Integrating an AI-Driven Hydronephrosis Decision-Making Tool
Integration of a Hydronephrosis AI-Driven Decision-Making Tool Into Clinical Practice: A Clinical Trial
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Phase
Phase
- Not Applicable
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
Accepts Healthy Volunteers
Description
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)
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Other
- Allocation: N/A
- Interventional Model: Single Group Assignment
- Masking: None (Open Label)
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
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.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Change in Clinician Management Decisions Following Exposure to the AI Model
Time Frame: 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)
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Agreement Between Clinician Decisions and Expert Reference Decisions Using Cohen's Kappa
Time Frame: 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
Time Frame: 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)
|
Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (Estimated)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 3474
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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