PDF(Pubmed)
Abstract：
This study compared the accuracy of facial landmark measurements using deep learning-based fiducial marker (FM) and arbitrary width reference (AWR) approaches. It quantitatively analysed mandibular hard and soft tissue lateral excursions and head tilting from consumer camera footage of 37 participants. A custom deep learning system recognised facial landmarks for measuring head tilt and mandibular lateral excursions. Circular fiducial markers (FM) and inter-zygion measurements (AWR) were validated against physical measurements using electrognathography and electronic rulers. Results showed notable differences in lower and mid-face estimations for both FM and AWR compared to physical measurements. The study also demonstrated the comparability of both approaches in assessing lateral movement, though fiducial markers exhibited variability in mid-face and lower face parameter assessments. Regardless of the technique applied, hard tissue movement was typically seen to be 30% less than soft tissue among the participants. Additionally, a significant number of participants consistently displayed a 5 to 10° head tilt.
摘要：
这项研究比较了使用基于深度学习的基准标记（FM）和任意宽度参考（AWR）方法进行面部标志测量的准确性。它从37名参与者的消费者相机镜头中定量分析了下颌硬软组织侧向偏移和头部倾斜。自定义深度学习系统可识别面部标志，用于测量头部倾斜和下颌外侧偏移。使用电声描记术和电子标尺对圆形基准标记（FM）和系间测量（AWR）进行了物理测量验证。结果显示，与物理测量相比，FM和AWR的低面和中面估计存在显着差异。这项研究还证明了两种方法在评估横向运动方面的可比性，尽管基准标记在中面部和下面部参数评估中表现出变异性。不管采用何种技术,在参与者中,通常观察到硬组织运动比软组织运动少30%.此外,大量参与者始终显示头部倾斜5至10°。
