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Browsing by Author "Lo, Benny"

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    Development and Validation of an Objective, Passive Dietary Assessment Method for Estimating Food and Nutrient Intake in Households in Low- and Middle-Income Countries: A Study Protocol
    (Oxford University Press, 2020) Jobarteh, Modou L.; McCrory, Megan A.; Lo, Benny; Sun, Mingui; Sazonov, Edward; Anderson, Alex K.; Jia, Wenyan; Maitland, Kathryn; Qiu, Jianing; Steiner-Asiedu, Matilda; Higgins, Janine A.; Baranowski, Tom; Olupot-Olupot, Peter; Frost, Gary; Imperial College London; Boston University; University of Pittsburgh; University of Alabama Tuscaloosa; University of Georgia; University of Ghana; University of Colorado Anschutz Medical Campus; Baylor College of Medicine; United States Department of Agriculture (USDA)
    Malnutrition is a major concern in low- and middle-income countries (LMIC), but the full extent of nutritional deficiencies remains unknown largely due to lack of accurate assessment methods. This study seeks to develop and validate an objective, passive method of estimating food and nutrient intake in households in Ghana and Uganda. Household members (including under-5s and adolescents) are assigned a wearable camera device to capture images of their food intake during waking hours. Using custom software, images captured are then used to estimate an individual's food and nutrient (i.e., protein, fat, carbohydrate, energy, and micronutrients) intake. Passive food image capture and assessment provides an objective measure of food and nutrient intake in real time, minimizing some of the limitations associated with self-reported dietary intake methods. Its use in LMIC could potentially increase the understanding of a population's nutritional status, and the contribution of household food intake to the malnutrition burden. This project is registered at clinicaltrials.gov (NCT03723460).
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    Feasibility of the automatic ingestion monitor (AIM-2) for infant feeding assessment: a pilot study among breast-feeding mothers from Ghana
    (Cambridge University Press, 2022) Cerminaro, Caroline; Sazonov, Edward; McCrory, Megan A.; Steiner-Asiedu, Matilda; Bhaskar, Viprav; Gallo, Sina; Laing, Emma; Jia, Wenyan; Sun, Mingui; Baranowski, Tom; Frost, Gary; Lo, Benny; Anderson, Alex Kojo; University of Georgia; University of Alabama Tuscaloosa; Boston University; University of Ghana; University of Pittsburgh; United States Department of Agriculture (USDA); Baylor College of Medicine; Imperial College London
    Objective: Passive, wearable sensors can be used to obtain objective information in infant feeding, but their use has not been tested. Our objective was to compare assessment of infant feeding (frequency, duration and cues) by self-report and that of the Automatic Ingestion Monitor-2 (AIM-2). Design: A cross-sectional pilot study was conducted in Ghana. Mothers wore the AIM-2 on eyeglasses for 1 d during waking hours to assess infant feeding using images automatically captured by the device every 15 s. Feasibility was assessed using compliance with wearing the device. Infant feeding practices collected by the AIM-2 images were annotated by a trained evaluator and compared with maternal self-report via interviewer-administered questionnaire. Setting: Rural and urban communities in Ghana. Participants: Participants were thirty eight (eighteen rural and twenty urban) breast-feeding mothers of infants (child age <= 7 months). Results: Twenty-five mothers reported exclusive breast-feeding, which was common among those < 30 years of age (n 15, 60 %) and those residing in urban communities (n 14, 70 %). Compliance with wearing the AIM-2 was high (83 % of wake-time), suggesting low user burden. Maternal report differed from the AIM-2 data, such that mothers reported higher mean breast-feeding frequency (eleven v. eight times, P = 0 center dot 041) and duration (18 center dot 5 v. 10 min, P = 0 center dot 007) during waking hours. Conclusion: The AIM-2 was a feasible tool for the assessment of infant feeding among mothers in Ghana as a passive, objective method and identified overestimation of self-reported breast-feeding frequency and duration. Future studies using the AIM-2 are warranted to determine validity on a larger scale.
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    Food/Non-Food Classification of Real-Life Egocentric Images in Low- and Middle-Income Countries Based on Image Tagging Features
    (Frontiers, 2021) Chen, Guangzong; Jia, Wenyan; Zhao, Yifan; Mao, Zhi-Hong; Lo, Benny; Anderson, Alex K.; Frost, Gary; Jobarteh, Modou L.; McCrory, Megan A.; Sazonov, Edward; Steiner-Asiedu, Matilda; Ansong, Richard S.; Baranowski, Thomas; Burke, Lora; Sun, Mingui; University of Pittsburgh; Imperial College London; University of Georgia; Boston University; University of Alabama Tuscaloosa; University of Ghana; United States Department of Agriculture (USDA); Baylor College of Medicine
    Malnutrition, including both undernutrition and obesity, is a significant problemin low- and middle-income countries (LMICs). In order to study malnutrition and develop effective intervention strategies, it is crucial to evaluate nutritional status in LMICs at the individual, household, and community levels. In a multinational research project supported by the Bill & Melinda Gates Foundation, we have been using a wearable technology to conduct objective dietary assessment in sub-Saharan Africa. Our assessment includes multiple diet-related activities in urban and rural families, including food sources (e.g., shopping, harvesting, and gathering), preservation/storage, preparation, cooking, and consumption (e.g., portion size and nutrition analysis). Our wearable device ("eButton" worn on the chest) acquires real-life images automatically during wake hours at preset time intervals. The recorded images, in amounts of tens of thousands per day, are post-processed to obtain the information of interest. Although we expect future Artificial Intelligence (AI) technology to extract the information automatically, at present we utilize AI to separate the acquired images into two binary classes: images with (Class 1) and without (Class 0) edible items. As a result, researchers need only to study Class-1 images, reducing their workload significantly. In this paper, we present a composite machine learning method to perform this classification, meeting the specific challenges of high complexity and diversity in the real-world LMIC data. Our method consists of a deep neural network (DNN) and a shallow learning network (SLN) connected by a novel probabilistic network interface layer. After presenting the details of our method, an image dataset acquired from Ghana is utilized to train and evaluate the machine learning system. Our comparative experiment indicates that the new composite method performs better than the conventional deep learning method assessed by integrated measures of sensitivity, specificity, and burden index, as indicated by the Receiver Operating Characteristic (ROC) curve.
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    I2N: image to nutrients, a sensor guided semi-automated tool for annotation of images for nutrition analysis of eating episodes
    (Frontiers, 2023) Ghosh, Tonmoy; McCrory, Megan A.; Marden, Tyson; Higgins, Janine; Anderson, Alex Kojo; Domfe, Christabel Ampong; Jia, Wenyan; Lo, Benny; Frost, Gary; Steiner-Asiedu, Matilda; Baranowski, Tom; Sun, Mingui; Sazonov, Edward; University of Alabama Tuscaloosa; Boston University; University of Colorado Denver; University of Colorado Anschutz Medical Campus; University of Georgia; University of Pittsburgh; Imperial College London; University of Ghana; Baylor College of Medicine
    IntroductionDietary assessment is important for understanding nutritional status. Traditional methods of monitoring food intake through self-report such as diet diaries, 24-hour dietary recall, and food frequency questionnaires may be subject to errors and can be time-consuming for the user. MethodsThis paper presents a semi-automatic dietary assessment tool we developed - a desktop application called Image to Nutrients (I2N) - to process sensor-detected eating events and images captured during these eating events by a wearable sensor. I2N has the capacity to offer multiple food and nutrient databases (e.g., USDA-SR, FNDDS, USDA Global Branded Food Products Database) for annotating eating episodes and food items. I2N estimates energy intake, nutritional content, and the amount consumed. The components of I2N are three-fold: 1) sensor-guided image review, 2) annotation of food images for nutritional analysis, and 3) access to multiple food databases. Two studies were used to evaluate the feasibility and usefulness of I2N: 1) a US-based study with 30 participants and a total of 60 days of data and 2) a Ghana-based study with 41 participants and a total of 41 days of data). ResultsIn both studies, a total of 314 eating episodes were annotated using at least three food databases. Using I2N's sensor-guided image review, the number of images that needed to be reviewed was reduced by 93% and 85% for the two studies, respectively, compared to reviewing all the images. DiscussionI2N is a unique tool that allows for simultaneous viewing of food images, sensor-guided image review, and access to multiple databases in one tool, making nutritional analysis of food images efficient. The tool is flexible, allowing for nutritional analysis of images if sensor signals aren't available.
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    A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation
    (MDPI, 2022) Jia, Wenyan; Ren, Yiqiu; Li, Boyang; Beatrice, Britney; Que, Jingda; Cao, Shunxin; Wu, Zekun; Mao, Zhi-Hong; Lo, Benny; Anderson, Alex K.; Frost, Gary; McCrory, Megan A.; Sazonov, Edward; Steiner-Asiedu, Matilda; Baranowski, Tom; Burke, Lora E.; Sun, Mingui; University of Pittsburgh; Imperial College London; University of Georgia; Boston University; University of Alabama Tuscaloosa; University of Ghana; Baylor College of Medicine; United States Department of Agriculture (USDA)
    Knowing the amounts of energy and nutrients in an individual's diet is important for maintaining health and preventing chronic diseases. As electronic and AI technologies advance rapidly, dietary assessment can now be performed using food images obtained from a smartphone or a wearable device. One of the challenges in this approach is to computationally measure the volume of food in a bowl from an image. This problem has not been studied systematically despite the bowl being the most utilized food container in many parts of the world, especially in Asia and Africa. In this paper, we present a new method to measure the size and shape of a bowl by adhering a paper ruler centrally across the bottom and sides of the bowl and then taking an image. When observed from the image, the distortions in the width of the paper ruler and the spacings between ruler markers completely encode the size and shape of the bowl. A computational algorithm is developed to reconstruct the three-dimensional bowl interior using the observed distortions. Our experiments using nine bowls, colored liquids, and amorphous foods demonstrate high accuracy of our method for food volume estimation involving round bowls as containers. A total of 228 images of amorphous foods were also used in a comparative experiment between our algorithm and an independent human estimator. The results showed that our algorithm overperformed the human estimator who utilized different types of reference information and two estimation methods, including direct volume estimation and indirect estimation through the fullness of the bowl.