Integrating artificial intelligence and wing geometric morphometry to automate mosquito classification

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dc.contributorSistema FMUSP-HC: Faculdade de Medicina da Universidade de São Paulo (FMUSP) e Hospital das Clínicas da FMUSP
dc.contributor.authorLIMA, Vinicio Rodrigues de
dc.contributor.authorMORAIS, Mauro Cesar Cafundo de
dc.contributor.authorKIRCHGATTER, Karin
dc.date.accessioned2024-02-15T14:54:10Z
dc.date.available2024-02-15T14:54:10Z
dc.date.issued2024
dc.description.abstractMosquitoes (Diptera: Culicidae) comprise over 3500 global species, primarily in tropical regions, where the females act as disease vectors. Thus, identifying medically significant species is vital. In this context, Wing Geometric Morphometry (WGM) emerges as a precise and accessible method, excelling in species differentiation through mathematical approaches. Computational technologies and Artificial Intelligence (AI) promise to overcome WGM challenges, supporting mosquito identification. AI explores computers' thinking capacity, originating in the 1950s. Machine Learning (ML) arose in the 1980s as a subfield of AI, and deep Learning (DL) characterizes ML's subcategory, featuring hierarchical data processing layers. DL relies on data volume and layer adjustments. Over the past decade, AI demonstrated potential in mosquito identification. Various studies employed optical sensors, and Convolutional Neural Networks (CNNs) for mosquito identification, achieving average accuracy rates between 84 % and 93 %. Furthermore, larval Aedes identification reached accuracy rates of 92 % to 94 % using CNNs. DL models such as ResNet50 and VGG16 achieved up to 95 % accuracy in mosquito identification. Applying CNNs to georeference mosquito photos showed promising results. AI algorithms automated landmark detection in various insects' wings with repeatability rates exceeding 90 %. Companies have developed wing landmark detection algorithms, marking significant advancements in the field. In this review, we discuss how AI and WGM are being combined to identify mosquito species, offering benefits in monitoring and controlling mosquito populations.eng
dc.description.indexMEDLINE
dc.description.indexPubMed
dc.description.indexWoS
dc.description.indexScopus
dc.description.sponsorshipCoordenacao de Aperfeicoamento de Pessoal de Nivel Superior-CAPES [2020/12017-9, 88887.626329/2021-00, 309396/2021-2]
dc.description.sponsorshipFAPESP PosDoctoral scholarship [2020/12017-9]
dc.description.sponsorshipCNPq research fellow [309396/2021-2]
dc.identifier.citationACTA TROPICA, v.249, article ID 107089, 7p, 2024
dc.identifier.doi10.1016/j.actatropica.2023.107089
dc.identifier.eissn1873-6254
dc.identifier.issn0001-706X
dc.identifier.urihttps://observatorio.fm.usp.br/handle/OPI/58108
dc.language.isoeng
dc.publisherELSEVIEReng
dc.relation.ispartofActa Tropica
dc.rightsrestrictedAccesseng
dc.rights.holderCopyright ELSEVIEReng
dc.subjectMosquito-borne diseaseseng
dc.subjectSpecies identificationeng
dc.subjectIntegrative approacheng
dc.subject.otherfluctuating asymmetryeng
dc.subject.otherlife-historyeng
dc.subject.othertemperatureeng
dc.subject.otherdipteraeng
dc.subject.othershapeeng
dc.subject.otherdnaeng
dc.subject.wosParasitologyeng
dc.subject.wosTropical Medicineeng
dc.titleIntegrating artificial intelligence and wing geometric morphometry to automate mosquito classificationeng
dc.typearticleeng
dc.type.categoryoriginal articleeng
dc.type.versionpublishedVersioneng
dspace.entity.typePublication
hcfmusp.author.externalMORAIS, Mauro Cesar Cafundo de:Inst Israelita Ensino & Pesquisa Albert Einstein I, Soc Beneficente Israelita Brasileira Albert Einste, Sao Paulo, SP, Brazil; Inst Pasteur Sao Paulo, Computat Syst Biol Lab CSBL, BR-05508020 Sao Paulo, SP, Brazil
hcfmusp.citation.scopus1
hcfmusp.contributor.author-fmusphcVINICIO RODRIGUES DE LIMA
hcfmusp.contributor.author-fmusphcKARIN KIRCHGATTER
hcfmusp.description.articlenumber107089
hcfmusp.description.volume249
hcfmusp.origemWOS
hcfmusp.origem.pubmed38043672
hcfmusp.origem.scopus2-s2.0-85178614945
hcfmusp.origem.wosWOS:001135057500001
hcfmusp.publisher.cityAMSTERDAMeng
hcfmusp.publisher.countryNETHERLANDSeng
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