Cannet A., Simon-Chane C., Histace A., Akhoundi M., Romain O., Souchaud M., Jacob P., Sereno D., Volf P., Dvorak V., Sereno Denis. (2023). Species identification of phlebotomine sandflies using deep learning and wing interferential pattern (WIP). Scientific Reports - Nature, 13 (1), 21389 [9 p.]. ISSN 2045-2322.
Titre du document
Species identification of phlebotomine sandflies using deep learning and wing interferential pattern (WIP)
Année de publication
2023
Auteurs
Cannet A., Simon-Chane C., Histace A., Akhoundi M., Romain O., Souchaud M., Jacob P., Sereno D., Volf P., Dvorak V., Sereno Denis
Source
Scientific Reports - Nature, 2023,
13 (1), 21389 [9 p.] ISSN 2045-2322
Sandflies (Diptera; Psychodidae) are medical and veterinary vectors that transmit diverse parasitic, viral, and bacterial pathogens. Their identification has always been challenging, particularly at the specific and sub-specific levels, because it relies on examining minute and mostly internal structures. Here, to circumvent such limitations, we have evaluated the accuracy and reliability of Wing Interferential Patterns (WIPs) generated on the surface of sandfly wings in conjunction with deep learning (DL) procedures to assign specimens at various taxonomic levels. Our dataset proves that the method can accurately identify sandflies over other dipteran insects at the family, genus, subgenus, and species level with an accuracy higher than 77.0%, regardless of the taxonomic level challenged. This approach does not require inspection of internal organs to address identification, does not rely on identification keys, and can be implemented under field or near-field conditions, showing promise for sandfly pro-active and passive entomological surveys in an era of scarcity in medical entomologists.
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020]
;
Entomologie médicale / Parasitologie / Virologie [052]
Localisation
Fonds IRD [F B010089714]
Identifiant IRD
fdi:010089714
