ArticleScientific reports2023
Species identification of phlebotomine sandflies using deep learning and wing interferential pattern (WIP).
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 11 citations in OpenAlex.
- A structured literature review of computer vision methods for insect identification.Journal of insect science (Online) · 2026Review
- Leishmaniasis in Morocco: Epidemiology, Transmission Dynamics, and the Potential of Artificial Intelligence for Disease Management.Journal of parasitology research · 2026Review
- Leishmaniasis.Nature reviews. Disease primers · 2025Review
- Potentials and limitations in the application of Convolutional Neural Networks for mosquito species identification using wing images.PLoS computational biology · 2025Article
- Application of wings interferential patterns (WIPs) and deep learning (DL) to classify some Culex. spp (Culicidae) of medical or veterinary importance.Scientific reports · 2025Article
- Using deep learning artificial intelligence for sex identification and taxonomy of sand fly species.PloS one · 2025Article
- A Century of Epidemiological Advances in Cutaneous and Visceral Leishmaniasis in Algeria.Journal of parasitology research · 2025Review
- Identifying disease vector images in the Americas in the age of artificial intelligence.Revista da Sociedade Brasileira de Medicina Tropical · 2025Review
- Efficient Convolutional Neural Network Model for the Taxonomy and Sex Identification of Three Phlebotomine Sandfly Species (Diptera, Psychodidae, and Phlebotominae).Animals : an open access journal from MDPI · 2024Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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