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Development and validation of the Alcoholic Beverage Identification Deep Learning Algorithm Version 2 (ABIDLA2) for quantifying alcohol exposure in electronic images

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Version 2 2022-10-28, 01:32
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journal contribution
posted on 2022-10-28, 01:32 authored by Abraham Albert BonelaAbraham Albert Bonela, Zhen HeZhen He, Thomas NormanThomas Norman, Emmanuel KuntscheEmmanuel Kuntsche

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. Methods: ABIDLA2 was trained on 191,286 images downloaded from Google Image Search results (based on search terms) and Bing Image Search results. In Task-1, ABIDLA2 identified images as containing one of eight beverage categories (beer/cider cup, beer/cider bottle, beer/cider can, wine, champagne, cocktails, whiskey/cognac/ brandy, other images). In Task-2, ABIDLA2 made a binary classification between images containing an “alcoholic beverage” or “other”. An ablation study was performed to determine which techniques improved algorithm performance. Results: ABIDLA2 was most accurate in identifying Whiskey/Cognac/Brandy (88.1%) followed by Beer/Cider Can (80.5%), Beer/Cider Bottle (78.3%), and Wine (77.8%). Its overall accuracy was 77.0% (Task-1) and 87.7% (Task-2). Even the identification of the least accurate beverage category (Champagne, 64.5%) was more than five times higher than random chance (12.5% = 1/8 categories). The implementation of balanced data sampler to address class skewness and the use of self-training to make use of a large, secondary, weakly labeled dataset particularly improved overall algorithm performance. Conclusion: With extended capabilities and a higher accuracy, ABIDLA2 outperforms its predecessor and enables the screening of any kind of electronic media rapidly to estimate the quantity of alcohol exposure. Quantifying alcohol exposure automatically through algorithms like ABIDLA2 is important because viewing images of alcoholic beverages in media tends to increase alcohol consumption and related harms. 

History

Publication Date

2022-10-15

Journal

Alcoholism: Clinical and Experimental Research

Volume

46

Issue

10

Pagination

9p. (p.1837-1845)

Publisher

Wiley

ISSN

0145-6008

Rights Statement

© 2022 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.