An interventional study of Machine learning model in Hydronephrosis and Hydronephrosis Congenital, sponsored by The Hospital for Sick Children. Not yet recruiting. Open to participants aged 0 Months to 24 Months. Per ClinicalTrials.gov, last updated 2026-05-12.
Sponsored by The Hospital for Sick Children · Not applicable, Interventional, and Other
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.
55 studies on the registry are indexed under Hydronephrosis; 9 are open to participants now.
This study's planned enrollment of 322 is above the median of 100 across 23 interventional studies indexed under Hydronephrosis.
Browse Hydronephrosis studies →The Hospital for Sick Children is the lead sponsor of 578 studies on the registry; 83 are open to participants now.
Of its 7 completed or terminated interventional studies of FDA-regulated products, 1 (14%) have results posted.
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Exclusion Criteria:
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.
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.
Time frame: Immediately after AI model exposure during each case review session, through study completion (average of 6 months)
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.
Time frame: 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.
Time frame: Immediately after AI model exposure during each case review session, through study completion (average of 6 months)
No study locations are listed for this record.
Start is estimated for Aug 2026; primary completion for Jan 2027, 5 months later.
How these figures are worked out, and which studies are left out of them.
This study is not yet recruiting, as verified in May 2026. You cannot join it, but the record below documents what was studied.
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