人工智能辅助增长预测的准确性在骨I类青春期前的患者中,使用连续横向脑电图进行2年增长间隔的预测
1Postgraduate Orthodontic Program, Arizona School of Dentistry & Oral Health, A.T. Still University, Mesa, Arizona, USA.
Orthodontics & craniofacial research
|February 7, 2024
概括
使用卷积神经网络 (CNN) 的人工智能显示出从侧向脑图预测骨生长的潜力. 然而,准确性各不相同,一些硬和软组织的地标需要进一步改进,以获得可靠的正牙预测.
科学领域:
- 矯正牙科 矯正牙科是一種矯正牙科.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 准确预测骨生长对于正义牙科治疗计划至关重要.
- 纵向横向脑图 (Lat-cephs) 通常用于增长评估.
- 人工智能 (AI) 提供了自动化和提高增长预测准确性的潜力.
研究的目的:
- 评估人工智能辅助卷积神经网络 (CNN) 模型在使用Lat-cephs预测骨生长中的准确性.
- 评估CNN模型在识别硬组织地标 (HTL) 和软组织地标 (STL) 的性能.
主要方法:
- 使用了198名患有I级骨缺陷的日本青春期前儿童的数据集.
- 分析了8岁和10岁的拉特塞夫.
- 一个CNN模型确定了28个HTL和19个STL,并将预测错误分类.
主要成果:
- 对于大多数硬组织标志物,CNN模型实现了可接受至优异的平均预测错误值.
- 在下巴区域的软组织标志 (例如,Pog', Gn', Me') 显示出不令人满意的预测准确性.
- 一些硬和软组织标志物,特别是部区域的标志物,具有较低的准确预测百分比 (APP).
结论:
- 人工智能辅助的CNN模型显示了对正牙生长预测的前景.
- 需要进一步的研究来提高地标识别的准确性,特别是软组织和下巴相关的硬组织地标.
- 改进的人工智能算法可能会导致更精确的正治疗计划.
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