Deformable Tissue Modelling and Augmented Reality Based Guidance for Head and Neck Tumor Re-Resection Task
This is not medical advice. AI-assisted translation — inaccuracies may occur. Always verify the original and consult your oncologist before taking any steps.
About the trial
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.
Original English text from ClinicalTrials.gov
Who can (and can't) join
✓ Qualifies
- •Lekarze-rezydenci na latach 1-5 stażu.
- •Lekarze specjaliści w trakcie dalszego szkolenia.
- •Lekarze posiadający specjalizację.
- •Posiadanie doświadczenia w pracy z preparatami zwłok lub doświadczenia chirurgicznego.
- •Każdy chirurg, który brał udział w ocenie próbek tkankowych (tzw. zamrożone) podczas operacji.
✗ Disqualifies
- •Osoby niebędące lekarzami, które uczestniczą w operacjach.
Simplified criteria — AI translation
Trial details
- Last updated (source)
- July 8, 2026
- Sex
- No restrictions
Therapies / drugs in trial
Locations (1)
Vanderbilt University Medical Center
Nashville, United States
Trial contact
Jie Ying Wu Assistant Professor of Computer Science, PhD
Contact information from ClinicalTrials.gov. Contact in English.
Share this trial
Data from ClinicalTrials.gov. AI-assisted translation, last sync: 7/8/2026.
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