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NCT Number: NCT07581223

Integrating an AI-Driven Hydronephrosis Decision-Making Tool

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.

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Key information

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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)

Treatment and study plan

Machine learning model

Other

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 outcomes

  1. 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.

Secondary outcomes

  1. 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.

  2. 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.

Sponsors and collaborators

Lead sponsor

The Hospital for Sick Children

Other

Registry information

Official study title

Integration of a Hydronephrosis AI-Driven Decision-Making Tool Into Clinical Practice: A Clinical Trial

Important dates

Study start
2026
Primary completion
2027
Study completion
2027
First posted
May 12, 2026
Registry last updated
May 12, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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