Background: Frameless robot-assisted deep brain stimulation (DBS) is an innovative technique for leads implantation. This study aimed to evaluate the accuracy and precision of this technique using the Sinovation SR1 robot.
Methods: 35 patients with Parkinson's disease who accepted conventional frame-based DBS surgery ( = 18) and frameless robot-assisted DBS surgery ( = 17) by the same group of neurosurgeons were analyzed. The coordinate of the tip of the intended trajectory was recorded as x, y, and z. The actual position of lead implantation was recorded as xa, ya, and za. The vector error was calculated by the formula of √(x - x) + (y - y) + (z - z) to evaluate the accuracy.
Results: The vector error was 1.52 ± 0.53 mm (range: 0.20-2.39 mm) in the robot-assisted group and was 1.77 ± 0.67 mm (0.59-2.98 mm) in the frame-based group with no significant difference between two groups ( = 0.1301). In 10.7% ( = 3) frameless robot-assisted implanted leads, the vector error was greater than 2.00 mm with a maximum offset of 2.39 mm, and in 35.5% ( = 11) frame-based implanted leads, the vector error was larger than 2.00 mm with a maximum offset of 2.98 mm. Leads were more posterior than planned trajectories in the robot-assisted group and more medial and posterior in the conventional frame-based group.
Conclusions: Awake frameless robot-assisted DBS surgery was comparable to the conventional frame-based technique in the accuracy and precision for leads implantation.
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http://dx.doi.org/10.3390/brainsci12070906 | DOI Listing |
Br J Neurosurg
December 2024
Department of Neurosurgery, Alder Hey Children's NHS Foundation Trust, Eaton Road, Liverpool, L12 2AP, UK.
Background: We assessed the accuracy and performed a directional analysis of robot-assisted implantation of stereoelectroencephalography (SEEG) depth electrodes in children using the frameless neurolocate 3D registration module.
Methods: Thirteen children with epilepsy undergoing stereotactic robot-assisted insertion of SEEG electrodes were included. Six children were operated on with standard frame-based registration while 7 with the use of the frameless neurolocate registration module.
Neurosurg Focus
December 2024
1Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, Florida; and.
Neurosurg Focus Video
July 2024
Departments of Neurosurgery and.
Stereoelectroencephalography (SEEG) is the gold standard to investigate the epileptic network in cases of drug-resistant epilepsy. Robot-assisted SEEG is increasingly being used; however, its installation process in the operating room is more difficult than that of the stereotactic frame procedure. New robotic tools and 3D intraoperative imaging ease the setup while achieving the same mechanical precision and a lower complication rate.
View Article and Find Full Text PDFNeurosurg Focus Video
July 2024
"Claudio Munari" Epilepsy Surgery Centre, Azienda Socio Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda, Milan, Italy.
An accurate definition of the epileptogenic zone is critical to the success of epilepsy surgery. When noninvasive presurgical studies are insufficient, stereoelectroencephalography (SEEG) becomes indispensable. This study illustrates a systematic approach using an illustrative case of centroparietal epilepsy, detailing the stepwise workup, planning, and image-guided robot-assisted frameless stereotactic implantation of intracerebral electrodes.
View Article and Find Full Text PDFNeurooncol Adv
May 2024
Institute of Neuroscience and Physiology, Department of Clinical Neuroscience, University of Gothenburg, Sahlgrenska Academy, Gothenburg, Sweden.
Background: Biopsies of intracranial lesions are a cornerstone in the diagnosis of unresectable tumors to guide neurooncological treatment; however, the procedure is also associated with risks. The results from the cranial robot guidance system Stealth Autoguide™ were studied after introduction at a neurosurgical department. Primary aims include the presentation of clinical and radiological data, accuracy of radiological diagnosis, learning curves of the new technology, diagnostic yield, and precision.
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