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Integrating an AI-Driven Hydronephrosis Decision-Making Tool

NCT07581223 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 322

Last updated 2026-05-12

No results posted yet for this study

Summary

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.

Conditions

  • Hydronephrosis
  • Hydronephrosis Congenital

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.

Sponsors & Collaborators

  • The Hospital for Sick Children

    lead OTHER

Study Design

Allocation
NA
Purpose
OTHER
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
0 Months
Max Age
24 Months
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-08-01
Primary Completion
2027-01-31
Completion
2027-01-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07581223 on ClinicalTrials.gov