Integrating an AI-Driven Hydronephrosis Decision-Making Tool
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Machine learning model.
- Who it may be relevant to
- Registry conditions: Hydronephrosis, Hydronephrosis Congenital. Basic parameters: 0 months — 24 months · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
Integration of a Hydronephrosis AI-Driven Decision-Making Tool Into Clinical Practice: A Clinical Trial
Overview
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.
Interventions
- Other Machine learning 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.
Primary outcome measures
- 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)]
Secondary outcome measures (2)
- 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)]
- 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)]
Eligibility criteria
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)
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- N/A
- Model
- Single group
- Masking
- Open label
- Primary purpose
- Other
Study locations
Center list to be confirmed — check the primary protocol.
Identifiers
NCT: NCT07581223 · 3474