Literature DB >> 34774917

Web-based snake identification service: A successful model of snake identification in Sri Lanka.

Kalana Maduwage1, Parackrama Karunathilake2, José María Gutiérrez3.   

Abstract

Snakes are reptiles of great biomedical significance. The accurate identification of snakes is particularly important for healthcare workers to diagnose and treat victims of snakebite envenoming. Further, snake identification is vital for the general population, especially to those who live in areas of high snakebite incidence. Owing to the great diversity of snakes and the superficial similarities between some species, the correct identification of these reptiles is often difficult. Therefore, identification of snake species is challenging for healthcare workers, biologists, naturalists, and the general population. To overcome this challenge, we developed a web-based snake identification service (www.snakesidentification.org) in Sri Lanka, which provides rapid and accurate identification by experienced herpetologists. This service received 486 identification requests over a period of 40 months. The majority of requests were from Colombo District [140 (28.8%)], though only 63 (13.0%) of these were identified as medically important snakes. The majority [389 (80.0%)] of the requests related either to feebly venomous colubrid snakes or non-venomous species. The sample included 30 (of 107) snake species in the island, including 8 endemic species. There were 315 (64.8%) requests relating to live snakes. In the majority of cases (285, 90.4%), the snake was released to the closest available habitat after being identified. The median time taken to respond to requests was 70 min (interquartile range 23-299 min). The majority of persons making requests (283, 58.2%) were unable to identify the snakes. For those who attempted identification the snakes, correct identification was made by only 59 (12.1%), whereas 144 (29.6%) identified the snake incorrectly. This web-based snake identification service provides an example of a successful and useful model of rapid snake identification. Similar models could be implemented in other regions and countries to provide accurate information on snake identification both to the healthcare workers and the general public.
Copyright © 2021 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Snake conservation; Snake identification; Sri Lanka; Venomous snakes

Mesh:

Year:  2021        PMID: 34774917     DOI: 10.1016/j.toxicon.2021.11.007

Source DB:  PubMed          Journal:  Toxicon        ISSN: 0041-0101            Impact factor:   3.033


  1 in total

1.  An artificial intelligence model to identify snakes from across the world: Opportunities and challenges for global health and herpetology.

Authors:  Isabelle Bolon; Lukáš Picek; Andrew M Durso; Gabriel Alcoba; François Chappuis; Rafael Ruiz de Castañeda
Journal:  PLoS Negl Trop Dis       Date:  2022-08-15
  1 in total

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