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AI Prediction of PIVC-Related Phlebitis
Sponsor: Kutahya Health Sciences University
Summary
This study aims to develop and validate multimodal AI-based prediction models for peripheral intravenous catheter (PIVC)-related phlebitis by integrating demographic, clinical, catheter-related, and phlebitis image data. The models will be evaluated for their ability to accurately predict phlebitis risk, with the goal of facilitating early identification, timely intervention, and improved patient safety.
Official title: Predicting Peripheral Intravenous Catheter-Related Phlebitis With AI-Based Models.
Key Details
Gender
All
Age Range
18 Years - 85 Years
Study Type
OBSERVATIONAL
Enrollment
461
Start Date
2025-07-15
Completion Date
2026-09-13
Last Updated
2026-10-22
Healthy Volunteers
No
Interventions
Artificial Intelligence-Based Phlebitis Detection Model
An artificial intelligence-based model will be developed and evaluated to detect or predict phlebitis using clinical images. The model's performance will be assessed using image-based data.
Locations (2)
Kutahya Health Sciences University
Kütahya, Kütahya, Turkey (Türkiye)
Kutahya Health Sciences University
Merkez, Kütahya, Turkey (Türkiye)