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.

Cergy Paris University Cergy France

Direction des Affaires Sanitaires et Sociales de la Nouvelle Calédonie Nouméa France

ETIS UMR 8051 Cergy Paris University ENSEA CNRS 95000 Cergy France

InterTryp Univ Montpellier IRD CIRAD Infectiology Entomology and One Health Research Group Montpellier France

Laboratory of Vector Biology Department of Parasitology Faculty of Science Charles University Prague Czech Republic

MIVEGEC Univ Montpellier CNRS IRD Montpellier France

Parasitology Mycology Hopital Avicenne AP HP Bobigny France

Univ Bordeaux CNRS Bordeaux INP LaBRI UMR 5800 33400 Talence France

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