An Intelligent Vision-Based Nutritional Assessment Method for Handheld Food Items

Malnutrition is a common and severe problem in patients with cancer that directly increases the incidence of complications and significantly deteriorates quality of life. Nutritional risk screening and dietary assessment are critical because they are the basis for providing personalized nutritional support. No digital smartphone-based self-administered tool for nutritional risk screening and dietary assessment among hospitalized patients with cancer has been developed and evaluated. This study aims to develop a digital smartphone-based self-administered mini program for nutritional risk screening and dietary assessment for hospitalized patients with cancer and to evaluate the validity of the mini program. We have developed the R+ Dietitian mini program, which consists of 3 parts: (1) collection of basic information of patients, (2) nutritional risk screening, and (3) dietary energy and protein assessment. The face-to-face paper-based Nutritional Risk Screening (NRS-2002), the Patient-Generated Subjective Global Assessment Short Form (PG-SGA-SF), and 3 days of 24-hour dietary recall (3d-24HRs) questionnaires were administered according to standard procedure by 2 trained dietitians as the reference methods. Sensitivity, specificity, positive predictive value, negative predictive value, κ value, and correlation coefficients (CCs) of nutritional risk screened in R+ Dietitian against the reference methods, as well as the difference and CCs of estimated dietary energy and protein intakes between R+ Dietitian and 3d-24HRs were calculated to evaluate the validity of R+ Dietitian. A total of 244 hospitalized patients with cancer were recruited to evaluate the validity of R+ Dietitian. The NRS-2002 and PG-SGA-SF tools in R+ Dietitian showed high accuracy, sensitivity, and specificity (77.5%, 81.0%, and 76.7% and 69.3%, 84.5%, and 64.5%, respectively), and fair agreement (κ=0.42 and 0.37, respectively; CC 0.62 and 0.56, respectively) with the NRS-2002 and PG-SGA-SF tools administered by dietitians. The estimated intakes of dietary energy and protein were significantly higher (P<.001 for both) in R+ Dietitian (mean difference of energy intake: 144.2 kcal, SD 454.8; median difference of protein intake: 10.7 g, IQR 9.5-39.8), and showed fair agreement (CC 0.59 and 0.47, respectively), compared with 3d-24HRs performed by dietitians. The identified nutritional risk and assessment of dietary intakes of energy and protein in R+ Dietitian displayed a fair agreement with the screening and assessment conducted by dietitians. R+ Dietitian has the potential to be a tool for nutritional risk screening and dietary intake assessment among hospitalized patients with cancer. Chinese Clinical Trial Registry ChiCTR1900026324; https://www.chictr.org.cn/showprojen.aspx?proj=41528.

FVEstimator: A novel food volume estimator Wellness model for calorie measurement and healthy living

SPC: Self-supervised point cloud completion.

BackgroundFood Frequency Questionnaire (FFQ) is an effective tool for nutritional status assessment. This study had two objectives: (1) to develop a FFQ for habitual dietary intake assessment of children and adolescents with overweight or obesity and (2) to evaluate the reproducibility of this FFQ.MethodsA cross-sectional study was conducted in the Obesity Clinics at King Khalid University Hospital (KKUH), Riyadh, Saudi Arabia. A final sample of 236 children and adolescents (115/121: M/F; age 6–17 years) was used after they provided a written consent. Their anthropometrics were measured and they completed a a 3-day food record (3DFR) questionnaire. We constructed a comprehensive food list based on the most commonly reported food items and portion size reported in 3DFR. This food list was converted to FFQ format using the typically reported portions. FFQ was reduced and developed into final form. The newly developed 74-item FFQ was administered twice over a 4-week period. Reproducibility was explored by comparing the nutrients and food intakes of FFQ1 and FFQ2. All statistical analyses were done on SPSS (version 18.0 (SPSS, Inc., Chicago, IL, USA).ResultsThe newly developed FFQ had 74 food items distributed in 7 food groups. A satisfactory reproducibility of FFQ was achieved. The average correlation coefficients of nutrients and food groups were 0.46 and 0.49, respectively. The average % agreement of nutrients and food groups were 70.1 and 70.9%, respectively.ConclusionThe newly developed FFQ had 74 food items broadly categorized in 7 food groups and yielded satisfactory reproducibility. We conclude the newly developed FFQ may be a useful tool for further research for dietary assessment of children and adolescents with overweight and obesity in Saudi Arabia.

Objectives Dietary patterns are studied to determine the relationship between diet and health. However, little is known about whether various dietary patterns fulfill adequate nutrient intake. Therefore, the purpose of this study was to investigate the association between major dietary patterns and nutritional intakes in middle-aged and elderly men. Furthermore, we examined the associations between dietary patterns and micronutrients status by using the reference values from the Dietary Reference Intakes for Japanese 2010 (DRIs-J 2010).Methods A total of 229 middle-aged and elderly men (age range, 40-79 years) participated in this study. The dietary patterns were determined using principal component analysis of 52 food and beverage items via a validated brief diet history questionnaire. Overall, micronutrient intake status was quantified using a dietary reference intakes score (DRIs-score) for 21 micronutrients (based on the DRIs-J 2010). The association of the nutrient intake and the DRIs-score with each factor score was evaluated using Spearman's correlation coefficient.Results Three dietary patterns were identified: "side dish", "evening drink", and "snack". The "side dish" pattern was characterized by a high intake of vegetables, fruits, seaweeds, mushrooms, and potatoes, and low intake of rice. Spearman's correlation showed that the "side dish" pattern correlated with each of the 21 micronutrients, and positively correlated with the DRIs-score (ρ=0.782, P<0.001).Conclusions The "side dish" pattern was positively related with the DRIs-score, calculated from the DRIs-J 2010. The result suggested the "side dish" pattern may provide a favorable nutrient balance in middle-aged and elderly men.

Domain-invariant representation learning using an unsupervised domain adversarial adaptation deep neural network

Purpose: This study examined the impact of including weekend days in 24-hour dietary recalls on dietary intake patterns and investigated associations between dietary intake—analyzed using nutrient- and food-group–based categories—and cardiovascular risk factors in adults with metabolic syndrome (MetS). Methods: A cross-sectional study analyzed data from 185 adults aged 40-69 years with metabolic syndrome (MetS), who had either a history of cardiovascular disease or an intermediate-to-high 10-year cardiovascular disease risk. Dietary intake was assessed over three days using 24-hour dietary recalls and analyzed with CAN-Pro 4.0. Results: Overall dietary intake did not differ significantly according to the number of weekend days included, except food-group–based protein intake (p = .030). In the no-weekend group, most nutrients and food groups demonstrated relatively consistent correlations between the 3-day average intake and single-day intake (r ≥ .61). In contrast, correlation coefficients varied widely among groups that included weekend days (r = −.08 to .97). Further analyses stratified by specific weekend days (saturdays, sundays, and national holidays) revealed significant differences in nutrient-based protein intake (F = 3.38, p = .011). Both nutrient- and food-group–based dietary intakes were significantly associated with cardiovascular risk factors. Conclusion: The number of weekend days included in dietary assessment may influence estimated dietary intake. The observed variability across specific weekend days suggests that using multiple 24-hour dietary assessments and developing nutritional interventions that explicitly account for weekend schedules would improve the accuracy of dietary pattern assessment and the effectiveness of interventions in individuals with MetS.

Relevance. The objectivity of assessing the professional activities of pedagogical staff is a pressing task in modern education. This is due to dynamic changes in the organization of the educational process and increasing demands on the professional competence of pedagogical staff. Traditional methods of assessing the professional activities of pedagogical staff are often subjective and do not fully reveal their level of knowledge, skills, and abilities. In this context, artificial intelligence offers tools for a more accurate, transparent, and comprehensive assessment of the professional activities of pedagogical staff based on the analysis of data on teaching quality, their interaction with general secondary education students, and learning outcomes. Purpose. The purpose of the article is to investigate the role of artificial intelligence in enhancing the objectivity of assessing the professional activities of pedagogical staff in general secondary education institutions. Methods. The research methods included: studying scientific sources and regulatory documents concerning the use of artificial intelligence technologies in education to identify the state of research on the problem; theoretical analysis, synthesis, and generalization of views to substantiate the role of artificial intelligence in the objective assessment of the professional activities of pedagogical staff in general secondary education institutions; and generalization of findings. Results. The article substantiates the role of artificial intelligence in the objectivity of assessing the professional activities of pedagogical staff in general secondary education institutions based on the use of big data processing algorithms and comprehensive analytics, automation of the collection and analysis of quantitative and qualitative indicators of pedagogical staff's professional activities, the structure of their interaction with students, analysis of competency development dynamics, learning materials (through natural language processing), and student learning outcomes. The features of using artificial intelligence in assessing the professional activities of pedagogical staff are revealed through task personalization and the provision of individual recommendations for their further professional development. Key advantages and challenges associated with the use of artificial intelligence in assessing the professional activities of pedagogical staff in general secondary education institutions are identified. Conclusions. The study found that the use of artificial intelligence significantly enhances the objectivity, efficiency, and transparency of assessing the professional activities of pedagogical staff in general secondary education institutions, as it allows for a shift from quantitative assessment criteria to comprehensive analysis and contributes to the formation of individual trajectories for their professional growth. Successful implementation of artificial intelligence in assessing the professional activities of pedagogical staff is based on considering unified methodological approaches and standards, as well as the availability of appropriate technical infrastructure.

Accurate estimation of nutritional information may lead to healthier diets and better clinical outcomes. We propose a dietary assessment system based on artificial intelligence (AI), named goFOODTM. The system can estimate the calorie and macronutrient content of a meal, on the sole basis of food images captured by a smartphone. goFOODTM requires an input of two meal images or a short video. For conventional single-camera smartphones, the images must be captured from two different viewing angles; smartphones equipped with two rear cameras require only a single press of the shutter button. The deep neural networks are used to process the two images and implements food detection, segmentation and recognition, while a 3D reconstruction algorithm estimates the food’s volume. Each meal’s calorie and macronutrient content is calculated from the food category, volume and the nutrient database. goFOODTM supports 319 fine-grained food categories, and has been validated on two multimedia databases that contain non-standardized and fast food meals. The experimental results demonstrate that goFOODTM performed better than experienced dietitians on the non-standardized meal database, and was comparable to them on the fast food database. goFOODTM provides a simple and efficient solution to the end-user for dietary assessment.

Children are vulnerable to developing iron deficiency anemia, especially in resource-limited settings. Information on habitual dietary intake informs dietary interventions aimed at improving iron deficiency. Dietary assessment in school-aged children is challenging and requires concerted efforts to mitigate the pitfalls of long complex methods. Nested within an intervention trial, we aimed to obtain dietary intake information to assess iron nutrition in 8 to 13-year-old children from resource-limited settings in Cape Town, South Africa. Following careful consideration of the study objective, participant characteristics, research setting, available resources, and features of the different dietary intake assessment methods, we identified an iron quantified food frequency questionnaire (QFFQ) as the best method to obtain the information. The QFFQ reflected the study population’s habitual intake and the nutrients of interest (protein, iron, vitamin A, vitamin C, calcium, zinc, and fiber). In addition, strategies such as interview-administration of the QFFQ, interviewing the child participant and caregiver together, simplifying frequency reporting, a strategic food list order and a variety of interesting portion size estimation aids collectively supported dietary intake assessment in this young study population. Using a methodical, multiphase approach and strategies that promote participant engagement, we developed the QFFQ, achieved interview success, and obtained comparable data.

Objective: Although research studies increasingly use children as primary reporters in dietary assessments, it is unclear how well children’s self-reported intake correlates with independently validated reports of their intake; this meta-analysis assesses that correlation.Design: Moderators of the correlation between self-reported and independently validated intake were predicted a priori: type of dietary intake assessment (24 h recall, food diary and food frequency questionnaires), validation measures, parental assistance and age. Online databases were searched for articles published from 1990 to 2014 that compared children’s self-reports of dietary intake to validated observations of food intake in children age 4–16.Main outcome measures: Summary effect size Pearson r between children’s self-reported dietary intake and independently validated dietary intake were calculated.Results: In k = 32 samples from 23 studies, a statistically significant correlation (r = .48, Z = 7.26, p < .001) was found between children’s self-reported dietary intake and independently validated reports of dietary intake. Validation method (Q = 17.49, df = 2, p < .001) and parental assistance (Z = 2.03, p = .042) were significant moderators of this correlation. Self-report methodology (Q = 3.95, df = 2, p = .139) and age (Q = .02, p = .879) were not significant moderators of the distribution of effect sizes.Conclusion: Together, these results provide baseline information about children’s recall in dietary intake assessments conducted with children as primary reporters.

Understanding the dietary intake of people with kidney disease is fundamental to addressing their treatment or for the prevention or progression of disease. Several recent guidelines on chronic kidney disease (CKD) vary on recommendations for assessing the dietary intake of patients (Table 3.1) [1–8]. Whether the approach to assessing dietary intake is considered warranted is not discussed in these guidelines, so perhaps it is not an issue of the reliability of dietary intake assessment methods but rather one of the origin of the guidelines. For example, the guidelines from the National Kidney Foundation Kidney Disease Outcomes Quality Initiative (NKF KDOQI) on nutrition [1] are written to be multidisciplinary and include guidelines for dietitians. The guidelines for CKD that were produced by the Academy of Nutrition and Dietetics are written specifically for dietitians [4]. However, many of the other guidelines for CKD are physician specific, and obtaining the dietary record is not included in their guidelines, perhaps because physicians do not normally collect this information. In contrast, assessment of dietary intake is a significant practice of dietetics, and they routinely evaluate dietary intake in assessing the nutritional status of patients.

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.

The use of a food logging app in the naturalistic setting fails to provide accurate measurements of nutrients and poses usability challenges.

BackgroundDietary assessment is a fundamental component of nutrition research and plays a pivotal role in managing chronic diseases. Traditional dietary assessment methods, particularly in the context of Thai cuisine, often require extensive training and may lead to estimation errors. ObjectivesTo address these challenges, Institute of Nutrition, Mahidol University (INMU) iFood, an innovative artificial intelligence–based Thai food dietary assessment system, allows for estimating the nutritive values of dishes from food images. MethodsINMU iFood leverages state-of-the-art technology and integrates a validated automated Thai food analysis system. Users can use 3 distinct input methods: food image recognition, manual input, and a convenient barcode scanner. This versatility simplifies the tracking of dietary intake while maximizing data quality at the individual level. The core improvement in INMU iFood can be attributed to 2 key factors, namely, the replacement of Yolov4-tiny with Yolov7 and the expansion of noncarbohydrate source foods in the training image data set. ResultsThis combination significantly enhances the system’s ability to identify food items, especially in scenarios with closely packed food images, thus improving accuracy. Validation results showcase the superior performance of the INMU iFood integrated V7-based system over its predecessor, V4-based, with notable improvements in protein and fat estimation. Furthermore, INMU iFood addresses limitations by offering users the option to import additional food products via a barcode scanner, thus providing access to a vast database of nutritional information through Open Food Facts. This integration ensures users can track their dietary intake effectively, with expanded access to over 3000 food items added to or updated in the Open Food Facts database covering a wide variety of dietary choices. ConclusionsINMU iFood is a promising tool for researchers, health care professionals, and individuals seeking to monitor their dietary intake within the context of Thai cuisine and for ultimately promoting better health outcomes and facilitating nutrition-related research.