Integrating an AI-Driven Hydronephrosis Decision-Making Tool

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

Study Overview

• Status: Not yet recruiting
• Conditions: Hydronephrosis; Hydronephrosis Congenital
• Intervention / Treatment: Other: Machine learning model

• Study Type: Interventional
• Enrollment (Estimated): 322
• Phase: Not Applicable

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child
• Accepts Healthy Volunteers: No

Description

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)

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Other
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm

Intervention / Treatment

Experimental: AI Model Intervention Arm; When children with HN are seen in clinic, their ultrasound imaging and history will be provided to an initial clinician who will first formulate a plan of care without access to the AI model as per the standard of care. After the initial plan is documented and before discussion with the primary provider, the initial clinician will then be granted access to the AI model, where they can input the ultrasound images and receive the model's prediction. The clinician can choose to modify or maintain their drafted plan based on the model's output. The clinician's final drafted plan will subsequently be discussed with the blinded final clinical expert (primary provider) who will make the final decision to maintain the standard of care for each patient. The final clinical expert will be blinded to whether the initial clinician changed their plan or not given the AI model

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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Change in Clinician Management Decisions Following Exposure to the AI Model	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.	Immediately after AI model exposure during each case review session, through study completion (average of 6 months)

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Agreement Between Clinician Decisions and Expert Reference Decisions Using Cohen's Kappa	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.	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	The proportion of clinician management decisions revised after AI model exposure will be compared across clinician subgroups, including training level and years of experience.	Immediately after AI model exposure during each case review session, through study completion (average of 6 months)

Collaborators and Investigators

• Sponsor: The Hospital for Sick Children

Study record dates

Study Major Dates

• Study Start (Estimated): August 1, 2026
• Primary Completion (Estimated): January 31, 2027
• Study Completion (Estimated): January 31, 2027

Study Registration Dates

• First Submitted: April 16, 2026
• First Submitted That Met QC Criteria: May 6, 2026
• First Posted (Actual): May 12, 2026

Study Record Updates

• Last Update Posted (Actual): May 12, 2026
• Last Update Submitted That Met QC Criteria: May 6, 2026
• Last Verified: May 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Urogenital Diseases; Male Urogenital Diseases; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Hydronephrosis
• Other Study ID Numbers: 3474

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No