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Automatic breach detection during spine pedicle drilling based on vibroacoustic sensing


Massalimova, Aidana; Timmermans, Maikel; Cavalcanti, Nicola; Suter, Daniel; Seibold, Matthias; Carrillo, Fabio; Laux, Christoph J; Sutter, Reto; Farshad, Mazda; Denis, Kathleen; Fürnstahl, Philipp (2023). Automatic breach detection during spine pedicle drilling based on vibroacoustic sensing. Artificial Intelligence in Medicine, 144:102641.

Abstract

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.

Abstract

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Balgrist University Hospital, Swiss Spinal Cord Injury Center
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Health Sciences > Medicine (miscellaneous)
Physical Sciences > Artificial Intelligence
Uncontrolled Keywords:Breach detection; Deep learning; Pedicle drilling; Sensor fusion; Spine surgery; Vibroacoustic sensing
Language:English
Date:October 2023
Deposited On:30 Jan 2024 14:00
Last Modified:30 Apr 2024 01:48
Publisher:Elsevier
ISSN:0933-3657
OA Status:Hybrid
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1016/j.artmed.2023.102641
PubMed ID:37783536
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)