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

ABSTRACT:

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,

SUBMITTER: Bolon I 

PROVIDER: S-EPMC9426939 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature