深度学习模型用于从数字全景图像中对叶落和永久牙进行分类
Manoj Jaiswal1, Megha Sharma2, Padmavati Khandnor2
1Pedodontics and Preventive Dentistry, Postgraduate Institute of Medical Education and Research, Chandigarh, IND.
Cureus
|January 5, 2024
概括
人工智能模型,特别是EfficientNetB0和EfficientNetB3,在从牙科放射图中分类叶落牙和永久牙时,达到98%的准确性,提高了儿科牙科的诊断能力.
科学领域:
- 人工智能在牙科中的应用
- 数字放射学分析 数字放射学分析
- 儿科牙科诊断 儿科牙科诊断
背景情况:
- 牙科放射图对于诊断,年龄确定和治疗计划至关重要.
- 数字放射学和人工智能为改善牙科诊断和减少临床医生的工作量提供了潜力.
- 人工智能系统可以通过分类和本地化任务来简化决策.
研究的目的:
- 评估各种卷积神经网络 (CNN) 架构的性能,以对叶落和永久牙进行分类.
- 确定最有效的人工智能模型,用于在儿科牙科放射图中进行自动牙分类.
主要方法:
- 训练CNN模型 (Resnet,AlexNet,EfficientNet) 在620张4至16岁儿童的全景射线图上.
- 使用70:15:15数据分割进行培训,验证和测试.
- 使用开发的AI模型将牙分类为落叶或永久牙.
主要成果:
- 在牙分类方面,EfficientNetB0和EfficientNetB3表现出卓越的性能.
- 两个EfficientNet模型都实现了98%的准确性,精度,回忆和F1分数.
- 高性能表明在识别牙特征以进行分类方面具有强大的能力.
结论:
- EfficientNetB0和EfficientNetB3是高效的,用于自动化叶落和永久牙的分类.
- 人工智能集成显示了提高儿科牙科诊断准确性的前景.
- 这些模型有可能提高效率并减少牙科专业人员的压力.
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