双重框架用于分类和检测全景射线图中的第三冲击
Zohaib Khurshid1, Mousa Haney Alsleem1, Fahad Ahmed Aljubairah1
1Department of Prosthodontics and Dental Implantology, College of Dentistry, King Faisal University, Al-Ahsa, Saudi Arabia.
International dental journal
|February 8, 2026
概括
这项研究引入了先进的AI框架,用于分析受影响的下第三,改善手术规划和降低神经损伤等风险. 这些新型号为临床使用提供了高精度和高效率.
科学领域:
- 口腔和牙面部外科手术
- 放射学中的人工智能
- 医学成像中的深度学习
背景情况:
- 手术提取受冲击的下第三股存在风险,包括下膜神经损伤,需要精确的手术前评估.
- 牙科放射学当前的人工智能受限于二元分类,类不平衡和缺乏标准化的评估,阻碍了临床采用.
研究的目的:
- 开发和评估两个独立的深度学习框架,用于对受影响的下第三牙进行全面的自动化分析.
- 为了提高第三手术的术前评估的准确性和效率.
主要方法:
- 一个端到端的对象检测管道,使用修改后的YOLOv10和YOLOv11n架构,具有多头自我注意.
- 一种基于特征的分类方法,利用ResNet50和InceptionNetV3的深度特征,通过传统的机器学习算法进行分类.
主要成果:
- 具有ResNet50特性的Fine KNN分类器实现了97.56%的准确性和96.10%的F1得分.
- YOLOv11n对象检测的平均精度为88.9% (mAP@0.5),计算复杂度较低.
- 多头自我注意和基于GAN的增强将检测性能提高了6.4% mAP.
结论:
- 拟议的人工智能框架提供准确的,自动化的多类评估第三的影响,具有高的诊断性能和计算效率.
- 这项研究通过可靠的F1分数评估,实时检测和改进手术前风险分层来增强人工智能辅助的手术规划.
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