Inteligencia artificial autónoma versus diagnóstico óptico humano asistido por IA (CADx-Prosp)
Inteligencia artificial autónoma versus diagnóstico óptico humano asistido por IA de pólipos colorrectales: un ensayo controlado aleatorio
Computer-aided image-enhanced endoscopy can predict the nature of colorectal polyps with over 90% accuracy. This technology uses artificial intelligence (AI) to analyze video recordings of polyps, learning to make diagnoses in real-time. This means that doctors can get immediate predictions about small polyps during the procedure, reducing the need for separate pathology exams and saving costs, ultimately improving patient care.
Human and AI interactions are complex and a framework to reap synergistic effects CADx systems when used by humans to harness optimal performance needs to be established. AI solutions in medicine are usually developed to be used as assistive devices, however, then they rely on humans to correct AI errors. Optical polyp diagnosis is a complex task. Non experts usually achieve diagnostic accuracy in 70-80%. CADx systems have a similar diagnostic accuracy when used autonomously. Clinical evaluation of CADx systems showed that CADx assisted OD performs equally to the operator performance when using non CADx assisted OD. To harness a benefit of clinical CADx implementation we would have to find a way that synergies between human and CADx come into play to eliminate cases in which CADx assisted and/ or human OD results in low diagnostic accuracy and also addresses the problem of serrated polyp recognition.
Descripción general del estudio
Estado
Condiciones
Intervención / Tratamiento
Descripción detallada
Our study hypothesis is that for CADx implementation, instead of using the high/low confidence framework, identifying cases with suboptimal diagnostic accuracy could be facilitated through identifying cases in which CADx and endoscopist disagreed in their diagnosis. Eliminating such cases might separate out cases with low accuracy when using CADx assisted OD. Since endoscopists have a high sensitivity but low specificity for serrated polyp OD, this framework will also allow us to implement a strategy to adequately manage serrated polyps found in the cohort.
Tipo de estudio
Inscripción (Estimado)
Fase
- No aplica
Contactos y Ubicaciones
Estudio Contacto
- Nombre: Daniel von Renteln, MD
- Número de teléfono: 30912 514 890-8000
- Correo electrónico: daniel.von.renteln.med@ssss.gouv.qc.ca
Ubicaciones de estudio
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Quebec
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Montreal, Quebec, Canadá
- Reclutamiento
- Ghislaine Ahoua
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Contacto:
- Daniel von Renteln, MD
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Criterios de participación
Criterio de elegibilidad
Edades elegibles para estudiar
Acepta Voluntarios Saludables
Géneros elegibles para el estudio
Descripción
Inclusion Criteria:
- Indication for full colonoscopy.
Exclusion Criteria:
- Known inflammatory bowel disease
- Active colitis
- coagulopathy
- familial polyposis syndrome
- poor general health, defined as an American Society of Anesthesiologists class >3
- emergency colonoscopy
Plan de estudios
¿Cómo está diseñado el estudio?
Detalles de diseño
- Propósito principal: Diagnóstico
- Asignación: N / A
- Modelo Intervencionista: Asignación de un solo grupo
- Enmascaramiento: Ninguno (etiqueta abierta)
Armas e Intervenciones
Grupo de participantes/brazo |
Intervención / Tratamiento |
|---|---|
|
Otro: All participants
The endoscopist will make an optical diagnosis (OD) prediction for all small polyps (up to 10 mm) in white light (WL). Then, the endoscopist will make another OD prediction using image enhanced endoscopy (IEE) modes. After that, CADx will be activated in the IEE mode and a CADx prediction will be documented. Finally, after seeing the CADx prediction, the endoscopist will make a final prediction, which can agree or disagree with the autonomous CADx one. Polyps will be resected and sent to a pathology lab, where a pathologic diagnosis (blinded to the endoscopist's predictions) will be rendered. |
The CADx system will be used to predict the histopathology of the polyp detected. |
¿Qué mide el estudio?
Medidas de resultado primarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
|
Accuracy of optical diagnosis, for polyps 1-5mm, compared with an agreed upon CADx-assisted diagnosis
Periodo de tiempo: up to 100 weeks
|
Accuracy of optical diagnosis, for polyps 1-5mm, compared with an agreed upon CADx-assisted diagnosis , when histopathology results are used as the reference |
up to 100 weeks
|
Medidas de resultado secundarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
|
Accuracy of optical diagnosis, for polyps 1-10mm, compared with an agreed upon CADx-assisted diagnosis
Periodo de tiempo: up to 100 weeks
|
Accuracy of optical diagnosis, for polyps 1-10mm, compared with an agreed upon CADx-assisted diagnosis, when histopathology results are used as the reference |
up to 100 weeks
|
|
Test characteristics, including recall, specificity, positive and negative predictive values (PPV/NPV), and particularly the NPV of rectosigmoid neoplastic polyps.
Periodo de tiempo: up to 100 weeks
|
A ≥90% NPV will be used as a quality benchmark for a strategy to not resect such diminutive polyps. |
up to 100 weeks
|
|
Agreement of surveillance interval recommendations of AI-A and AI-H compared with the pathology-based recommendations
Periodo de tiempo: up to 100 weeks
|
For surveillance interval assignment, the pathology results of concomitant polyps >5 mm (including multiple concomitant polyps of all sizes and histology) will be considered when calculating the surveillance interval recommendation. Surveillance recommendations will be based on the 2020 United States Multi Society Task Force Guidelines as is current standard of practice at our center. |
up to 100 weeks
|
|
Proportion of patients for whom an immediate surveillance recommendation can be directly provided for each approach, and how often histopathology-based polyp examination would have been avoided.
Periodo de tiempo: up to 100 weeks
|
The potential cost-effectiveness of OD (either approaches) will be evaluated using the measured described above. |
up to 100 weeks
|
|
Variability of OD (AI-A and AI-H) across participating endoscopists.
Periodo de tiempo: up to 100 weeks
|
Each participating endoscopist will conduct a similar number of optical diagnoses to assess endoscopist-related factors. |
up to 100 weeks
|
|
Cost-effectiveness of OD ((AI-A and AI-H)
Periodo de tiempo: up to 100 weeks
|
A cost-effectiveness model will be applied to better quantify costs and understand cost impact including key cost drivers when generalized to a broader screening population. |
up to 100 weeks
|
Colaboradores e Investigadores
Investigadores
- Investigador principal: Daniel von Renteln, MD, University of Montreal Medical Center (CHUM)
Fechas de registro del estudio
Fechas importantes del estudio
Inicio del estudio (Actual)
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
Otros números de identificación del estudio
- 2025-12306
- 2025 (Subvención/contrato del NIH de EE. UU.)
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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