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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • 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

  • 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