牙的脉参与. 这种人工智能工具有望改善儿童牙科护理和诊断.
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:
- 牙损是一个重要的全球健康问题,特别是在儿童中.
- 精确评估纸干涉对于有效的纸治疗在儿科牙科至关重要.
- 目前用于脉参与的诊断方法缺乏显著的改进.
研究的目的:
- 开发和评估一种机器学习 (ML) 模型,用于使用X射线图来诊断多种初级牙中的脉参与.
- 评估模型的诊断性能,特别是区分上部和下部初级牙.
主要方法:
- 一个卷积神经网络 (CNN) 模型,YOLOv8m-cls,在482张来自儿童牙科记录的咬翼放射图上进行了训练.
- 图像进行了预处理,标准化和增强,用于分类脉参与.
- 模型性能使用top-1和top-5准确度,精度,回忆和F1得分进行评估.
主要成果:
- 通过YOLOv8m-cls模型,在上主要牙上达到78.7%的top-1准确度,在下主要牙上达到87.8%.
- 在验证数据集中,观察到较低初始牙的精度更高.
- 该模型在新的测试图像上证明了在诊断脉参与方面的有效性.
结论:
- 一个有前途的CNN模型被开发出来,用于从咬牙的X射线图来诊断母牙中的肉质干涉.
- 该模型显示了在儿科牙科临床应用的潜力,有助于诊断和治疗规划.
- 未来的研究应该专注于整合临床变量,并利用整个咬翼图像来增强诊断能力.
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