Artificial intelligence for the detection of local signs of infections around catheters.
Presentation of the project
DeepCath aims to develop the first therapeutic decision support device for the risk of infection around a catheter coupling an intuitive interface and an image recognition algorithm.
In the current context, in Europe, the incidence density of infections linked to central venous catheters (CVC) varies from 1 to 3.1 per 1000 patients per day. In the United States, the National Nosocomial Infections Surveillance System (NNIS) estimates that there are 80,000 CVC-related bloodstream infections per day. These infections are associated with increased mortality, increased lengths of stay and increased hospitalization costs.
The implementation of an automatic system facilitating the monitoring of patients with a catheter, regardless of their place of care (hospitals, clinics and home) would make it possible to detect the risks of infections very early, reduce complications and would thus participate in all the monitoring strategies, beneficial for the patient.
DeepCath aims to become a simple digital tool, at the service of healthcare professionals and patients in hospitals and in the city, to diagnose local signs compatible with the diagnosis of catheter-related infections in patients. Its integration into current clinical practice will respond to the problems encountered related to the difficulty of early diagnosis, and will thus contribute to improving the management of patients suspected of having an infectious complication.