NCT06600750

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

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 494 (actual)
• Sponsor: Seoul National University Hospital · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: —

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

Type	Name	Description
PROCEDURE	Arteriovenous fistula	Patients who underwent Radiocephalic arteriovenous fistula surgery

Timeline

• Start date: 2018-01-01
• Primary completion: 2022-12-31
• Completion: 2023-12-31
• First posted: 2024-09-19
• Last updated: 2024-09-26

Locations

1 site across 1 country: South Korea