Integrating an AI-Driven Hydronephrosis Decision-Making Tool

Trial statusNot yet recruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age0-24
SponsorThe Hospital for Sick Children

About this 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.

Eligibility criteria

Qualifiers

Seen for HN in-person in the Pediatric Urology clinic with ultrasound scans taken at SickKids

New and follow-up patients 0-24 months.

Disqualifiers

Older than 24m

Concurrent urinary tract anomalies (duplex configurations; PUV etc.)

History of renal surgical intervention (post-op patients)

Trial design

Treatments tested in this trial

  • Machine learning model

Treatment groups

322 Participants
are divided into 1 treatment group

Locations

This trial has no locations

Sponsors and collaborators