Cannet A., Simon-Chane C., Akhoundi M., Histace A., Romain O., Souchaud M., Jacob P., Delaunay P., Sereno D., Boussès Philippe, Grébaut Pascal, Geiger Anne, de Beer C., Kaba D., Sereno Denis. (2022). Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification. Scientific Reports - Nature, 12 (1), p. 20086 [15 p.]. ISSN 2045-2322.
Titre du document
Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification
Année de publication
2022
Auteurs
Cannet A., Simon-Chane C., Akhoundi M., Histace A., Romain O., Souchaud M., Jacob P., Delaunay P., Sereno D., Boussès Philippe, Grébaut Pascal, Geiger Anne, de Beer C., Kaba D., Sereno Denis
Source
Scientific Reports - Nature, 2022,
12 (1), p. 20086 [15 p.] ISSN 2045-2322
A simple method for accurately identifying Glossina spp in the field is a challenge to sustain the future elimination of Human African Trypanosomiasis (HAT) as a public health scourge, as well as for the sustainable management of African Animal Trypanosomiasis (AAT). Current methods for Glossina species identification heavily rely on a few well-trained experts. Methodologies that rely on molecular methodologies like DNA barcoding or mass spectrometry protein profiling (MALDI TOFF) haven't been thoroughly investigated for Glossina sp. Nevertheless, because they are destructive, costly, time-consuming, and expensive in infrastructure and materials, they might not be well adapted for the survey of arthropod vectors involved in the transmission of pathogens responsible for Neglected Tropical Diseases, like HAT. This study demonstrates a new type of methodology to classify Glossina species. In conjunction with a deep learning architecture, a database of Wing Interference Patterns (WIPs) representative of the Glossina species involved in the transmission of HAT and AAT was used. This database has 1766 pictures representing 23 Glossina species. This cost-effective methodology, which requires mounting wings on slides and using a commercially available microscope, demonstrates that WIPs are an excellent medium to automatically recognize Glossina species with very high accuracy.
Plan de classement
Entomologie médicale / Parasitologie / Virologie [052]
;
Informatique [122]
Localisation
Fonds IRD [F B010086665]
Identifiant IRD
fdi:010086665
