一个基于深度学习的全新视角,用于牙编号和虫检测.
Baturalp Ayhan1, Enes Ayan2, Yusuf Bayraktar3
1Department of Restorative Dentistry, Faculty of Dentistry, Kırıkkale University, Kırıkkale, Turkey. baturalpayhan@kku.edu.tr.
Clinical oral investigations
|February 27, 2024
概括
这项研究表明,深度学习算法,特别是卷积神经网络 (CNN),可以准确地检测,数量和识别牙放射图中的腔. 这种自动化方法有望在实时临床环境中提高诊断效率.
科学领域:
- 人工智能在牙科中的应用
- 医学成像分析 医学成像分析
- 深度学习应用程序
背景情况:
- 准确检测和计数牙对于牙科诊断和治疗规划至关重要.
- 对于有效的干预,在早期发现牙损伤是必不可少的.
- 目前用于分析咬翅放射图的方法可能耗时,并且可能从自动化中受益.
研究的目的:
- 开发和评估一个基于深度学习的系统,用于自动检测牙,并对数字咬牙翼射线影像进行编号.
- 评估该系统在识别牙 (牙) 方面的实时诊断效率.
- 通过人工智能驱动的牙科图像分析来增强临床工作流程和治疗结果.
主要方法:
- 采用了1170张匿名数字咬翅放射图的数据集,分为训练和测试集.
- 开发了一个三阶段的管道,使用预训练的卷积神经网络 (CNN) 进行图像定向和YOLOv7用于牙检测,编号和虫识别.
- 交叉与联合 (IoU) 度量被用来匹配编号的牙与检测到的.
主要成果:
- 该系统在牙检测 (回忆率0.994,精度0.987,F1得分0.99) 和牙编号 (回忆率0.974,精度0.985,F1得分0.979) 中实现了高性能.
- 虫检测的回忆率为0.833,精度为0.866,F1得分为0.822.
- 牙编号和虫检测的整体匹配性能显示准确率为0.934,回忆率为0.834,特异性为0.961,精度为0.851,F1-Score为0.842.
结论:
- 拟议的基于CNN的模型有效地在咬翼放射图上同时进行牙检测,编号和虫检测.
- 这种人工智能系统通过自动化放射分析的关键方面,为临床医生提供了巨大的潜力.
- 自动化能力可以提高诊断性能,节省时间,提高牙科评估的效率.
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