Devaraj Harshavardhan, Murphy Ethan K, Halter Ryan J
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:3977-3980. doi: 10.1109/EMBC44109.2020.9175364.
Surgical drilling to fixate dental implants is associated with high risk of injury to the inferior alveolar nerve (IAN) and the maxillary sinus. Current common practice is to use pre-operative radiographs to plan and drill with no real-time feedback of drill tip position with respect to these critical structures. Real-time proximity sensing of the IAN and maxillary sinus by measuring the electrical impedance properties of tissues, directly from the drill tip, while drilling may reduce and eventually eliminate this risk. Sensing impedance to detect tissue boundaries needs sensor geometry optimization for maximum detection distance. We have created a finite element method (FEM) based simulation platform that yields accurately impedances for different conductivities, frequencies and sensor geometries.
用于固定牙种植体的手术钻孔与下牙槽神经（IAN）和上颌窦损伤的高风险相关。目前的常规做法是使用术前X光片进行规划和钻孔，而对于这些关键结构，钻孔时没有钻头位置的实时反馈。在钻孔时，通过直接从钻头测量组织的电阻抗特性来对IAN和上颌窦进行实时接近感应，可能会降低并最终消除这种风险。检测组织边界的感应阻抗需要优化传感器几何形状以实现最大检测距离。我们创建了一个基于有限元方法（FEM）的模拟平台，该平台能针对不同的电导率、频率和传感器几何形状精确得出阻抗。
