Artificial Intelligence in Nutrients Science Research: A Review

The Role of Artificial Intelligence in Nutrition Research: A Scoping Review

Artificial intelligence (AI) refers to computer systems doing tasks that usually need human intelligence. AI is constantly changing and is revolutionizing the healthcare field, including nutrition. This review's purpose is four-fold: (i) to investigate AI's role in nutrition research; (ii) to identify areas in nutrition using AI; (iii) to understand AI's future potential impact; (iv) to investigate possible concerns about AI's use in nutrition research. Eight databases were searched: PubMed, Web of Science, EBSCO, Agricola, Scopus, IEEE Explore, Google Scholar and Cochrane. A total of 1737 articles were retrieved, of which 22 were included in the review. Article screening phases included duplicates elimination, title-abstract selection, full-text review, and quality assessment. The key findings indicated AI's role in nutrition is at a developmental stage, focusing mainly on dietary assessment and less on malnutrition prediction, lifestyle interventions, and diet-related diseases comprehension. Clinical research is needed to determine AI's intervention efficacy. The ethics of AI use, a main concern, remains unresolved and needs to be considered for collateral damage prevention to certain populations. The studies' heterogeneity in this review limited the focus on specific nutritional areas. Future research should prioritize specialized reviews in nutrition and dieting for a deeper understanding of AI's potential in human nutrition.

We got nuts! use deep neural networks to classify images of common edible nuts

BACKGROUND

Nuts are nutrient-dense foods that contribute to healthier eating. Food image datasets enable artificial intelligence (AI) powered diet-tracking apps to help people monitor daily eating patterns.

AIM

This study aimed to create an image dataset of commonly consumed nut types and use it to build an AI computer vision model to automate nut type classification tasks.

METHODS

iPhone 11 was used to take photos of 11 nut types-almond, brazil nut, cashew, chestnut, hazelnut, macadamia, peanut, pecan, pine nut, pistachio, and walnut. The dataset contains 2200 images, 200 per nut type. The dataset was randomly split into the training (60% or 1320 images), validation (20% or 440 images), and test sets (20% or 440 images). A neural network model was constructed and trained using transfer learning and other computer vision techniques-data augmentation, mixup, normalization, label smoothing, and learning rate optimization.

RESULTS

The trained neural network model correctly predicted 338 out of 440 images (40 per nut type) in the validation set, achieving 99.55% accuracy. Moreover, the model classified the 440 images in the test set with 100% accuracy.

CONCLUSION

This study built a nut image dataset and used it to train a neural network model to classify images by nut type. The model achieved near-perfect accuracy on the validation and test sets, demonstrating the feasibility of automating nut type classification using smartphone photos. Being made open-source, the dataset and model can assist the development of diet-tracking apps that facilitate users' adoption and adherence to a healthy diet.

How artificial intelligence can provide information about subdural hematoma: Assessment of readability, reliability, and quality of ChatGPT, BARD, and perplexity responses

Subdural hematoma is defined as blood collection in the subdural space between the dura mater and arachnoid. Subdural hematoma is a condition that neurosurgeons frequently encounter and has acute, subacute and chronic forms. The incidence in adults is reported to be 1.72-20.60/100.000 people annually. Our study aimed to evaluate the quality, reliability and readability of the answers to questions asked to ChatGPT, Bard, and perplexity about "Subdural Hematoma." In this observational and cross-sectional study, we asked ChatGPT, Bard, and perplexity to provide the 100 most frequently asked questions about "Subdural Hematoma" separately. Responses from both chatbots were analyzed separately for readability, quality, reliability and adequacy. When the median readability scores of ChatGPT, Bard, and perplexity answers were compared with the sixth-grade reading level, a statistically significant difference was observed in all formulas (P < .001). All 3 chatbot responses were found to be difficult to read. Bard responses were more readable than ChatGPT's (P < .001) and perplexity's (P < .001) responses for all scores evaluated. Although there were differences between the results of the evaluated calculators, perplexity's answers were determined to be more readable than ChatGPT's answers (P < .05). Bard answers were determined to have the best GQS scores (P < .001). Perplexity responses had the best Journal of American Medical Association and modified DISCERN scores (P < .001). ChatGPT, Bard, and perplexity's current capabilities are inadequate in terms of quality and readability of "Subdural Hematoma" related text content. The readability standard for patient education materials as determined by the American Medical Association, National Institutes of Health, and the United States Department of Health and Human Services is at or below grade 6. The readability levels of the responses of artificial intelligence applications such as ChatGPT, Bard, and perplexity are significantly higher than the recommended 6th grade level.

DeepTrayMeal: Automatic dietary assessment for Chinese tray meals based on deep learning

Tray meal is a popular way of eating in China, and tray-based automatic dietary assessment is important for public health. Relevant research is lacking because public tray meal datasets and suitable methods are unavailable. In this study, we established and published the first Chinese tray meal dataset, the ChinaLunchTray-99. We collected real-world 1185 tray meal images, covering 99 dish categories with corresponding manually annotated bounding box and category-level labels. We developed a new framework for automatic dietary assessment, which consists of dish image recognition, volume estimation and nutrition mapping. First, we demonstrated a tray meal detection model considering feature extraction, anchor scales, and loss function, resulting in a high mean Average Precision of 92.13%. Second, we proposed an automatic method to estimate volume via detection results and tray's information. Finally, nutrients were mapped from the estimated volume. Our research can promote applications of automatic dietary assessment for Chinese tray meals.

Innovative strategies to predict and prevent the risk for malnutrition in child, adolescent, and young adult cancer survivors

Children, adolescents, and young adult cancer survivors (CAYAs) constitute a growing population requiring a customized approach to mitigate the incidence of severe complications throughout their lifetimes. During cancer treatment, CAYAs cancer survivors undergo significant disruptions in their nutritional status, elevating the risks of mortality, morbidity, and cardiovascular events. The assessment of nutritional status during cancer treatment involves anthropometric and dietary evaluations, emphasizing the necessity for regular assessments and the timely identification of risk factors. Proactive nutritional interventions, addressing both undernutrition and overnutrition, should be tailored to specific age groups and incorporate a family-centered approach. Despite encouraging interventions, a notable evidence gap persists. The goal of this review is to comprehensively examine the existing evidence on potential nutritional interventions for CAYAs cancer survivors. We explore the evidence so far collected on the nutritional intervention strategies elaborated for CAYAs cancer survivors that should target both undernutrition and overnutrition, being age-specific and involving a family-based approach. Furthermore, we suggest harnessing artificial intelligence (AI) to anticipate and prevent malnutrition in CAYAs cancer survivors, contributing to the identification of novel risk factors and promoting proactive, personalized healthcare.

Effect of vitamin D supplementation on inflammatory markers and total antioxidant capacity in breast cancer women using a machine learning technique

Aim

This study aimed to establish a learning system using an artificial neural network (ANN) to predict the effects of vitamin D supplementation on the serum levels of vitamin D, inflammatory factors, and total antioxidant capacity (TAC) in women with breast cancer.

Methods

The data set of the current project was created from women with breast cancer who were referred to the Shafa State Hospital of Patients with Cancers in Ahvaz city, Iran. Modeling was implemented using the data set at the serum levels of vitamin D, tumor necrosis factor-α (TNF-α), transforming growth factor β (TGF-β), and TAC, before and after vitamin D3 supplement therapy. A prediction ANN model was designed to detect the effects of vitamin D3 supplementation on the serum level changes of vitamin D, inflammatory factors and TAC.

Results

The results showed that the ANN model could predict the effect of vitamin D3 supplementation on the serum level changes of vitamin D, TNF-α, TGF-β1, and TAC with an accuracy average of 85%, 40%, 89.5%, and 88.1%, respectively.

Conclusions

According to the findings of the study, the ANN method could accurately predict the effect of vitamin D3 supplementation on the serum levels of vitamin D, TNF-α, TGF-β1, and TAC. The results showed that the proposed ANN method can help specialists to improve the treatment process more confidently in terms of time and accuracy of predicting the influence of vitamin D supplementation on the factors affecting the progression of breast cancer (https://www.irct.ir/ identifier: IRCT2015090623924N1).

Machine learning accurately predicts food exchange list and the exchangeable portion

Introduction

Food Exchange Lists (FELs) are a user-friendly tool developed to help individuals aid healthy eating habits and follow a specific diet plan. Given the rapidly increasing number of new products or access to new foods, one of the biggest challenges for FELs is being outdated. Supervised machine learning algorithms could be a tool that facilitates this process and allows for updated FELs-the present study aimed to generate an algorithm to predict food classification and calculate the equivalent portion.

Methods

Data mining techniques were used to generate the algorithm, which consists of processing and analyzing the information to find patterns, trends, or repetitive rules that explain the behavior of the data in a food database after performing this task. It was decided to approach the problem from a vector formulation (through 9 nutrient dimensions) that led to proposals for classifiers such as Spherical K-Means (SKM), and by developing this idea, it was possible to smooth the limits of the classifier with the help of a Multilayer Perceptron (MLP) which were compared with two other algorithms of machine learning, these being Random Forest and XGBoost.

Results

The algorithm proposed in this study could classify and calculate the equivalent portion of a single or a list of foods. The algorithm allows the categorization of more than one thousand foods with a confidence level of 97% at the first three places. Also, the algorithm indicates which foods exceed the limits established in sodium, sugar, and/or fat content and show their equivalents.

Discussion

Accurate and robust FELs could improve implementation and adherence to the recommended diet. Compared with manual categorization and calculation, machine learning approaches have several advantages. Machine learning reduces the time needed for manual food categorization and equivalent portion calculation of many food products. Since it is possible to access food composition databases of various populations, our algorithm could be adapted and applied in other databases, offering an even greater diversity of regional products and foods. In conclusion, machine learning is a promising method for automation in generating FELs. This study provides evidence of a large-scale, accurate real-time processing algorithm that can be useful for designing meal plans tailored to the foods consumed by the population. Our model allowed us not only to distinguish and classify foods within a group or subgroup but also to perform the calculation of an equivalent food. As a neural network, this model could be trained with other food bases and thus improve its predictive capacity. Although the performance of the SKM model was lower compared to other types of classifiers, our model allows selecting an equivalent food not from a group previously classified by machine learning but with a fully interpretable algorithm such as cosine similarity for comparing food.

A paradigm shift in clinical nutrition

The current clinical nutrition paradigm is that decreased caloric intake, resulting in a caloric deficit, is central to the development disease-related malnutrition (DRM). In following with this paradigm, one should assume that nutrition interventions with artificially administered nutrition (food substitution paradigm) aimed at preventing a caloric deficit should result in the prevention and/or successful treatment of DRM. However, clear evidence demonstrates that the DRM observed in diverse illnesses is at least partially resistant to nutrition interventions aimed at preventing the development of a caloric deficit. Simply put, DRM cannot be prevented nor resolved through a nutrition intervention aimed solely on replacing what the person cannot or will not eat. It is time to stop oversimplifying nutrition therapy in clinical nutrition interventions as a food substitution issue, focusing instead on developing and testing innovative hypotheses aimed at a mechanistic understanding of how DRM develops. Through this effort, new paradigms should evolve. The aim of this opinion paper is to provide an overview of why we need a shift in the current paradigm.

Algorithmic lifestyle optimization

OBJECTIVE

A hallmark of personalized medicine and nutrition is to identify effective treatment plans at the individual level. Lifestyle interventions (LIs), from diet to exercise, can have a significant effect over time, especially in the case of food intolerances and allergies. The large set of candidate interventions, make it difficult to evaluate which intervention plan would be more favorable for any given individual. In this study, we aimed to develop a method for rapid identification of favorable LIs for a given individual.

MATERIALS AND METHODS

We have developed a method, algorithmic lifestyle optimization (ALO), for rapid identification of effective LIs. At its core, a group testing algorithm identifies the effectiveness of each intervention efficiently, within the context of its pertinent group.

RESULTS

Evaluations on synthetic and real data show that ALO is robust to noise, data size, and data heterogeneity. Compared to the standard of practice techniques, such as the standard elimination diet (SED), it identifies the effective LIs 58.9%-68.4% faster when used to discover an individual's food intolerances and allergies to 19-56 foods.

DISCUSSION

ALO achieves its superior performance by: (1) grouping multiple LIs together optimally from prior statistics, and (2) adapting the groupings of LIs from the individual's subsequent responses. Future extensions to ALO should enable incorporating nutritional constraints.

CONCLUSION

ALO provides a new approach for the discovery of effective interventions in nutrition and medicine, leading to better intervention plans faster and with less inconvenience to the patient compared to SED.

Discrepancy between Food Classification Systems: Evaluation of Nutri-Score, NOVA Classification and Chilean Front-of-Package Food Warning Labels

BACKGROUND

Currently, there are different food classification systems in order to inform the population of the best alternatives for consumption, considering all the diseases associated with the consumption of products of low nutritional quality. Reports indicate that these forms of labelling warnings correspond to a laudable strategy for populations that do not have the knowledge to discriminate between the wide range of products offered by the food industry. However, recent publications indicate that there may be inconsistencies between the different classification guidelines, and the guidelines that nations should adopt in their food guides are still a matter of debate. In view of this, the present study aimed to evaluate the quantitative and qualitative differences that exist between the NOVA, Nutri-Score and Chilean Front-of-package (FoP) food warning label according to the Chilean basic food basket list.

METHOD

An analytical study was carried out to classify a list of 736 foods according to three different systems, evaluating the distributions according to their methods of classifying the products. Quantitative differences were contrasted for each system, as well as between them, together with an analysis of the dimensions of each system.

RESULTS

According to the Nutri-Score classification, the most frequent category was A with 27% (high nutritional quality), followed by D with 22% (low nutritional quality) of the total. On the other hand, the NOVA classification showed that the most frequent categorization was ultra-processed food (NOVA 4) with 54%, followed by unprocessed (NOVA 1) with 19%. Regarding the FoP warning labels, 57% of the foods were categorized as free warning labels, followed by the category of foods with 3 warning labels (23%). Regarding the results of the principal component analysis, the Nutri-Score and FoP warning labels present a degree of similarity in their classification guidelines, being different than the dimension pointed out by NOVA.

CONCLUSION

The present work managed to demonstrate that there are quantitative and qualitative differences between the classification and recommendation guidelines of the Nutri-Score, NOVA and FoP warning labels, finding concrete discrepancies between them.

Statistical Methods for the Analysis of Food Composition Databases: A Review

Evidence-based knowledge of the relationship between foods and nutrients is needed to inform dietary-based guidelines and policy. Proper and tailored statistical methods to analyse food composition databases (FCDBs) could assist in this regard. This review aims to collate the existing literature that used any statistical method to analyse FCDBs, to identify key trends and research gaps. The search strategy yielded 4238 references from electronic databases of which 24 fulfilled our inclusion criteria. Information on the objectives, statistical methods, and results was extracted. Statistical methods were mostly applied to group similar food items (37.5%). Other aims and objectives included determining associations between the nutrient content and known food characteristics (25.0%), determining nutrient co-occurrence (20.8%), evaluating nutrient changes over time (16.7%), and addressing the accuracy and completeness of databases (16.7%). Standard statistical tests (33.3%) were the most utilised followed by clustering (29.1%), other methods (16.7%), regression methods (12.5%), and dimension reduction techniques (8.3%). Nutrient data has unique characteristics such as correlated components, natural groupings, and a compositional nature. Statistical methods used for analysis need to account for this data structure. Our summary of the literature provides a reference for researchers looking to expand into this area.

Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology

Nutritional epidemiology employs observational data to discover associations between diet and disease risk. However, existing analytic methods of dietary data are often sub-optimal, with limited incorporation and analysis of the correlations between the studied variables and nonlinear behaviours in the data. Machine learning (ML) is an area of artificial intelligence that has the potential to improve modelling of nonlinear associations and confounding which are found in nutritional data. These opportunities notwithstanding, the applications of ML in nutritional epidemiology must be approached cautiously to safeguard the scientific quality of the results and provide accurate interpretations. Given the complex scenario around ML, judicious application of such tools is necessary to offer nutritional epidemiology a novel analytical resource for dietary measurement and assessment and a tool to model the complexity of dietary intake and its relation to health. This work describes the applications of ML in nutritional epidemiology and provides guidelines to avoid common pitfalls encountered in applying predictive statistical models to nutritional data. Furthermore, it helps unfamiliar readers better assess the significance of their results and provides new possible future directions in the field of ML in nutritional epidemiology.

A Cross-Sectional Reproducibility Study of a Standard Camera Sensor Using Artificial Intelligence to Assess Food Items: The FoodIntech Project

Having a system to measure food consumption is important to establish whether individual nutritional needs are being met in order to act quickly and to minimize the risk of undernutrition. Here, we tested a smartphone-based food consumption assessment system named FoodIntech. FoodIntech, which is based on AI using deep neural networks (DNN), automatically recognizes food items and dishes and calculates food leftovers using an image-based approach, i.e., it does not require human intervention to assess food consumption. This method uses one-input and one-output images by means of the detection and synchronization of a QRcode located on the meal tray. The DNN are then used to process the images and implement food detection, segmentation and recognition. Overall, 22,544 situations analyzed from 149 dishes were used to test the reliability of this method. The reliability of the AI results, based on the central intra-class correlation coefficient values, appeared to be excellent for 39% of the dishes (n = 58 dishes) and good for 19% (n = 28). The implementation of this method is an effective way to improve the recognition of dishes and it is possible, with a sufficient number of photos, to extend the capabilities of the tool to new dishes and foods.

Challenges for Estimating the Global Prevalence of Micronutrient Deficiencies and Related Disease Burden: A Case Study of the Global Burden of Disease Study

Information on the prevalence of micronutrient deficiencies is needed to determine related disease burden; underpin evidence-based advocacy; and design, deliver, and monitor safe, effective interventions. Assessing the global prevalence of deficiency requires a valid micronutrient status biomarker with an appropriate cutoff to define deficiency and relevant data from representative surveys across multiple locations and years. The Global Burden of Disease Study includes prevalence estimates for iodine, iron, zinc, and vitamin A deficiencies, for which recommended biomarkers and appropriate deficiency cutoffs exist. Because representative survey data are lacking, only retinol concentration is used to model vitamin A deficiency, and proxy indicators are used for the other micronutrients (goiter for iodine, hemoglobin for iron, and dietary food adequacy for zinc). Because of data limitations, complex statistical modeling is required to produce current estimates, relying on assumptions and proxies that likely understate the extent of micronutrient deficiencies and the consequent global health burden.

Postprandial concentration of circulating branched chain amino acids are able to predict the carbohydrate content of the ingested mixed meal

BACKGROUND

The amount of the macronutrients protein and carbohydrate (CHO) in a mixed meal is known to affect each other's digestion, absorption, and subsequent metabolism. While the effect of the amount of dietary protein and fat on the glycemic response is well studied, the ability of postprandial plasma amino acid patterns to predict the meal composition is unknown.

OBJECTIVE

To study the postprandial plasma amino acid patterns in relation to the protein, CHO, and fat content of different mixed meals and to investigate if these patterns can predict the macronutrient meal composition.

DESIGN

Ten older adults were given 9 meals with 3 different levels (low, medium, and high) of protein, CHO, and fat in different combinations, taking the medium content as that of a standardized western meal. We monitored the postprandial plasma response for amino acids, glucose, insulin, and triglycerides for 8 h and the areas under the curve (AUC) were subsequently calculated. Multiple regression analysis was performed to determine if amino acid patterns could predict the meal composition.

RESULTS

Increasing meal CHO content reduced the postprandial plasma response of several amino acids including all branched chain amino acids (BCAA) (leucine; q < 0.0001, isoleucine; q = 0.0035, valine; q = 0.0022). The plasma BCAA patterns after the meal significantly predicted the meal's CHO content (leucine; p < 0.0001, isoleucine; p = 0.0003, valine; p = 0.0008) along with aspartate (p < 0.0001), tyrosine (p < 0.0001), methionine (p = 0.0159) and phenylalanine (p = 0.0332). Plasma citrulline predicted best the fat content of the meal (p = 0.0024).

CONCLUSIONS

The postprandial plasma BCAA patterns are lower with increasing meal CHO content and are strong predictors of a mixed meal protein and CHO composition, as are plasma citrulline for the fat content. We hypothesize that postprandial plasma amino acid concentrations can be used to predict the meal's macronutrient composition.