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dc.contributor.authorBolon, Isabelle
dc.contributor.authorPicek, Lukáš
dc.contributor.authorDurso, Andrew
dc.contributor.authorAlcoba, Gabriel
dc.contributor.authorChappuis, François
dc.contributor.authorCastañeda, Rafael Ruiz de
dc.date.accessioned2023-03-06T11:00:26Z-
dc.date.available2023-03-06T11:00:26Z-
dc.date.issued2022
dc.identifier.citationBOLON, I. PICEK, L. DURSO, A. ALCOBA, G. CHAPPUIS, F. CASTAÑEDA, RRD. An artificial intelligence model to identify snakes from across the world: Opportunities and challenges for global health and herpetology. PLoS Neglected Tropical Diseases, 2022, roč. 16, č. 8, s. nestránkováno. ISSN: 1935-2735cs
dc.identifier.issn1935-2735
dc.identifier.uri2-s2.0-85137125459
dc.identifier.urihttp://hdl.handle.net/11025/51654
dc.format19 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherPLOSen
dc.relation.ispartofseriesPLoS Neglected Tropical Diseasesen
dc.rights© PLOSen
dc.titleAn artificial intelligence model to identify snakes from across the world: Opportunities and challenges for global health and herpetologyen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedBackground Snakebite envenoming is a neglected tropical disease that kills an estimated 81,000 to 138,000 people and disables another 400,000 globally every year. The World Health Organization aims to halve this burden by 2030. To achieve this ambitious goal, we need to close the data gap in snake ecology and snakebite epidemiology and give healthcare providers up-to-date knowledge and access to better diagnostic tools. An essential first step is to improve the capacity to identify biting snakes taxonomically. The existence of AI-based identification tools for other animals offers an innovative opportunity to apply machine learning to snake identification and snakebite envenoming, a life-threatening situation. Methodology We developed an AI model based on Vision Transformer, a recent neural network architecture, and a comprehensive snake photo dataset of 386,006 training photos covering 198 venomous and 574 non-venomous snake species from 188 countries. We gathered photos from online biodiversity platforms (iNaturalist and HerpMapper) and a photo-sharing site (Flickr). Principal findings The model macro-averaged F1 score, which reflects the species-wise performance as averaging performance for each species, is 92.2%. The accuracy on a species and genus level is 96.0% and 99.0%, respectively. The average accuracy per country is 94.2%. The model accurately classifies selected venomous and non-venomous lookalike species from Southeast Asia and sub-Saharan Africa. Conclusions To our knowledge, this model’s taxonomic and geographic coverage and performance are unprecedented. This model could provide high-speed and low-cost snake identification to support snakebite victims and healthcare providers in low-resource settings, as well as zoologists, conservationists, and nature lovers from across the world.en
dc.subject.translatedSnakesen
dc.subject.translatedSnakebiteen
dc.subject.translatedArtificial intelligenceen
dc.subject.translatedGeographyen
dc.subject.translatedHealth care providersen
dc.subject.translatedAsiaen
dc.subject.translatedAfricaen
dc.subject.translatedComputer visionen
dc.identifier.doi10.1371/journal.pntd.0010647
dc.type.statusPeer-revieweden
dc.identifier.document-number922516300025
dc.identifier.obd43937118
dc.project.IDSGS-2022-017/Inteligentní metody strojového vnímání a porozumění 5cs
dc.project.IDSS05010008/Detekce, identifikace a monitoring živočichů pokročilými metodami počítačového viděnícs
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