基于横向头脑图的多阶段Unet细分和喉气道的自动测量
Xiangquan Meng1, Feng Mao2, Zhi Mao2
1School of Mathematics, Jilin University, Changchun 130012, China.
Journal of dentistry
|July 28, 2023
概括
一个新的AI模型使用横向头脑图准确地划分喉气道 (PA),帮助正牙医进行个性化治疗和气道风险评估.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 矯正牙科 矯正牙科是一種矯正牙科.
背景情况:
- 牙正治疗显著影响喉气道 (PA).
- 呼吸道检查对于正牙诊断至关重要.
- 侧向脑图 (LCs) 是可靠的评估PA结构.
研究的目的:
- 开发一种简单的,人工智能驱动的方法,用于对正牙患者的初步PA评估.
- 评估正牙治疗对患者呼吸道的影响.
- 帮助临床医生判断PA状况.
主要方法:
- 在764个LC上训练了一种多阶段的单元细分模型 (Unet骨干).
- 该模型在130张图像上得到验证,并在另外130张图像上进行了测试.
- 确定了七个关键的PA点,并测量了相关的指数.
主要成果:
- 该模型实现了0.8180的平均子值,0.8393的精度和0.8188.8的回忆.
- 对于测量的气道线路,发现了高的类内相关系数 (0.599和0.855).
- 线性回归显示,模型预测与手动测量之间存在很强的相关性.
结论:
- 人工智能方法可靠地分割鼻,口和鼻下,计算关键指数.
- 临床从业者应审查和调整模型预测,因为潜在的错误.
- 这种方法提供了一种更简单的方法来检测PA阻塞在正牙科.
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