Title:	An artificial intelligence model to identify snakes from across the world: Opportunities and challenges for global health and herpetology
Authors:	Bolon, Isabelle Picek, Lukáš Durso, Andrew Alcoba, Gabriel Chappuis, François Castañeda, Rafael Ruiz de
Citation:	BOLON, 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: 1935-2735
Issue Date:	2022
Publisher:	PLOS
Document type:	článek article
URI:	2-s2.0-85137125459 http://hdl.handle.net/11025/51654
ISSN:	1935-2735
Keywords in different language:	Snakes;Snakebite;Artificial intelligence;Geography;Health care providers;Asia;Africa;Computer vision
Abstract in different language:	Background 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.
Rights:	© PLOS
Appears in Collections:	Články / Articles (NTIS) Články / Articles (KKY) OBD

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