Overall Objectives
Scientific Foundations
Application Domains
New Results
Contracts and Grants with Industry
Other Grants and Activities

Section: Contracts and Grants with Industry


Participants : François Charpillet, Cédric Rose.

Kidney graft

We continue to develop telemedicine solutions for End Stage Renal Chronic patients. Transplantelic is a telemedicine project which aims at improving the follow up for patients with kidney graft. A new system is being developed and a clinical trial in a three year project is scheduled. Transplantelic just started in the beginning of 2006 and it is funded both by Region Lorraine and ARH. We have developed a new expert system using Bayabox (see Sec.  5.1.1 ) for the surveillance of patients with graft kidney.

Vascular access control

In kidney failure treatment by hemodialysis, the blood is purified outside the body. The vascular access that allows to perform the extra-body blood circulation is usually a vein of the arm that has been enlarged by a surgical creation of a fistula that connects the vein to an artery. Since the number of veins in the arm is limited, the prevention of complications such as stenosis or thrombosis of the vascular access is a key issue in hemodialysis treatment.

One of the parameters recorded by some dialysis machines is the ionic dialysance which is based on a conductivity measure. The ionic dialysance is an indicator of the flow of filtered wastes. Previous work have shown that the follow-up of the dialysance and blood pressures can help to detect early a potential risk with the vascular access.

Gambro is an international company that develops dialysis machines. The Diatelic company was founded in 2002 as an Inria start-up for developping telemedicine solutions for kidney failure treatments. Gambro, Diatelic and Maia have collaborated through a CIFRE convention to develop an automated classifier of the dialysis sessions for estimating the risk related to the vascular access.

The main difficulty of the analysis is the large variability of the measures and the need to detect tendencies. The system developed is based on a supervised learning of a dynamic signal classifier formalized as a dynamic bayesian network. A preprocessing of the signals allows for each patient to be his own reference. Two separate labelled datasets were provided by a medical expert from Gambro for developping and testing of the system.

The evaluation of the results was done performing a double-blind analysis of real data which resulted in a 85% agreement rate. The system was validated by the medical expert who estimated that the concordance of the automated classification with his own classification was good enough for the system to be included in a gambro software that is planned for 2010.


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