Aim:This study evaluates the effectiveness of a custom 3D-printed Deep Brain Stimulation (DBS) lead holder used with the StealthStation Autoguide (Medtronic, Dublin, Ireland) for electrode placement in the Subthalamic Nucleus (STN), Globus Pallidus interna (GPi), and Ventral Intermediate Nucleus (Vim) in a cadaveric model. The goal was to measure the deviation rate of the placed electrodes from their intended targets, providing a benchmark for the system\'s accuracy and paving the way for its use in standard DBS workflows.Material and Methods:The study was conducted in an experimental lab using a cadaver obtained according to local regulations. Planned electrode trajectories, designed with Medtronic\'s DBS surgery planning system, were transferred to the StealthStation Autoguide. A 3D-printed DBS lead holder with integrated navigation fiducials was used to place six electrodes in the targeted brain regions. Pre-operative CT and MRI scans were used for planning, and post-operative imaging confirmed electrode placement. Deviation from planned trajectories was analyzed using Python to assess accuracy.Results:Following a 30-minute registration and draping process, the median electrode placement time was 22.5 minutes (range: 15-120). The total surgical time for all six electrodes was approximately 5 hours, including imaging, adjustments, and confirmation. The median difference was 1.73 mm (0.03-5.45) on the X-axis, 1.86 mm (0.46-2.74) on the Y-axis, and 1.95 mm (0.73-4.4) on the Z-axis. The median vectorial difference was 2.68 mm (2.3-6.71), while the median trajectory difference was 3.01 mm (1.64-6.63). Conclusion:Despite 50% of leads having a vectorial difference exceeding 4 mm, most had a trajectory difference of less than 3 mm, which could be attributed to the inability to measure the length of the electrode precisely. These results suggest that with minor adjustments, the StealthStation Autoguide could be a cost-effective alternative to similar systems, though further cadaveric studies are necessary to address potential learning curves and random factors.