Identifying Local Signs at the Catheter Insertion Site With Artificial Intelligence

Study Overview
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Brief Summary
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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.

Official Title
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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

Conditions
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Catheter Infection

Intervention / Treatment
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* Diagnostic Test: Photographs collection phase


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Patient<br>Patients will be recruited independently in each of the centers participating in the study, according to the specific organization of each service (hospital and home). Patients will be selected based on the study inclusion/non-inclusion criteria. The included patients will receive oral information about the study. The informed consent will be obtained before any investigation. Each photograph will focus on the intravascular catheter. The investigators will respect the main non-inclusion criterion: not to show any peripheral identification sign close to the insertion point of the catheter that cannot be masked when the photograph is taken. In this context, jewellery, clothing, tattoos, scars, and birthmarks represent identifying features. Individual access accounts with secure, randomly generated passwords will be provided to investigators. The photographs will be anonymous, and any identification of the person concerned will be impossible. | Diagnostic Test: Photographs collection phase<br>* 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.<br>* Other names: Development/Validation of artificial intelligence algorithm;|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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. | through study completion, an average of 1 year | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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. | 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. | 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. | through study completion, an average of 1 year | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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catheter infection, deep learning, local signs