系统自动评估下膜神经损伤的可能性
Ziyang Gong1, Weikang Feng2, Xin Su2
1Department of Computer Engineering, Gachon University, Seongnam-si, 13120, Republic of Korea.
Computers in biology and medicine
|January 10, 2024
概括
这项研究引入了一个自动化系统,使用全景放射图来预测下膜神经 (IAN) 损伤风险在下第三 (MM3) 提取之前. 该系统提高了细分的准确性,并实现了比现有的深度学习模型更高的分类性能.
科学领域:
- 牙科 牙科是指牙科的专业.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 下膜神经 (IAN) 损伤在下第三 (MM3) 提取过程中是一个显著的风险.
- 目前使用面具图像进行IAN伤害预测的深度学习方法在准确性和可解释性方面存在局限性.
研究的目的:
- 开发一个自动化系统来评估IAN伤害的可能性,使用全景X线图.
- 为了增强MM3和下沟通道 (MC) 的细分.
- 为了提高IAN伤害风险的分类准确性.
主要方法:
- 为MM3和MC提出了一个新的细分模型,SS-TransUnet,用于MM3.
- 开发了一个分类算法,CD-IAN损伤类别,用于IAN损伤概率.
- 利用全景射线图作为主要的成像方式.
主要成果:
- 细分模型显示,MM3和MC的子系数分别提高了0.9%和2.6%.
- 将MM3和MC的95%豪斯多夫距离缩小到1.619和1.886.
- 该分类算法实现了84.6%的准确性,超过了深度学习模型的3.8%.
结论:
- 开发的自动化系统有效地提高了IAN伤害风险评估的细分和分类准确性.
- 该系统为诊断IAN损伤可能性提供了更易于解释的基础.
- 这些发现表明,通过改进手术前评估,IAN伤害的潜在减少.
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