Identifying Local Signs at the Catheter Insertion Site With Artificial Intelligence

Outcome Rea1,000 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

1,000

Conditions

Catheter Infection

Interventions

Photographs collection phaseDIAGNOSTIC_TEST

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.

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

Inclusion Criteria: Patients over 18 years of age Patients with one or more implanted central venous, midline, piCCline, arterial, or peripheral catheters. Patient and/or trusted person and/or family who have verbally stated their non-objection to the study Patient affiliated or beneficiary of a social security plan Exclusion Criteria: Patients presenting a peripheral identification sign close to the catheter insertion point cannot be masked when the photograph is taken. Thus, jewelry, clothing, tattoos, scars, and birthmarks are identifying features. Patients whose catheter insertion point is not visible.

Locations (4)

CHU Grenoble Alpes

Grenoble, Isère, France

RECRUITING

CHU Clermont-Ferrand

Clermont-Ferrand, Puy-de-Dôme, France

RECRUITING

Réseau RéPIAS SPIADI

Tours, France

RECRUITING

Outcomes

Primary Outcomes

The number of correct predictions for redness divided by the total number of predictions

Overall classification accuracy of the learning model compared to the assessment of three independent medical experts on the detection of the presence of redness greater than or equal to 5 mm at the catheter insertion site.

Time frame: through study completion, an average of 1 year

Secondary Outcomes

The ratio of true positives and total positives predicted:

The precision metric focuses on Type-I errors(FP). A Type-I error occurs when we reject a true null Hypothesis.

Time frame: through study completion, an average of 1 year

The number of correct predictions for indurated venous cord divided by the total number of predictions

Evaluate, on the basis of images of peripheral catheter insertion sites, the reliability of the learning model on the assessment of the presence of indurated venous cord.

Time frame: through study completion, an average of 1 year

The link between presence of local signs and infection

Measure the correlation between the appearance of the catheter puncture site and the presence of signs consistent with local and systemic infection.

Time frame: through study completion, an average of 1 year

Central Contacts

Jean-François TIMSIT, Pr

CONTACT

+33140257703 jean-francois.timsit@aphp.fr

Data from ClinicalTrials.gov

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