@article{fdi:010089714,
  title = {{S}pecies identification of phlebotomine sandflies using deep learning and wing interferential pattern ({WIP})},
  author = {{C}annet, {A}. and {S}imon-{C}hane, {C}. and {H}istace, {A}. and {A}khoundi, {M}. and {R}omain, {O}. and {S}ouchaud, {M}. and {J}acob, {P}. and {S}ereno, {D}. and {V}olf, {P}. and {D}vorak, {V}. and {S}ereno, {D}enis},
  editor = {},
  language = {{ENG}},
  abstract = {{S}andflies ({D}iptera; {P}sychodidae) are medical and veterinary vectors that transmit diverse parasitic, viral, and bacterial pathogens. {T}heir identification has always been challenging, particularly at the specific and sub-specific levels, because it relies on examining minute and mostly internal structures. {H}ere, to circumvent such limitations, we have evaluated the accuracy and reliability of {W}ing {I}nterferential {P}atterns ({WIP}s) generated on the surface of sandfly wings in conjunction with deep learning ({DL}) procedures to assign specimens at various taxonomic levels. {O}ur 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. {T}his 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.},
  keywords = {},
  booktitle = {},
  journal = {{S}cientific {R}eports - {N}ature},
  volume = {13},
  numero = {1},
  pages = {21389 [9 ]},
  ISSN = {2045-2322},
  year = {2023},
  DOI = {10.1038/s41598-023-48685-2},
  URL = {https://www.documentation.ird.fr/hor/fdi:010089714},
}