Machine Learning-Based Surgical Planning for Neurosurgery: Artificial Intelligent Approaches to the Cranium

Area of Science:

• Neurosurgery
• Artificial Intelligence
• Medical Imaging

Background:

• Artificial intelligence (AI) and machine learning are increasingly integrated into medical research, particularly in neurosurgery.
• Understanding brain function and simulating its processes are key areas of AI research.
• Technological advancements have enhanced the effectiveness of intracranial surgical approaches.

Purpose of the Study:

• To develop AI-driven, individualized, and safe intracranial surgical approaches.
• To integrate functional anatomical structures and pathological areas into AI for surgical planning.
• To optimize surgical pathways for maximum tumor resection with minimal impact on healthy tissue.

Main Methods:

• Preoperative MRI scans of patients with deep brain tumors were utilized for AI-driven surgical planning.
• Voxel data from intracranial arteries, veins, and neural tracts were extracted and labeled.
• A Q-learning algorithm processed labeled voxel data to identify optimal cortico-tumoral pathways.

Main Results:

• AI algorithms successfully identified the most suitable cranial entry points for surgical procedures.
• Q-learning delineated cortico-tumoral pathways from these optimal entry points.
• The identified pathways prioritized maximum tumor tissue removal while preserving functional anatomical tissues.

Conclusions:

• AI significantly contributes to improved outcomes in neurosurgery.
• AI enhances both preoperative surgical planning and intraoperative technique.
• Increased implementation of AI in neurosurgery is expected to yield positive results.