Calorie Estimation From Pictures of Food: Crowdsourcing Study (Preprint)

Abstract

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.

Event-shape engineering and heavy-flavour observables in relativistic heavy-ion collisions

“…More recent measurements opened the possibility to get access to a richer information. Studies of D s and Λ c production in nuclear collisions have the potential to put the issue of medium-modification of heavy-flavour hadrochemistry on solid ground [14,15], making possible to validate heavy-quark hadronization models based on the recombination with light thermal partons. Experimental data on B meson production [16] allow one to study the mass dependence of the heavy-quark medium interaction; if in the future these analysis were extended to lower transverse momentum they would allow one to perform a theoryto-experiment comparison in a kinematic region in which transport calculations are under the best control, reaching the goal of really measuring the heavy-quark diffusion coefficient.…”