A Novel Intraoperative CT Navigation System for Spinal Fusion Surgery in Lumbar Degenerative Disease: Accuracy and Safety of Pedicle Screw Placement.
Haruo KannoKyoichi HandaMotoki MurotaniHiroshi OzawaPublished in: Journal of clinical medicine (2024)
Background: In recent years, intraoperative computed tomography (CT) navigation has become widely used for the insertion of pedicle screws in spinal fusion surgery. However, conventional intraoperative CT navigation may be impaired by infrared interference between the infrared camera and surgical instruments, which can lead to the misplacement of pedicle screws. Recently, a novel intraoperative CT navigation system, NextAR, has been developed. It uses a small infrared camera mounted on surgical instruments within the surgical field. NextAR navigation can minimize the problem of infrared interference and be expected to improve the accuracy of pedicle screw placement. Methods: This study investigated the accuracy of pedicle screw insertion under NextAR navigation in spinal fusion surgery for lumbar degenerative diseases. The accuracy of pedicle screw placement was evaluated in 15 consecutive patients using a CT grading scale. Results: Screw perforation occurred in only 1 of the total 70 screws (1.4%). Specifically, there was one grade 1 perforation within 2 mm, but no perforations larger than 2 mm. There were no reoperations or neurological complications due to screw misplacement. Conclusions: NextAR navigation can provide high accuracy for pedicle screw insertion and help ensure safe spinal fusion surgery for lumbar degenerative diseases.
Keyphrases
- minimally invasive
- computed tomography
- dual energy
- image quality
- contrast enhanced
- positron emission tomography
- spinal cord
- coronary artery bypass
- robot assisted
- magnetic resonance imaging
- patients undergoing
- end stage renal disease
- chronic kidney disease
- surgical site infection
- magnetic resonance
- ultrasound guided
- spinal cord injury
- risk factors
- prognostic factors
- convolutional neural network
- high speed
- coronary artery disease
- mass spectrometry
- deep learning
- pet ct