Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex species
Abstract We present a new and innovative identification method based on deep learning of the wing interferential patterns carried by mosquitoes of the Anopheles genus to classify and assign 20 Anopheles species, including 13 malaria vectors. We provide additional evidence that this approach can iden...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-41114-4 |
| _version_ | 1827634395625816064 |
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| author | Arnaud Cannet Camille Simon-Chane Mohammad Akhoundi Aymeric Histace Olivier Romain Marc Souchaud Pierre Jacob Darian Sereno Karine Mouline Christian Barnabe Frédéric Lardeux Philippe Boussès Denis Sereno |
| author_facet | Arnaud Cannet Camille Simon-Chane Mohammad Akhoundi Aymeric Histace Olivier Romain Marc Souchaud Pierre Jacob Darian Sereno Karine Mouline Christian Barnabe Frédéric Lardeux Philippe Boussès Denis Sereno |
| author_sort | Arnaud Cannet |
| collection | DOAJ |
| description | Abstract We present a new and innovative identification method based on deep learning of the wing interferential patterns carried by mosquitoes of the Anopheles genus to classify and assign 20 Anopheles species, including 13 malaria vectors. We provide additional evidence that this approach can identify Anopheles spp. with an accuracy of up to 100% for ten out of 20 species. Although, this accuracy was moderate (> 65%) or weak (50%) for three and seven species. The accuracy of the process to discriminate cryptic or sibling species is also assessed on three species belonging to the Gambiae complex. Strikingly, An. gambiae, An. arabiensis and An. coluzzii, morphologically indistinguishable species belonging to the Gambiae complex, were distinguished with 100%, 100%, and 88% accuracy respectively. Therefore, this tool would help entomological surveys of malaria vectors and vector control implementation. In the future, we anticipate our method can be applied to other arthropod vector-borne diseases. |
| first_indexed | 2024-03-09T15:13:42Z |
| format | Article |
| id | doaj.art-6ec3a830d6494cde997f37ae21c762d4 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:13:42Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-6ec3a830d6494cde997f37ae21c762d42023-11-26T13:13:36ZengNature PortfolioScientific Reports2045-23222023-08-0113111310.1038/s41598-023-41114-4Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex speciesArnaud Cannet0Camille Simon-Chane1Mohammad Akhoundi2Aymeric Histace3Olivier Romain4Marc Souchaud5Pierre Jacob6Darian Sereno7Karine Mouline8Christian Barnabe9Frédéric Lardeux10Philippe Boussès11Denis Sereno12Direction des Affaires Sanitaires et Sociales de la Nouvelle-CalédonieETIS UMR 8051, ENSEA, CNRS, Cergy Paris UniversityParasitology-Mycology, Hopital Avicenne, AP-HPETIS UMR 8051, ENSEA, CNRS, Cergy Paris UniversityETIS UMR 8051, ENSEA, CNRS, Cergy Paris UniversityETIS UMR 8051, ENSEA, CNRS, Cergy Paris UniversityCNRS, Bordeaux INP, LaBRI, UMR 5800, Univ. BordeauxInterTryp, IRD-CIRAD, Infectiology, Medical entomology & One Health research group, Univ MontpellierMIVEGEC, CNRS, IRD, Univ MontpellierInterTryp, IRD-CIRAD, Infectiology, Medical entomology & One Health research group, Univ MontpellierMIVEGEC, CNRS, IRD, Univ MontpellierMIVEGEC, CNRS, IRD, Univ MontpellierInterTryp, IRD-CIRAD, Infectiology, Medical entomology & One Health research group, Univ MontpellierAbstract We present a new and innovative identification method based on deep learning of the wing interferential patterns carried by mosquitoes of the Anopheles genus to classify and assign 20 Anopheles species, including 13 malaria vectors. We provide additional evidence that this approach can identify Anopheles spp. with an accuracy of up to 100% for ten out of 20 species. Although, this accuracy was moderate (> 65%) or weak (50%) for three and seven species. The accuracy of the process to discriminate cryptic or sibling species is also assessed on three species belonging to the Gambiae complex. Strikingly, An. gambiae, An. arabiensis and An. coluzzii, morphologically indistinguishable species belonging to the Gambiae complex, were distinguished with 100%, 100%, and 88% accuracy respectively. Therefore, this tool would help entomological surveys of malaria vectors and vector control implementation. In the future, we anticipate our method can be applied to other arthropod vector-borne diseases.https://doi.org/10.1038/s41598-023-41114-4 |
| spellingShingle | Arnaud Cannet Camille Simon-Chane Mohammad Akhoundi Aymeric Histace Olivier Romain Marc Souchaud Pierre Jacob Darian Sereno Karine Mouline Christian Barnabe Frédéric Lardeux Philippe Boussès Denis Sereno Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex species Scientific Reports |
| title | Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex species |
| title_full | Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex species |
| title_fullStr | Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex species |
| title_full_unstemmed | Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex species |
| title_short | Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex species |
| title_sort | deep learning and wing interferential patterns identify anopheles species and discriminate amongst gambiae complex species |
| url | https://doi.org/10.1038/s41598-023-41114-4 |
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