Deformable Tissue Modelling and Augmented Reality Based Guidance for Head and Neck Tumor Re-Resection Task (SPeAR)
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.
調査の概要
詳細な説明
Augmented reality (AR) technology, combined with computer vision algorithms, offers significant potential to enhance surgical visualization by generating GPS-like spatial maps over the patient's anatomy. This study aims to evaluate the usability and impact of our AR surgical guidance system, delivered through Microsoft HoloLens 2 (or equivalent AR/VR goggles such as Magic Leap or Apple Vision Pro), among surgeons while they complete various surgical tasks on cadaveric specimens. Specifically, an assessment of how the AR system influences surgeon performance and user experience during tasks such as suturing and specimen relocation, performed both with and without AR assistance.
Task accuracy (e.g., resection precision) will be measured and survey responses will be collected to assess the system's usability, ease of use, and comfort. Building on prior work where the investigators validated the feasibility and accuracy of AR-guided surgical holograms, this study focuses on advancing the evaluation of the system's usability and impact on performance. The goal is to generate insights into the application of AR guidance in head and neck tumor resection, ultimately contributing to improved intraoperative surgical precision and patient outcomes.
研究の種類
入学 (推定)
段階
- 初期フェーズ 1
連絡先と場所
研究連絡先
- 名前:Jie Ying Wu Assistant Professor of Computer Science, PhD
- 電話番号:615-343-4996
- メール:JieYing.Wu@vanderbilt.edu
研究場所
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Tennessee
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Nashville、Tennessee、アメリカ、37232
- 募集
- Vanderbilt University Medical Center
-
コンタクト:
- Jie Ying Wu Assistant Professor of Computer Science, PhD
- 電話番号:615-343-4996
- メール:JieYing.Wu@vanderbilt.edu
-
-
参加基準
適格基準
就学可能な年齢
- 子
- 大人
- 高齢者
健康ボランティアの受け入れ
調査対象母集団
説明
Inclusion Criteria:
- 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.
- Any surgeon, regardless of training and experience, who has been involved in the surgeon-pathologist interaction during surgical resection for frozen section and margin clearance assessment.
Exclusion Criteria:
1. Non-physician surgery providers.
研究計画
研究はどのように設計されていますか?
デザインの詳細
- 主な目的:デバイスの実現可能性
- 割り当て:なし
- 介入モデル:単一グループの割り当て
- マスキング:なし(オープンラベル)
武器と介入
参加者グループ / アーム |
介入・治療 |
|---|---|
|
他の:Augmented Reality (AR)
Participants will be asked to localize simulated margins on tissue resection beds on a fresh-frozen cadaver head. Specimens of skin, buccal, or tongue tissue will be resected by the research team beforehand. Participants will be asked to place pins or stitches where the indicated targets are located. These positions will be recorded by the research team. Participants will first receive oral guidance only, corresponding to common descriptions between pathologists and surgeons. Participants will then reproduce the same task with AR guidance. In this case, the target will be displayed in the see-through AR headset. The target will be overlaid on the resection bed site and follow your head's movements. |
Task 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. |
この研究は何を測定していますか?
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Performance Task accuracy (e.g., resection precision)
時間枠:within 90 minutes of AR-guided use
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Surgeon performance of target re-localization compared with and without the AR-headset.
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within 90 minutes of AR-guided use
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User Experience
時間枠:immediately after the AR-guided task.
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Assess AR usability, ease of use, and comfort, through surgeon feedback surveys
|
immediately after the AR-guided task.
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Accuracy of overlay alignment
時間枠:within 90 minutes of completing the AR-guided task.
|
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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within 90 minutes of completing the AR-guided task.
|
協力者と研究者
捜査官
- 主任研究者:Michael Topf, MD、Vanderbilt University Medical Center
研究記録日
主要日程の研究
研究開始 (実際)
一次修了 (推定)
研究の完了 (推定)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
IPD プランの説明
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
米国FDA規制機器製品の研究
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