  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.
