Head and neck cancers have one of the highest recurrence rates among solid malignancies, and recurrence is strongly correlated with overall survival. Reducing recurrence rates depends, in part, on the surgeon's ability to accurately re-resect areas of positive or close margins during surgery. Currently, margin status is communicated primarily through verbal descriptions between the surgeon and pathologist, which can be imprecise. This challenge is further compounded by the deformable nature of soft tissues, as once the specimen is resected, the shape and size of the specimen change, making it difficult to accurately map the specimen's margins back onto the surgical site. Emerging technologies -such as augmented reality (AR), 3D scanning, and advanced soft tissue modeling- offer promising solutions for improving surgical navigation and precision. Building on these advances, an AR-based surgical navigation system was developed specifically for head and neck tumor resections. The system uses a 3D scanner to generate virtual models of both the resected specimen and the patient's surgical site, as demonstrated in prior work. A soft tissue modeling algorithm is then applied to account for specimen shrinkage and deformation, enabling accurate tracking of positive tumor margins. This guidance information is visualized through an AR headset, which overlays the margin data directly onto the patient's surgical site, providing surgeons with real-time visual guidance during re-resection. In this study, the goal is to evaluate the benefits and usability of this novel navigation software, compared to the standard of care. By assessing surgeon performance and user experience in cadaveric tasks with and without the AR system to identify strengths, limitations, and opportunities for refinement of the system, ultimately advancing surgical precision and improving patient outcomes by reducing recurrence rates.
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Performance Task accuracy (e.g., resection precision)
Timeframe: within 90 minutes of AR-guided use
User Experience
Timeframe: immediately after the AR-guided task.
Accuracy of overlay alignment
Timeframe: within 90 minutes of completing the AR-guided task.
Jie Ying Wu Assistant Professor of Computer Science, PhD