- ICH GCP
- Registro de ensayos clínicos de EE. UU.
- Ensayo clínico NCT07568366
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).
Descripción general del estudio
Estado
Condiciones
Tipo de estudio
Inscripción (Estimado)
Fase
- Fase temprana 1
Contactos y Ubicaciones
Ubicaciones de estudio
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London, Reino Unido
- National Hospital For Neurology and Neurosurgery
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Criterios de participación
Criterio de elegibilidad
Edades elegibles para estudiar
- Adulto
- Adulto Mayor
Acepta Voluntarios Saludables
Descripción
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
Plan de estudios
¿Cómo está diseñado el estudio?
Detalles de diseño
- Propósito principal: Otro
- Asignación: N / A
- Modelo Intervencionista: Asignación de un solo grupo
- Enmascaramiento: Ninguno (etiqueta abierta)
Armas e Intervenciones
Grupo de participantes/brazo |
Intervención / Tratamiento |
|---|---|
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Experimental: Brazo de intervención
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Live intra-op AI analysis of endoscopic video feed, with output displayed on supplementary monitor
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¿Qué mide el estudio?
Medidas de resultado primarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
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Feasibility of live AI video analysis
Periodo de tiempo: 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).
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Immediately after the intervention/procedure/surgery
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Medidas de resultado secundarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
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Safety
Periodo de tiempo: 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
Periodo de tiempo: Immediately after the intervention/procedure/surgery
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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
Periodo de tiempo: Through study completion, an average of 1 year
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Through study completion, an average of 1 year
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Colaboradores e Investigadores
Patrocinador
Colaboradores
Publicaciones y enlaces útiles
Publicaciones Generales
- 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.
Fechas de registro del estudio
Fechas importantes del estudio
Inicio del estudio (Estimado)
Finalización primaria (Estimado)
Finalización del estudio (Estimado)
Fechas de registro del estudio
Enviado por primera vez
Primero enviado que cumplió con los criterios de control de calidad
Publicado por primera vez (Actual)
Actualizaciones de registros de estudio
Última actualización publicada (Actual)
Última actualización enviada que cumplió con los criterios de control de calidad
Última verificación
Más información
Términos relacionados con este estudio
Palabras clave
Términos MeSH relevantes adicionales
- Enfermedades del sistema endocrino
- Enfermedades Cerebrales
- Enfermedades del Sistema Nervioso Central
- Enfermedades del Sistema Nervioso
- Neoplasias por sitio
- Neoplasias
- Neoplasias de glándulas endocrinas
- Neoplasias del Sistema Nervioso
- Neoplasias del Sistema Nervioso Central
- Enfermedades hipotalámicas
- Neoplasias hipotalámicas
- Neoplasias Supratentoriales
- Neoplasias Cerebrales
- Enfermedades de la pituitaria
- Neoplasias hipofisarias
Otros números de identificación del estudio
- 127474
Plan de datos de participantes individuales (IPD)
¿Planea compartir datos de participantes individuales (IPD)?
Descripción del plan IPD
Marco de tiempo para compartir IPD
Criterios de acceso compartido de IPD
Tipo de información de apoyo para compartir IPD
- PROTOCOLO DE ESTUDIO
- CIF
- RSC
Información sobre medicamentos y dispositivos, documentos del estudio
Estudia un producto farmacéutico regulado por la FDA de EE. UU.
Estudia un producto de dispositivo regulado por la FDA de EE. UU.
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