Objectives
Canine leishmaniasis poses a concern for both veterinary and public health. Current data clearly indicate the absence of a gold standard method for detecting Leishmania infections in asymptomatic dogs, highlighting the need for improved diagnostic tests. Developing a user-friendly, cost-effective, and non-invasive method for detecting Leishmania infection would greatly help in limiting the spread of the disease and would make the adoption and transport of dogs between countries easier. CanLeish aims to develop an innovative, non-invasive and real-time sensing tool that detects various volatile compounds (i.e., biomarkers) emitted by dogs’ breath and hair. This sensing approach will be based on the combination of an array of non-selective gas-sensing devices that undertakes analysis of different volatile chemicals and lightweight artificial intelligence, offering an easy diagnostic tool for canine leishmaniasis diagnosis.
Our Role
NVISION leads gas sensor data analysis and develops data fusion and lightweight artificial intelligence including pattern recognition algorithms for the detection of Canine leishmaniasis.
Funding
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101007653”.
