Augmented reality-driven organ deformation modeling: a roadmap for next-generation intraoperative navigation

Accurate intraoperative modeling of organ deformation is essential for translating augmented reality (AR) guidance into measurable clinical benefit. This editorial evaluates deformation-aware AR systems for intraoperative guidance, emphasizing how precise deformation modeling can reduce targeting errors, shorten operative time, and lower procedure-related complications. We review three principal algorithmic strategies: physics-based biomechanical models (e.g., finite-element methods), data-driven image-registration and deep-learning techniques, and hybrid sensor-informed frameworks and summarize representative applications in neurosurgery (brain-shift compensation), hepatic, and thoracic interventions. Key translational barriers are discussed: real-time performance, validation against objective error metrics, workflow integration, regulatory pathways, and equitable access. We also address ethical considerations and data stewardship. We recommend prospective clinical trials with measurable endpoints (resection-margin accuracy, intraoperative blood loss, conversion rates, operative duration, and length of stay) and propose a translational roadmap emphasizing multimodal integration, standardization, and clinician training. Coordinated technical and clinical validation could establish deformation-aware AR as a validated intraoperative tool to improve surgical precision and patient outcomes.