Integrating an AI-Driven Hydronephrosis Decision-Making Tool
6 de mayo de 2026 actualizado por: Mandy Rickard, The Hospital for Sick Children
Integration of a Hydronephrosis AI-Driven Decision-Making Tool Into Clinical Practice: A Clinical Trial
Hydronephrosis is a common congenital kidney anomaly.
While most cases resolve on their own, some require surgery.
Clinicians rely on repeated ultrasounds and sometimes invasive tests to decide if surgery is needed, but predicting outcomes is difficult.
Researchers at SickKids developed an AI model that analyzes ultrasound images to assist in diagnosing and managing hydronephrosis.
This study tests how well the AI integrates into real-world care.
Clinicians will first make care decisions without AI and then review the AI's prediction before deciding whether to change their plan.
A separate expert, unaware of whether AI influenced the first clinician's plan, will make the final decision to ensure care remains unchanged.
The study will assess whether AI improves decision-making, reduces unnecessary tests, and fits into clinical workflows.
If successful, the AI model could serve as a complementary tool to make diagnoses more efficient and precise while minimizing invasive procedures.
Descripción general del estudio
Estado
Aún no reclutando
Condiciones
Intervención / Tratamiento
Tipo de estudio
Intervencionista
Inscripción (Estimado)
322
Fase
- No aplica
Criterios de participación
Los investigadores buscan personas que se ajusten a una determinada descripción, denominada criterio de elegibilidad. Algunos ejemplos de estos criterios son el estado de salud general de una persona o tratamientos previos.
Criterio de elegibilidad
Edades elegibles para estudiar
- Niño
Acepta Voluntarios Saludables
No
Descripción
Inclusion Criteria:
- Seen for HN in-person in the Pediatric Urology clinic with ultrasound scans taken at SickKids
- New and follow-up patients 0-24 months.
Exclusion Criteria:
- Older than 24m
- Concurrent urinary tract anomalies (duplex configurations; PUV etc.)
- History of renal surgical intervention (post-op patients)
Plan de estudios
Esta sección proporciona detalles del plan de estudio, incluido cómo está diseñado el estudio y qué mide el estudio.
¿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 |
|---|---|
|
Experimental: AI Model Intervention Arm
When children with HN are seen in clinic, their ultrasound imaging and history will be provided to an initial clinician who will first formulate a plan of care without access to the AI model as per the standard of care.
After the initial plan is documented and before discussion with the primary provider, the initial clinician will then be granted access to the AI model, where they can input the ultrasound images and receive the model's prediction.
The clinician can choose to modify or maintain their drafted plan based on the model's output.
The clinician's final drafted plan will subsequently be discussed with the blinded final clinical expert (primary provider) who will make the final decision to maintain the standard of care for each patient.
The final clinical expert will be blinded to whether the initial clinician changed their plan or not given the AI model
|
The AI intervention is a deep learning algorithm used to predict obstructive hydronephrosis.
It was developed at SickKids and has recently completed the silent trial phase.
This clinical trial aims to validate the model's clinical integration by assessing its impact on clinician decision-making and care plan recommendations.
To uphold standard care, a blinded clinician will make final decisions.
|
¿Qué mide el estudio?
Medidas de resultado primarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
|
Change in Clinician Management Decisions Following Exposure to the AI Model
Periodo de tiempo: Immediately after AI model exposure during each case review session, through study completion (average of 6 months)
|
The proportion of clinician management decisions revised immediately after exposure to the AI model output.
Management decisions include: (1) discharge, (2) monitor with ultrasound, (3) additional invasive testing, or (4) referral for surgery.
|
Immediately after AI model exposure during each case review session, through study completion (average of 6 months)
|
Medidas de resultado secundarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
|
Agreement Between Clinician Decisions and Expert Reference Decisions Using Cohen's Kappa
Periodo de tiempo: Immediately after clinician review and AI model exposure during each case review session, through study completion (average of 6 months)
|
Agreement between clinician management decisions and the expert reference decision will be assessed before and after AI exposure using Cohen's kappa statistic.
Higher kappa values indicate greater agreement.
|
Immediately after clinician review and AI model exposure during each case review session, through study completion (average of 6 months)
|
|
Proportion of Management Decision Changes Stratified by Clinician Experience Level
Periodo de tiempo: Immediately after AI model exposure during each case review session, through study completion (average of 6 months)
|
The proportion of clinician management decisions revised after AI model exposure will be compared across clinician subgroups, including training level and years of experience.
|
Immediately after AI model exposure during each case review session, through study completion (average of 6 months)
|
Colaboradores e Investigadores
Aquí es donde encontrará personas y organizaciones involucradas en este estudio.
Patrocinador
Fechas de registro del estudio
Estas fechas rastrean el progreso del registro del estudio y los envíos de resultados resumidos a ClinicalTrials.gov. Los registros del estudio y los resultados informados son revisados por la Biblioteca Nacional de Medicina (NLM) para asegurarse de que cumplan con los estándares de control de calidad específicos antes de publicarlos en el sitio web público.
Fechas importantes del estudio
Inicio del estudio (Estimado)
1 de agosto de 2026
Finalización primaria (Estimado)
31 de enero de 2027
Finalización del estudio (Estimado)
31 de enero de 2027
Fechas de registro del estudio
Enviado por primera vez
16 de abril de 2026
Primero enviado que cumplió con los criterios de control de calidad
6 de mayo de 2026
Publicado por primera vez (Actual)
12 de mayo de 2026
Actualizaciones de registros de estudio
Última actualización publicada (Actual)
12 de mayo de 2026
Última actualización enviada que cumplió con los criterios de control de calidad
6 de mayo de 2026
Última verificación
1 de mayo de 2026
Más información
Términos relacionados con este estudio
Términos MeSH relevantes adicionales
Otros números de identificación del estudio
- 3474
Información sobre medicamentos y dispositivos, documentos del estudio
Estudia un producto farmacéutico regulado por la FDA de EE. UU.
No
Estudia un producto de dispositivo regulado por la FDA de EE. UU.
No
Esta información se obtuvo directamente del sitio web clinicaltrials.gov sin cambios. Si tiene alguna solicitud para cambiar, eliminar o actualizar los detalles de su estudio, comuníquese con register@clinicaltrials.gov. Tan pronto como se implemente un cambio en clinicaltrials.gov, también se actualizará automáticamente en nuestro sitio web. .