Integrating an AI-Driven Hydronephrosis Decision-Making Tool

Starting soon · Not applicable

Conditions studied: Hydronephrosis, Hydronephrosis Congenital

In brief

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.

Key facts

Study ID

NCT07581223

Run by

The Hospital for Sick Children

People needed

322

Starts

2026-08-01

Expected to finish

2027-01-31

Last updated by the study team

2026-05-12

Who can join

Age: any, up to 2. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

• Seen for HN in-person in the Pediatric Urology clinic with ultrasound scans taken at SickKids
• New and follow-up patients 0-24 months.

You may not qualify if…

• Older than 24m
• Concurrent urinary tract anomalies (duplex configurations; PUV etc.)
• History of renal surgical intervention (post-op patients)

Full record on ClinicalTrials.gov

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