BackgroundSoftware designed to accurately estimate food calories from still images could help users and health professionals identify dietary patterns and food choices associated with health and health risks more effectively. However, calorie estimation from images is difficult, and no publicly available software can do so accurately while minimizing the burden associated with data collection and analysis.ObjectiveThe aim of this study was to determine the accuracy of crowdsourced annotations of calorie content in food images and to identify and quantify sources of bias and noise as a function of respondent characteristics and food qualities (eg, energy density).MethodsWe invited adult social media users to provide calorie estimates for 20 food images (for which ground truth calorie data were known) using a custom-built webpage that administers an online quiz. The images were selected to provide a range of food types and energy density. Participants optionally provided age range, gender, and their height and weight. In addition, 5 nutrition experts provided annotations for the same data to form a basis of comparison. We examined estimated accuracy on the basis of expertise, demographic data, and food qualities using linear mixed-effects models with participant and image index as random variables. We also analyzed the advantage of aggregating nonexpert estimates.ResultsA total of 2028 respondents agreed to participate in the study (males: 770/2028, 37.97%, mean body mass index: 27.5 kg/m2). Average accuracy was 5 out of 20 correct guesses, where “correct” was defined as a number within 20% of the ground truth. Even a small crowd of 10 individuals achieved an accuracy of 7, exceeding the average individual and expert annotator’s accuracy of 5. Women were more accurate than men (P<.001), and younger people were more accurate than older people (P<.001). The calorie content of energy-dense foods was overestimated (P=.02). Participants performed worse when images contained reference objects, such as credit cards, for scale (P=.01).ConclusionsOur findings provide new information about how calories are estimated from food images, which can inform the design of related software and analyses.

Background: Software to accurately estimate food calories from still images could help users and health professionals more efficiently identify dietary patterns and food choices associated with health and health risks. However, calorie estimation from images is difficult, and no publicly available software can do so accurately while minimizing the burden associated with data collection and analysis. Objective: The aim of this study is to determine the accuracy of crowdsourced annotations of calorie content in food images, and to identify and quantify sources of bias and noise as a function of respondent characteristics and food qualities (e.g., energy density). Methods: We invited adult social media users to provide calorie estimates for 20 food images (for which ground truth calorie data were known) using a custom-built webpage that administers an online quiz. The images were selected to provide a range of food types and energy density. Participants optionally provided age range, gender, and their height and weight. Additionally, five nutrition experts provided annotations for the same data to form a basis of comparison. We examined estimate accuracy on the basis of expertise, demographic data, and food qualities using linear mixed effects models with participant and image index as random variables. We also analyzed the advantage of aggregating nonexpert estimates. Results: 2028 respondents agreed to participate in the study (males: 770 [38%], mean body mass index: 27.5). Average accuracy was 5 out of 20 correct guesses, where "correct" was defined as a number within 20% of the ground truth. Even a small crowd of 10 individuals achieved an accuracy of 7, exceeding the average individual's and expert annotator's accuracy of 5. Women were more accurate than men (P<.001), and younger people were more accurate than older people (P<.001). The calorie content of energy-dense foods was overestimated (P=.024). Participants did not perform better when images contained reference objects (such as credit cards) for scale. Conclusions: Our findings provide new information about the way we process food information, which can inform the design of future calorie-estimation applications.