Integrating an AI-Driven Hydronephrosis Decision-Making Tool
6. maj 2026 opdateret af: Mandy Rickard, The Hospital for Sick Children
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.
Studieoversigt
Status
Ikke rekrutterer endnu
Betingelser
Intervention / Behandling
Undersøgelsestype
Interventionel
Tilmelding (Anslået)
322
Fase
- Ikke anvendelig
Deltagelseskriterier
Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.
Berettigelseskriterier
Aldre berettiget til at studere
- Barn
Tager imod sunde frivillige
Ingen
Beskrivelse
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)
Studieplan
Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.
Hvordan er undersøgelsen tilrettelagt?
Design detaljer
- Primært formål: Andet
- Tildeling: N/A
- Interventionel model: Enkelt gruppeopgave
- Maskning: Ingen (Åben etiket)
Våben og indgreb
Deltagergruppe / Arm |
Intervention / Behandling |
|---|---|
|
Eksperimentel: 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
|
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.
|
Hvad måler undersøgelsen?
Primære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
|---|---|---|
|
Change in Clinician Management Decisions Following Exposure to the AI Model
Tidsramme: 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.
|
Immediately after AI model exposure during each case review session, through study completion (average of 6 months)
|
Sekundære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
|---|---|---|
|
Agreement Between Clinician Decisions and Expert Reference Decisions Using Cohen's Kappa
Tidsramme: 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.
|
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
Tidsramme: 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.
|
Immediately after AI model exposure during each case review session, through study completion (average of 6 months)
|
Samarbejdspartnere og efterforskere
Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.
Sponsor
Datoer for undersøgelser
Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.
Studer store datoer
Studiestart (Anslået)
1. august 2026
Primær færdiggørelse (Anslået)
31. januar 2027
Studieafslutning (Anslået)
31. januar 2027
Datoer for studieregistrering
Først indsendt
16. april 2026
Først indsendt, der opfyldte QC-kriterier
6. maj 2026
Først opslået (Faktiske)
12. maj 2026
Opdateringer af undersøgelsesjournaler
Sidste opdatering sendt (Faktiske)
12. maj 2026
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
6. maj 2026
Sidst verificeret
1. maj 2026
Mere information
Begreber relateret til denne undersøgelse
Yderligere relevante MeSH-vilkår
Andre undersøgelses-id-numre
- 3474
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