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Updated: May 28, 2026

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
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Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

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Artificial Intelligence for STN-DBS Surgical Planning in Parkinson's Disease: A Multicenter Study Comparing

Feifei Wu1, Raffaella Buonanno2, Valentina Baro1

  • 1Academic Neurosurgery, Department of Neuroscience, University of Padua, 35128 Padua, Italy.

Brain Sciences
|May 27, 2026
PubMed
Summary

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Artificial intelligence (AI) shows comparable results to traditional methods for determining Deep Brain Stimulation (DBS) targets in Parkinson's disease (PD) patients, particularly for lateral-lateral coordinates. Further research is needed to validate AI's role in surgical planning.

Area of Science:

  • Neurosurgery
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Deep Brain Stimulation (DBS) is an established therapy for advanced Parkinson's disease (PD).
  • Accurate target identification is crucial for effective DBS surgery.
  • Traditional methods for target identification include direct and indirect approaches.

Purpose of the Study:

  • To compare the efficacy of traditional targeting methods with artificial intelligence (AI) for DBS in Parkinson's disease.
  • To evaluate the accuracy of AI-driven target identification against conventional atlases and intraoperative recordings.

Main Methods:

  • Analysis of eight patients undergoing bilateral subthalamic nucleus (STN) DBS.
  • Comparison of target coordinates derived from Schaltenbrand and Wahren atlases, AI (RebrAIn system), and microelectrode recordings (MERs).
Keywords:
AIDBSParkinson’s diseasefunctional neurosurgery

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  • Statistical evaluation using non-parametric ANOVA Friedman test for stereotactic coordinate differences (X, Y, Z).

    • Main Results:

    • Significant agreement observed in lateral-lateral (X) coordinates across all methods.
    • Substantial discrepancies noted in antero-posterior (Y) and cranio-caudal (Z) coordinates.
    • AI demonstrated comparable lateral-lateral (X) coordinate values to traditional methods.

    Conclusions:

    • AI shows promise in DBS target determination, exhibiting comparable accuracy to traditional methods along the X-axis.
    • Interindividual anatomical variability and imaging limitations present challenges.
    • Further validation of AI and machine learning models is essential for integrating them into preoperative workflows.