在CBCT图像中检测和分类使用深度学习的牙.
Rasool Esmaeilyfard1, Haniyeh Bonyadifard1, Maryam Paknahad2
1Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, Iran.
International dental journal
|November 8, 2023
概括
深度学习算法在束计算机断层扫描 (CBCT) 图像中准确地检测和分类. 这个人工智能应用程序提高了牙医的诊断准确度和治疗计划.
科学领域:
- 人工智能在牙科中的应用
- 放射学和成像学 放射学和成像学
- 口腔健康诊断 口腔健康诊断
背景情况:
- 从束计算机断层扫描 (CBCT) 图像中诊断牙虫对于治疗计划至关重要.
- 当前的诊断方法可能是主观的,耗时的.
- 深度学习在CBCT中用于牙腐烂检测的应用是一个新兴的领域.
研究的目的:
- 评估深度学习算法在诊断牙损伤方面的准确性.
- 评估深度学习在CBCT图像中分类牙损伤的延伸和位置的能力.
- 确定在CBCT中使用深度学习来检测牙的新奇性.
主要方法:
- 从CBCT图像中使用了382个牙和403个非牙的数据集.
- 数据集被分为开发 (培训/验证) 和测试集.
- 采用多输入卷积神经网络 (CNN) 来分析轴向,斜面和冠状CBCT视图,以检测和分类.
主要成果:
- CNN在牙检测方面取得了很高的诊断准确率:牙的95.3%,非牙的94.8%.
- 牙腐烂检测的灵敏度,特异性和F1分数在牙腐烂时超过92%,而非牙腐烂时超过94%.
- 该网络在分类的扩散和位置方面表现出很高的表现.
结论:
- 深度学习模型在高灵敏度和特异性的CBCT图像中准确地识别和分类牙.
- 这项技术可以显著帮助牙医改善诊断和治疗计划.
- 人工智能驱动的牙检测可以提高诊断的可访问性,特别是在牙医短缺的地区.
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