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:椎弓钻孔是一项复杂而关键的脊柱手术任务。在导引孔钻孔期间,监测手术工具是否穿透皮层壁以避免对靠近椎弓的关键解剖结构,如脊髓、血管和神经等造成损伤是非常重要的。目前,椎弓钻孔的导引是通过影像引导方法进行的,这种方法需要放射性介入,并且仅限于术前信息。本文提出了一种新的无放射性破损检测算法,结合非视觉传感器和深度学习方法。该系统集成了多个振动声传感器,如接触式麦克风、自由场麦克风、三轴加速度计、单轴加速度计和光学跟踪系统。实验数据收集于人类尸体脊柱(从L5到T10),由经验丰富的脊柱外科医生在光学导航下进行椎弓钻孔。介绍了一种基于跟踪数据的自动标记方法。标记数据随后以Mel变换谱图的形式输入网络进行分类,将数据分类为破损和非破损。评估了不同传感器类型、传感器位置及其组合。个体传感器具有最佳破损检测率,使用贴在背部皮肤上的接触式麦克风(85.8\%)和夹在钻孔的椎骨棘突上的单轴加速度计(81.0\%)。最佳表现的传感器融合模型包括后一种传感器和夹在钻孔的椎骨棘突上的单轴加速度计,其破损检测率为98\%。该方法表明了非视觉传感器融合在避免螺钉错误放置和意外骨折方面具有巨大的潜力,可以扩展到更多手术应用场合。