人工智能辅助的融合技术在形电脑断层扫描和面部扫描数据之间进行正牙诊断
Than Trong Khanh Dat1,2, Jang-Hoon Ahn3, Hyunkyo Lim4,5
1Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet, Dien Hong Ward, Ho Chi Minh City 700000, Vietnam.
Bioengineering (Basel, Switzerland)
|September 27, 2025
概括
这项研究引入了一种深度学习方法,将形束计算断层扫描 (CBCT) 和面部扫描集成为精确的3D面部识别. 该方法显示了在医学成像中提高诊断准确度和治疗规划的潜力.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 准确的3D面部重建对于医疗成像应用,如整形外科和正牙科至关重要.
- 与面部扫描相结合的圆束计算机断层扫描 (CBCT) 由于数据采集的差异和扫描噪声带来了挑战.
- 现有的方法在精确对齐和处理复杂的面部几何形状方面存在困难.
研究的目的:
- 开发一种基于深度学习的方法,用于准确整合CBCT和面部扫描数据.
- 为了提高诊断准确度和优化医疗成像中的治疗计划.
- 提高3D面部识别和对齐过程的可靠性和效率.
主要方法:
- 用一个深度学习模型来精确地检测面部网状网络.
- 代的最接近点 (ICP) 算法被用于初始对齐.
- 该系统旨在处理独立的数据采集和复杂的面部几何形状.
主要成果:
- 在可行性测试中实现了约0.3毫米的平均注册错误 (早期RMSE).
- 在各种扫描方向上表现出一致的性能.
- 深度学习框架有效地处理了各种面部几何形状,提高了对齐可靠性.
结论:
- 拟议的深度学习方法显示了准确的CBCT和面部扫描在医学成像中集成的希望.
- 初步结果表明,有可能改善诊断结果和治疗策略.
- 需要进一步开发,以减少处理时间,并允许同时捕获用于临床验证的数据.
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