Artificial Intelligence in Intraoperative Imaging and Navigation for Spine Surgery: A Narrative Review.

Area of Science:

• Spine Surgery
• Medical Imaging
• Artificial Intelligence

Background:

• Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are increasingly integrated into medical practice.
• Spine surgery is undergoing transformation with AI applications in diagnostics, intraoperative imaging, and navigation.
• Current AI in spine surgery focuses on enhancing surgeon expertise and optimizing operative workflows.

Purpose of the Study:

• To provide a narrative review of AI applications in intraoperative imaging and navigation for spine surgery.
• To define AI, ML, and DL in a medical context and outline AI's historical evolution in orthopedic spine surgery.
• To highlight AI's potential for perioperative and intraoperative optimization, including radiation reduction and workflow streamlining.

Main Methods:

• Review of current AI capabilities in image recognition and automated pathology detection.
• Examination of conventional intraoperative imaging platforms (e.g., intraoperative CT, surface-based registration, optical surface mapping).
• Discussion of emerging AI-optimized advancements in imaging protocols and 3D reconstruction techniques.

Main Results:

• AI enhances image acquisition and navigation, improving intraoperative decision-making and technical execution.
• AI-driven advancements include low-dose CT protocols, metal artifact reduction, and 2D/3D image integration.
• These AI innovations aim to improve image quality, reduce radiation exposure, and increase navigational accuracy.

Conclusions:

• AI-enhanced imaging systems are redefining intraoperative spine surgery and advancing precision care.
• AI integration promises significant improvements in radiation exposure reduction, workflow efficiency, and surgical planning.
• The continued development of AI in spine surgery is crucial for shaping the future of precision-based orthopedic care.