NCT05440396

Identifying Local Signs at the Catheter Insertion Site With Artificial Intelligence

Development of an Artificial Intelligence Model of Image Recognition Through Images of Intravascular Catheters From Inpatients and Outpatients to Identify the Presence of Local Signs Associated With Infection

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 1,000 (estimated)
• Sponsor: Outcome Rea · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

Deepcath is the first step to the introduction of artificial intelligence in catheter care. A better use of visualisation of catheter exit site should be used not only by the HCWs but also by the patients and their family. A deep learning system able to detect visual abnormalities of the catheter exit site will be an helpful tools to develop a continuous follow-up of intravascular catheters.

Conditions

• Catheter Infection

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Photographs collection phase	Three medical experts have been selected to review the photo collected. Each expert medical assesses the presence of local signs of infection on the photographs by annotating them directly via a dedicated software. They will annotate local signs: redness, perfusion extravasation, necrosis, hematoma, edema, non-purulent discharge, and purulent discharge. A convolutional neural network model will determine the probability of local sign presence. Each picture will be annotated to determine the main characteristics of the catheter. A dataset preparation with photo cropping will be performed for modelling.

Timeline

• Start date: 2022-09-01
• Primary completion: 2023-12-31
• Completion: 2023-12-31
• First posted: 2022-06-30
• Last updated: 2022-10-25

Locations

4 sites across 1 country: France

Regulatory

• FDA-regulated device study