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Artificial Intelligence-based Prediction of Radio-cephalic Arteriovenous Fistula Maturation Using Preoperative Duplex Examination

NCT06600750 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 494

Last updated 2024-09-26

No results posted yet for this study

Summary

The goal of this observational study is to assess the efficacy of AI-driven models in analyzing comprehensive ultrasonographic variables across multiple forearm locations to predict successful AVF maturation. The main question it aims to answer is:

Can AI-driven models analyzing comprehensive ultrasonographic variables accurately predict the successful maturation of arteriovenous fistulas (AVFs)?

Participants who underwent radiocephalic arteriovenous fistula (AVF) creation had their preoperative ultrasonographic data analyzed using AI-driven models to predict successful AVF maturation over a four-year retrospective period.

Conditions

  • Renal Insufficiency, Chronic
  • Arteriovenous Fistula
  • Artificial Intelligence (AI)
  • Machine Learning

Interventions

PROCEDURE

Arteriovenous fistula

Patients who underwent Radiocephalic arteriovenous fistula surgery

Sponsors & Collaborators

  • Seoul National University Hospital

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2018-01-01
Primary Completion
2022-12-31
Completion
2023-12-31

Countries

  • South Korea

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06600750 on ClinicalTrials.gov