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.
Post-graduate year 1, 2, 3, 4 and 5 (PGY2-5) resident physicians. (no age limit)
Surgical fellows.
Attending physicians.
Prior cadaver lab or surgical experience.
None
Single group
Augmented Reality
Other interventionTask accuracy will be evaluated by measuring distances between the points identified with and without AR guidance, and the pathologist-intended target locations. Participants will then complete post-tasks surveys and interviews.
Performance Task accuracy (e.g., resection precision)
Surgeon performance of target re-localization compared with and without the AR-headset.
User Experience
Assess AR usability, ease of use, and comfort, through surgeon feedback surveys
Accuracy of overlay alignment
This will validate the accuracy of overlay alignment through landmark-based (tumor margin relocation) error metrics, which support precision of re-resection tasks.
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Vanderbilt University Medical Center
Lead sponsor
Vanderbilt University
Collaborator
