Machine Learning in Neurosurgery: Toward Complex Inputs, Actionable Predictions, and Generalizable Translations.

Area of Science:

• Neurosurgery
• Artificial Intelligence
• Medical Imaging

Background:

• Machine learning (ML) is increasingly applied in neurosurgery for diverse tasks.
• Advanced ML models can reconstruct images, predict surgical events, and aid intraoperative decisions.

Purpose of the Study:

• To review neurosurgical applications of machine learning, from basic to complex models.
• To highlight the transformative potential of ML in neurosurgical patient care.
• To identify challenges in evaluating and translating complex ML advancements.

Main Methods:

• Review of current literature on machine learning in neurosurgery.
• Analysis of ML model capabilities including image analysis, navigation, and prediction.
• Discussion of translation roadblocks and the need for neurosurgeon involvement.

Main Results:

• ML models demonstrate capabilities in diagnosis, outcome prediction, imaging analysis, and robotic assistance.
• State-of-the-art models perform image reconstruction, video-based event prediction, and intraoperative decision support.
• Increasing complexity of ML presents evaluation and translation challenges.

Conclusions:

• Machine learning holds significant potential to revolutionize neurosurgical patient care.
• Effective translation of advanced ML requires neurosurgeon participation in development and validation.
• Addressing evaluation complexities is key for safe and impactful ML integration in neurosurgery.