基于深度学习的儿童的自动斜骨分类
Lan Nan1, Min Tang1, Bohui Liang2
1College of Stomatology, Guangxi Medical University, Nanning 530021, China.
Diagnostics (Basel, Switzerland)
|May 27, 2023
概括
一种新的深度学习方法使用脑图和照片准确地分类儿童的斜骨模式. 这有助于早期诊断缺陷,有利于儿童 ортодонтика治疗.
科学领域:
- 牙科和口腔卫生 牙科和口腔卫生
- 人工智能在医学中的应用
- 儿科的面发育 儿童的面发育
背景情况:
- 缺陷,头骨面发育形,在儿童中很常见.
- 早期诊断和干预对于有效的正牙治疗至关重要.
- 在儿童中使用深度学习自动检测缺陷仍然未得到充分探索.
研究的目的:
- 开发和验证一种基于深度学习的方法,用于自动分类儿童的 sagittal 骨模式.
- 建立一个基础的决策支持系统,用于早期的正牙干预.
- 为了解决将深度学习应用于儿科脑力测量数据的新奇问题.
主要方法:
- 训练并比较四个最先进的深度学习模型,使用1613个横向脑图.
- 选择Densenet-121作为表现最好的模型进行进一步的验证.
- 利用转移学习,数据增强和标签分布学习进行模型优化.
- 输入数据包括横向脑图和个人资料照片.
- 使用五倍交叉验证评估的性能.
主要成果:
- 使用横向头脑测量放射图的卷积神经网络 (CNN) 模型实现了83.99%的灵敏度,92.44%的特异性和90.33%的准确性.
- 使用个人资料照片的模型实现了83.39%的准确性.
- 纳入标签分发学习提高了准确度,达到91.28% (脑图) 和83.98% (照片),同时减少了过度贴合.
- 丹森特-121模型在分类儿科的形骨模式方面表现出高精度.
结论:
- 深度学习模型,特别是Densenet-121,可以从侧向脑图和个人资料照片中准确地分类儿科斜骨模式.
- 开发的方法代表了自动检测儿童缺陷的重大进步.
- 这种方法有望发展为儿童群体及时接受正牙治疗的决策支持系统.
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