Identifying Smoking Environments From Images of Daily Life With Deep Learning

JAMA Network Open
Matthew M EngelhardF Joseph McClernon

Abstract

Environments associated with smoking increase a smoker's craving to smoke and may provoke lapses during a quit attempt. Identifying smoking risk environments from images of a smoker's daily life provides a basis for environment-based interventions. To apply a deep learning approach to the clinically relevant identification of smoking environments among settings that smokers encounter in daily life. In this cross-sectional study, 4902 images of smoking (n = 2457) and nonsmoking (n = 2445) locations were photographed by 169 smokers from Durham, North Carolina, and Pittsburgh, Pennsylvania, areas from 2010 to 2016. These images were used to develop a probabilistic classifier to predict the location type (smoking or nonsmoking location), thus relating objects and settings in daily environments to established smoking patterns. The classifier combines a deep convolutional neural network with an interpretable logistic regression model and was trained and evaluated via nested cross-validation with participant-wise partitions (ie, out-of-sample prediction). To contextualize model performance, images taken by 25 randomly selected participants were also classified by smoking cessation experts. As secondary validation, craving levels repor...Continue Reading

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Citations

Mar 21, 2020·NPJ Digital Medicine·Matthew M EngelhardF Joseph McClernon
Feb 18, 2021·NPJ Digital Medicine·Matthew M EngelhardF Joseph McClernon
Aug 29, 2021·Tobacco Control·Rui FuMichael Oliver Chaiton

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Software Mentioned

JITAI
Google Clip
Inception
TensorFlow
HERO
Scikit
learn

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