Nowadays, spinal disease has gradually increased due to population aging, improper lifestyle, or accidents; for example, intervertebral disc herniation, spinal degeneration, and scoliosis are well-known spine disorders. They all require Pedicle Screw Placement (PSP) as a crucial step during surgical interventions. Robotic-assisted spine surgery combined with imaging modalities such as fluoroscopy and Computed Tomography (CT) have improved surgical outcomes for PSP. Taking advantage of these modalities, the surgeon pre-operatively estimates the ideal trajectory for PSP, and fluoroscopy can help visualize the drilled trajectory intraoperatively. The pre-operative CT image is a rich data set that can enhance surgical accuracy. For instance, Qi et al. proposed a path-planning method based on pre-operative CT scans where the spine of the patient is segmented, and then each individual vertebra is classified by using a trained deep-learning network model [1]. Cheng et al. also used pre-operative CT images to do automatic vertebra landmark detection and trajectory planning for PSP with the help of a Convolutional Neural Network (CNN) [2]. Ma et al. improved the pre-operative drilling trajectory causing the highest pull-out force and hence better fixation after surgery [3]. Pre-operative CT imaging is also used to model drilling thrust force in bones as a function of different drilling speed, feed rate, and drill bit geometry [4]. To the author’s knowledge, using the pre-operative CT image to predict the drilling breach is still at the early stage. This work aims to use the Hounsfield unit of pre-operative CT images over the drilling trajectory, which helps to predict the bone drilling mechanical stiffness and force profile. Using this metric as pre-knowledge during drilling helps to identify the instantaneous moment when a breach happens and stop the robotic system. Therefore, in this work, the correlation between the Hounsfield unit and the force and stiffness was investigated to show the usability of the CT data to provide extra pre-operative information and aid intraoperative drilling state identification.