AI-Assisted Multimodal Surgical Navigation and Anatomical Landmark Localization for Augmented Reality–Guided Neurosurgery
Keywords:
augmented reality, multimodal surgical navigation, anatomical landmark localization, deep learning, neurosurgery, clinical AI governanceAbstract
Surgical navigation has entered a transition from systems that merely display tracked instruments to systems that fuse multimodal imaging, machine interpretation, and augmented reality rendering in a shared intraoperative coordinate frame. This paper examines the architecture and system-level trade-offs of AI-assisted multimodal surgical navigation for neurosurgery, with particular attention to anatomical landmark localization and safety-critical integration. The discussion moves beyond individual algorithm performance to address the coupling between preoperative imaging, intraoperative deformation, registration latency, visualization accuracy, and clinical workflow. Structural choices such as the separation of low-latency tracking from slower inference pipelines, the use of markerless registration, and the placement of AI-derived anatomical overlays are analyzed as governance and engineering decisions rather than isolated technical details. The paper argues that robust deployment depends on coherent error management across model segmentation, landmark inference, coordinate alignment, and display calibration. It further considers infrastructure sustainability, fairness, and regulatory implications. Case illustrations from cranial and spinal navigation, markerless augmented reality registration, and deep learning tumor detection from computed tomography highlight both the promise and the fragility of current systems. The conclusion sets out a research agenda oriented toward verifiable uncertainty communication, learning health system integration, and equitable clinical AI governance.
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