An artificial intelligence model to identify snakes from across the world: Opportunities and challenges for global health and herpetology
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F22%3A43966118" target="_blank" >RIV/49777513:23520/22:43966118 - isvavai.cz</a>
Result on the web
<a href="https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0010647" target="_blank" >https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0010647</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1371/journal.pntd.0010647" target="_blank" >10.1371/journal.pntd.0010647</a>
Alternative languages
Result language
angličtina
Original language name
An artificial intelligence model to identify snakes from across the world: Opportunities and challenges for global health and herpetology
Original language description
BackgroundSnakebite 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.MethodologyWe 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 findingsThe 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.ConclusionsTo 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
<a href="/en/project/SS05010008" target="_blank" >SS05010008: Detection, identification and monitoring of animals by advanced computer vision methods.</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
PLoS Neglected Tropical Diseases
ISSN
1935-2735
e-ISSN
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Volume of the periodical
16
Issue of the periodical within the volume
8
Country of publishing house
US - UNITED STATES
Number of pages
19
Pages from-to
nestrankovano
UT code for WoS article
000922516300025
EID of the result in the Scopus database
2-s2.0-85137125459