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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.
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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.
1,000 participants in 1 patient group
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Central trial contact
Jean-François TIMSIT, Pr
Data sourced from clinicaltrials.gov
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