Development and validation of the Alcoholic Beverage Identification Deep Learning Algorithm version 2 for quantifying alcohol exposure in electronic images.

Journal: Alcoholism, clinical and experimental research
PMID: 36242596

Abstract

BACKGROUND: Seeing alcohol in media has been demonstrated to increase alcohol craving, impulsive decision-making, and hazardous drinking. Due to the exponential growth of (social) media use it is important to develop algorithms to quantify alcohol exposure efficiently in electronic images. In this article, we describe the development of an improved version of the Alcoholic Beverage Identification Deep Learning Algorithm (ABIDLA), called ABIDLA2.

Authors

  • Abraham Albert Bonela
    Centre for Alcohol Policy Research, La Trobe University, Melbourne, Australia; Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia.
  • Zhen He
  • Thomas Norman
    Centre for Alcohol Policy Research, La Trobe University, Melbourne, Victoria, Australia.
  • Emmanuel Kuntsche
    Centre for Alcohol Policy Research, La Trobe University, Melbourne, Australia. Electronic address: e.kuntsche@latrobe.edu.au.

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