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Deformable Tissue Modelling and Augmented Reality Based Guidance for Head and Neck Tumor Re-Resection Task (SPeAR)

2026年8月5日 更新者:Michael Topf、Vanderbilt University Medical Center

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

介入

入学 (推定)

30

段階

  • 初期フェーズ 1

連絡先と場所

このセクションには、調査を実施する担当者の連絡先の詳細と、この調査が実施されている場所に関する情報が記載されています。

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

    • Tennessee
      • Nashville、Tennessee、アメリカ、37232
        • 募集
        • Vanderbilt University Medical Center
        • コンタクト:

参加基準

研究者は、適格基準と呼ばれる特定の説明に適合する人を探します。これらの基準のいくつかの例は、人の一般的な健康状態または以前の治療です。

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

いいえ

調査対象母集団

Surgeon-physician, surgical fellow, or post-graduate year 1, 2, 3, 4 and 5 (PGY2-5) resident physicians

説明

Inclusion Criteria:

  1. Post-graduate year 1, 2, 3, 4 and 5 (PGY2-5) resident physicians. (no age limit)
  2. Surgical fellows.
  3. Attending physicians.
  4. Prior cadaver lab or surgical experience.
  5. 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

このセクションでは、研究がどのように設計され、研究が何を測定しているかなど、研究計画の詳細を提供します。

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
Surgeon performance of target re-localization compared with and without the AR-headset.
within 90 minutes of AR-guided use
User Experience
時間枠:immediately after the AR-guided task.
Assess AR usability, ease of use, and comfort, through surgeon feedback surveys
immediately after the AR-guided task.
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.
within 90 minutes of completing the AR-guided task.

協力者と研究者

ここでは、この調査に関係する人々や組織を見つけることができます。

捜査官

  • 主任研究者:Michael Topf, MD、Vanderbilt University Medical Center

研究記録日

これらの日付は、ClinicalTrials.gov への研究記録と要約結果の提出の進捗状況を追跡します。研究記録と報告された結果は、国立医学図書館 (NLM) によって審査され、公開 Web サイトに掲載される前に、特定の品質管理基準を満たしていることが確認されます。

主要日程の研究

研究開始 (実際)

2026年2月18日

一次修了 (推定)

2029年6月1日

研究の完了 (推定)

2029年6月1日

試験登録日

最初に提出

2026年6月30日

QC基準を満たした最初の提出物

2026年6月30日

最初の投稿 (実際)

2026年7月7日

学習記録の更新

投稿された最後の更新 (実際)

2026年8月10日

QC基準を満たした最後の更新が送信されました

2026年8月5日

最終確認日

2026年6月1日

詳しくは

本研究に関する用語

その他の研究ID番号

  • 251090

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?

未定

IPD プランの説明

NIH requires through Data Management and Sharing Plans (DMSP) that all data collected during the research project be archived indefinitely and shared with the community after the project termination. De-identified data will then be transferred to Open Science Framework (OSF).

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

いいえ

米国FDA規制機器製品の研究

いいえ

この情報は、Web サイト clinicaltrials.gov から変更なしで直接取得したものです。研究の詳細を変更、削除、または更新するリクエストがある場合は、register@clinicaltrials.gov。 までご連絡ください。 clinicaltrials.gov に変更が加えられるとすぐに、ウェブサイトでも自動的に更新されます。

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