Denne siden ble automatisk oversatt og nøyaktigheten av oversettelsen er ikke garantert. Vennligst referer til engelsk versjon for en kildetekst.

Integrating an AI-Driven Hydronephrosis Decision-Making Tool

6. mai 2026 oppdatert av: 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.

Studieoversikt

Status

Har ikke rekruttert ennå

Forhold

Intervensjon / Behandling

Studietype

Intervensjonell

Registrering (Antatt)

322

Fase

  • Ikke aktuelt

Deltakelseskriterier

Forskere ser etter personer som passer til en bestemt beskrivelse, kalt kvalifikasjonskriterier. Noen eksempler på disse kriteriene er en persons generelle helsetilstand eller tidligere behandlinger.

Kvalifikasjonskriterier

Alder som er kvalifisert for studier

  • Barn

Tar imot friske frivillige

Nei

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

Denne delen gir detaljer om studieplanen, inkludert hvordan studien er utformet og hva studien måler.

Hvordan er studiet utformet?

Designdetaljer

  • Primært formål: Annen
  • Tildeling: N/A
  • Intervensjonsmodell: Enkeltgruppeoppdrag
  • Masking: Ingen (Open Label)

Våpen og intervensjoner

Deltakergruppe / Arm
Intervensjon / Behandling
Eksperimentell: 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
Annen: 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.

Hva måler studien?

Primære resultatmål

Resultatmål
Tiltaksbeskrivelse
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
Tiltaksbeskrivelse
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)

Samarbeidspartnere og etterforskere

Det er her du vil finne personer og organisasjoner som er involvert i denne studien.

Sponsor

The Hospital for Sick Children

Studierekorddatoer

Disse datoene sporer fremdriften for innsending av studieposter og sammendragsresultater til ClinicalTrials.gov. Studieposter og rapporterte resultater gjennomgås av National Library of Medicine (NLM) for å sikre at de oppfyller spesifikke kvalitetskontrollstandarder før de legges ut på det offentlige nettstedet.

Studer hoveddatoer

Studiestart (Antatt)

1. august 2026

Primær fullføring (Antatt)

31. januar 2027

Studiet fullført (Antatt)

31. januar 2027

Datoer for studieregistrering

Først innsendt

16. april 2026

Først innsendt som oppfylte QC-kriteriene

6. mai 2026

Først lagt ut (Faktiske)

12. mai 2026

Oppdateringer av studieposter

Sist oppdatering lagt ut (Faktiske)

12. mai 2026

Siste oppdatering sendt inn som oppfylte QC-kriteriene

6. mai 2026

Sist bekreftet

1. mai 2026

Mer informasjon

Begreper knyttet til denne studien

Ytterligere relevante MeSH-vilkår

Andre studie-ID-numre

  • 3474

Legemiddel- og utstyrsinformasjon, studiedokumenter

Studerer et amerikansk FDA-regulert medikamentprodukt

Nei

Studerer et amerikansk FDA-regulert enhetsprodukt

Nei

Denne informasjonen ble hentet direkte fra nettstedet clinicaltrials.gov uten noen endringer. Hvis du har noen forespørsler om å endre, fjerne eller oppdatere studiedetaljene dine, vennligst kontakt register@clinicaltrials.gov. Så snart en endring er implementert på clinicaltrials.gov, vil denne også bli oppdatert automatisk på nettstedet vårt. .

Abonnere