Pedicle drilling is a complex and critical spinal surgery task. Detecting breach or penetration of the surgical tool to the cortical wall during pilot-hole drilling is essential to avoid damage to vital anatomical structures adjacent to the pedicle, such as the spinal cord, blood vessels, and nerves. Currently, the guidance of pedicle drilling is done using image-guided methods that are radiation intensive and limited to the preoperative information. This work proposes a new radiation-free breach detection algorithm leveraging a non-visual sensor setup in combination with deep learning approach. Multiple vibroacoustic sensors, such as a contact microphone, a free-field microphone, a tri-axial accelerometer, a uni-axial accelerometer, and an optical tracking system were integrated into the setup. Data were collected on four cadaveric human spines, ranging from L5 to T10. An experienced spine surgeon drilled the pedicles relying on optical navigation. A new automatic labeling method based on the tracking data was introduced. Labeled data was subsequently fed to the network in mel-spectrograms, classifying the data into breach and non-breach. Different sensor types, sensor positioning, and their combinations were evaluated. The best results in breach recall for individual sensors could be achieved using contact microphones attached to the dorsal skin (85.8\%) and uni-axial accelerometers clamped to the spinous process of the drilled vertebra (81.0\%). The best-performing data fusion model combined the latter two sensors with a breach recall of 98\%. The proposed method shows the great potential of non-visual sensor fusion for avoiding screw misplacement and accidental bone breaches during pedicle drilling and could be extended to further surgical applications.

翻译：椎弓根钻孔是一项复杂且关键的脊柱手术任务。在导向孔钻孔过程中，检测手术器械对骨皮质壁的突破或穿透至关重要，以避免损伤椎弓根邻近的重要解剖结构，如脊髓、血管和神经。目前，椎弓根钻孔的引导主要依赖图像引导方法，这些方法辐射剂量大且仅限于术前信息。本研究提出一种新的无辐射穿透检测算法，融合非视觉传感器设置与深度学习方法。该装置集成了多种振动声学传感器，包括接触式麦克风、自由场麦克风、三轴加速度计、单轴加速度计以及光学追踪系统。数据采集自四具人体尸体的脊椎（L5至T10），由经验丰富的脊柱外科医生借助光学导航进行椎弓根钻孔。引入了一种基于追踪数据的自动标注方法，并将标注后的数据以梅尔频谱图形式输入网络，以分类为穿透与非穿透两类。研究评估了不同传感器类型、安装位置及其组合的效果。单个传感器中，附着于背部皮肤的接触式麦克风（85.8%）和夹持于钻孔椎骨棘突的单轴加速度计（81.0%）在穿透召回率上表现最佳。性能最优的数据融合模型结合了后两种传感器，穿透召回率达到98%。本研究表明，非视觉传感器融合在避免椎弓根钻孔中螺钉错位及意外骨穿透方面具有巨大潜力，并可推广至更多外科手术应用。