Surgical Tool Handle Vibration-Based Drilling State Recognition During Hip Fracture Fixation.

Abstract

Traditional manual drilling during hip fracture fixation can easily lead to unstable fixation and vascular damage. This study aimed to investigate a safe and easy-to-use robot-assisted method to automatically drill bone and distinguish critical bone drilling states with high accuracy in real-time for the bone hole-making process during hip fracture fixation. A bone-drilling robotic system was designed to automatically create holes in the femoral neck. Four fresh pig femurs were drilled at the posterosuperior femoral neck using three modes: "all-in" (AI), "in-out-in" (IOI), and "percutaneous fixation" (PF). A high-frequency accelerometer captured the generated vibrations of the drill handle, which were then transferred to a personal computer using a data acquisition card. Five bone drilling states are defined, including: "drill idling," "initial drilling," "in the cancellous bone," "out the femoral neck," and "in the cortical bone." The harmonic distribution of the vibration signal was extracted by fast Fourier transform (FFT) and used as a critical feature to identify different drilling states. To prove the difference in the harmonic distribution at different drilling states, an independent sample t-test was used to compare the percentage of the first harmonic amplitude in the first 10 harmonics at each drilling state. A neural network classifier was trained with the frequency spectrum as the input and the drilled state as the output to distinguish the critical bone drilling states with high accuracy in real-time. The classifier was trained and tested on four specimens to ensure that the surgical robot could accurately identify the five drilling states. In each specimen, the harmonic distributions of the drilling vibration at different drilling modes were significantly different (p< 0.05). The average recognition accuracies of the drilling state for the four specimens were all higher than 84%. The three defined modes were distinguished with extremely high accuracies. The recognition accuracies of "in the cancellous bone" for specimens 1 to 4 were 83.2%, 84.8%, 92.9%, and 84.7%. The recognition accuracies of "in out the femoral neck" from specimens 1 to 4 are 98.2%, 88.4%, 95.8%, and 88.8%. The recognition accuracies of "in the cortical bone" for specimens 1 to 4 were 94.6%, 80.8%, 95.5%, and 85.8%. The proposed robot-assisted method can automatically distinguish five critical bone-drilling states with high accuracy in real-time to avoid weak fixation and damage to the lateral epiphyseal artery.