AI in Endoscopic Transsphenoidal Surgery
The Application of Artificial Intelligence to Patients Undergoing Endoscopic Transsphenoidal Surgery: a Single-site Prospective Feasibility and Exploratory Study (IDEAL Stage 1 and 2a)
This study focuses on bringing artificial intelligence into the operating room to assist with pituitary tumour surgeries performed through the nose. These procedures are technically demanding, and training new surgeons is often inconsistent. To address this, researchers at the National Hospital for Neurology and Neurosurgery are testing AI systems that "watch" surgical videos in real-time to identify anatomy, instruments, and the specific phase of the operation.
The core goal of the prospective trial is to improve education and team coordination without interfering with the surgery itself. The AI displays its analysis on tablets positioned for the surgical residents and nurses, rather than the lead surgeon. This setup allows the team to follow the procedure's progress, key anatomy and anticipate next steps without the surgeon needing to stop and explain. Because hospital internet can be unreliable, the study is prioritizing specialized hardware from NVIDIA that processes data locally. This "edge computing" approach ensures the AI is fast and doesn't require a live cloud connection to function.
This trial will assess the device feasibility (IDEAL Stage 1 study, ~6 cases), followed by early safety and system technical refinement (IDEAL 2a study, ~20-30 cases).
調査の概要
状態
条件
研究の種類
入学 (推定)
段階
- 初期フェーズ 1
連絡先と場所
研究場所
-
-
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London、イギリス
- National Hospital For Neurology and Neurosurgery
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参加基準
適格基準
就学可能な年齢
- 大人
- 高齢者
健康ボランティアの受け入れ
説明
The inclusion criteria will be:
- Adult patients (above the age of 18 years old)
- Undergoing endoscopic transsphenoidal surgery
- Able to provide consent
The exclusion criteria will be:
- Patients less than 18 years of age
- Undergoing transcranial surgery or microscopic transsphenoidal surgery
- Unable to provide consent e.g., cannot understand, mental illness, or later withdrawing consent
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:他の
- 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 |
|---|---|
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実験的:介入アーム
|
Live intra-op AI analysis of endoscopic video feed, with output displayed on supplementary monitor
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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 |
|---|---|---|
|
Feasibility of live AI video analysis
時間枠:Immediately after the intervention/procedure/surgery
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The primary objective of this study is to evaluate the feasibility of the TouchSurgery platform or NVIDIA AGx/IGx based platforms for prospective AI-based surgical video analysis (via observation, validated implementation assessment and human factors questionnaires; and semi-structured interviews of surgical team members).
|
Immediately after the intervention/procedure/surgery
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二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Safety
時間枠:Perioperatively/periprocedurally (surgeon distraction, team disruption); and immediately after the intervention/procedure/surgery (output accuracy, volatility and latency)
|
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Perioperatively/periprocedurally (surgeon distraction, team disruption); and immediately after the intervention/procedure/surgery (output accuracy, volatility and latency)
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Educational yield
時間枠:Immediately after the intervention/procedure/surgery
|
To evaluate the utility of the platform for educational purposes. Via structured educational yield questionnaire of surgeons involved in each case |
Immediately after the intervention/procedure/surgery
|
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Surgical outcomes
時間枠:Through study completion, an average of 1 year
|
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Through study completion, an average of 1 year
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協力者と研究者
出版物と役立つリンク
一般刊行物
- Hirst A, Philippou Y, Blazeby J, Campbell B, Campbell M, Feinberg J, Rovers M, Blencowe N, Pennell C, Quinn T, Rogers W, Cook J, Kolias AG, Agha R, Dahm P, Sedrakyan A, McCulloch P. No Surgical Innovation Without Evaluation: Evolution and Further Development of the IDEAL Framework and Recommendations. Ann Surg. 2019 Feb;269(2):211-220. doi: 10.1097/SLA.0000000000002794.
- Valetopoulou A, Newall N, Khan DZ, Borg A, Bouloux PMG, Bremner F, Buchfelder M, Cudlip S, Dorward N, Drake WM, Fernandez-Miranda JC, Fleseriu M, Geltzeiler M, Ginn J, Gurnell M, Harris S, Jaunmuktane Z, Korbonits M, Kosmin M, Koulouri O, Horsfall HL, Mamelak AN, Mannion R, McBride P, McCormack AI, Melmed S, Miszkiel KA, Raverot G, Santarius T, Schwartz TH, Serrano I, Zada G, Baldeweg SE, Marcus HJ, Kolias AG; PitCOP Collaborators. A core outcome set for pituitary surgery research: an international delphi consensus study. Pituitary. 2025 Jul 23;28(4):88. doi: 10.1007/s11102-025-01553-w.
- Newall N, Khan DZ, Hanrahan JG, Booker J, Borg A, Davids J, Nicolosi F, Sinha S, Dorward N, Marcus HJ. High fidelity simulation of the endoscopic transsphenoidal approach: Validation of the UpSurgeOn TNS Box. Front Surg. 2022 Dec 6;9:1049685. doi: 10.3389/fsurg.2022.1049685. eCollection 2022.
- Khan DZ, Newall N, Koh CH, Das A, Aapan S, Layard Horsfall H, Baldeweg SE, Bano S, Borg A, Chari A, Dorward NL, Elserius A, Giannis T, Jain A, Stoyanov D, Marcus HJ. Video-Based Performance Analysis in Pituitary Surgery - Part 2: Artificial Intelligence Assisted Surgical Coaching. World Neurosurg. 2024 Oct;190:e797-e808. doi: 10.1016/j.wneu.2024.07.219. Epub 2024 Aug 8.
- Khan DZ, Valetopoulou A, Das A, Hanrahan JG, Williams SC, Bano S, Borg A, Dorward NL, Barbarisi S, Culshaw L, Kerr K, Luengo I, Stoyanov D, Marcus HJ. Artificial intelligence assisted operative anatomy recognition in endoscopic pituitary surgery. NPJ Digit Med. 2024 Nov 9;7(1):314. doi: 10.1038/s41746-024-01273-8.
研究記録日
主要日程の研究
研究開始 (推定)
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
追加の関連 MeSH 用語
その他の研究ID番号
- 127474
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
IPD 共有時間枠
IPD 共有アクセス基準
IPD 共有サポート情報タイプ
- STUDY_PROTOCOL
- ICF
- CSR
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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