ArticleAlcoholism, clinical and experimental research2022
Development and validation of the Alcoholic Beverage Identification Deep Learning Algorithm version 2 for quantifying alcohol exposure in electronic images.
Article in Alcoholism, clinical and experimental research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Development of the lyrics-based deep learning algorithm for identifying alcohol-related words (LYDIA).Alcohol and alcoholism (Oxford, Oxfordshire) · 2024Article
- The promise of zero-shot learning for alcohol image detection: comparison with a task-specific deep learning algorithm.Scientific reports · 2023Article
- Development and validation of the Alcoholic Beverage Identification Deep Learning Algorithm version 2 for quantifying alcohol exposure in electronic images.Alcoholism, clinical and experimental research · 2022Article
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4 authors.
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Abstract
backgroundSeeing 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.
methodsABIDLA2 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.
resultsABIDLA2 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.
conclusionWith 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.
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