Arteriovenous Fistula, Artificial Intelligence (AI), Machine Learning, Renal Insufficiency, Chronic
Conditions
Keywords
Preoperative Ultrasound Mapping, Radiocephalic Arteriovenous Fistula, Renal Dialysis, Artificial intelligence, Machine Learning, Chronic Kidney Disease
Brief 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.
Interventions
PROCEDUREArteriovenous fistula
Patients who underwent Radiocephalic arteriovenous fistula surgery
Sponsors
Seoul National University Hospital
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
* patients who underwent RCAVF due to advanced chronic kidney disease from 2018 to 2022
Exclusion criteria
* Patients who did not have follow-up data available
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Maturation of the fistula | 90 days | Fistula maturation was defined as an arteriovenous fistula that matures and is usable for dialysis with two-needle cannulation for hemodialysis for at least 90 days without the need for endovascular or surgical interventions. |
Countries
South Korea
Outcome results
None listed