HydronephrosisNCT07581223
Integrating an AI-Driven Hydronephrosis Decision-Making Tool
A study testing Machine learning model in people with Hydronephrosis and Hydronephrosis Congenital, run by The Hospital for Sick Children.
Trial 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. Read more about this study
- Who is it for?
- Ages 0 to 24 months
- Conditions the study is for
- Hydronephrosis, Hydronephrosis Congenital
- What is being tested?
- Machine learning model
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 322 participants
- Where
- No sites listed yet
- Study dates
- 1 Aug 2026to 31 Jan 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 0 to 24 months
- All sexes
- No healthy volunteers
- Child
Who may be able to join
- Seen for HN in-person in the Pediatric Urology clinic with ultrasound scans taken at SickKids
- New and follow-up patients 0-24 months.
Who cannot join
- Older than 24m
- Concurrent urinary tract anomalies (duplex configurations; PUV etc.)
- History of renal surgical intervention (post-op patients)
About this study
Official title: 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.
- Conditions
- Hydronephrosis, Hydronephrosis Congenital
- Being tested
- Machine learning model
- Medical subject headings
- Hydronephrosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Machine learning modelOther
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.
| Study group | Role | What they receive |
|---|---|---|
| AI Model Intervention Arm | Experimental | 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 Receives: Other: Machine learning model |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Immediately after AI model exposure during each case review session, through study completion (average of 6 months)
Secondary outcomes (2)
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.
Measured: 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.
Measured: Immediately after AI model exposure during each case review session, through study completion (average of 6 months)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 May 2026
- Study start1 Aug 2026 (estimated)
- Primary completion31 Jan 2027 (estimated)
- Study completion31 Jan 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for HydronephrosisStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- Model description
- The intervention is an AI algorithm for the prediction of obstructive HN. 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.
- Masking
- None (open label)
- Enrolment
- 322 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The Hospital for Sick Children
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Mandy Rickard, The Hospital for Sick Children
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07581223
- Sponsor's protocol ID
- 3474
- Status last verified
- May 2026
- First submitted
- 16 Apr 2026
- Last updated
- 12 May 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 12 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Integration of a Hydronephrosis AI-Driven Decision-Making Tool Into Clinical Practice: A Clinical Trial
- ClinicalTrials.gov ID
- NCT07581223
- Acronym / sponsor ID
- 3474
- Conditions
- Hydronephrosis, Hydronephrosis Congenital
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 322 participants (estimated)
- Lead sponsor
- The Hospital for Sick ChildrenUniversity, hospital or other
- Study start
- 1 Aug 2026 (estimated)
- Primary completion
- 31 Jan 2027 (estimated)
- Study completion
- 31 Jan 2027 (estimated)
- First posted
- 12 May 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- The intervention is an AI algorithm for the prediction of obstructive HN. 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.
| Arm | Type | Receives |
|---|---|---|
| AI Model Intervention Arm | Experimental | Other: Machine learning modelWhen 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 |
Who may qualify
- Ages
- 0 to 24 months
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child
Key inclusion criteria (2 listed)
- Seen for HN in-person in the Pediatric Urology clinic with ultrasound scans taken at SickKids
- New and follow-up patients 0-24 months.
Key exclusion criteria (3 listed)
- Older than 24m
- Concurrent urinary tract anomalies (duplex configurations; PUV etc.)
- History of renal surgical intervention (post-op patients)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Machine learning modelThe 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. | Other | AI Model Intervention Arm |
Primary outcome
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.
2 secondary outcomes
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)
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)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 May 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
A study testing Machine learning model in people with Hydronephrosis and Hydronephrosis Congenital, run by The Hospital for Sick Children.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 12 May 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 May 2026.