The Compass of the Future: Machine Learning-Guided Navigation in Spine Surgery

Le résumé fourni par la source

Neuronavigation, initially developed for cranial applications, has laid the groundwork for innovation in spine surgery. Recent advances in artificial intelligence (AI) and machine learning (ML) have elevated navigation techniques by improving image acquisition, registration and intraoperative tracking. Contemporary ML-driven neuronavigation systems now demonstrate higher pedicle screw placement accuracy and lower complication rates than traditional fluoroscopy- or stereotactic-guided approaches, while also reducing radiation exposure and surgeon fatigue. These frameworks frequently leverage architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs) to enhance segmentation, anatomical mapping and real-time image reconstruction. Integrated into both standalone navigation systems and robot-assisted workflows, these algorithms support dynamic intraoperative adaptation to patient-specific anatomy even with complex tissue manipulation and instrumentation. Although fully autonomous execution is not yet realized, current FDA-cleared systems emphasize surgeon-directed control with progressive elements of computational assistance; future iterations of AI/ML-enabled technologies promise to incorporate greater levels of autonomy. This chapter highlights the transformative potential of intelligent navigation in spine surgery, situating algorithmic principles within clinical context to illustrate how AI/ML-based navigation can shape the future of neurosurgical precision medicine.

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