Study Protocol for the Effects of Artificial Intelligence (AI)-Supported Automated Nutritional Intervention on Glycemic Control in Patients with Type 2 Diabetes Mellitus

Visual fatigue a comprehensive review of mechanisms of occurrence, animal model design and nutritional intervention strategies

When the eyes work intensively, it is easy to have eye discomfort such as blurred vision, soreness, dryness, and tearing, that is, visual fatigue. Visual fatigue not only affects work and study efficiency, but long-term visual fatigue can also easily affect physical and mental health. In recent years, with the popularization of electronic products, although it has brought convenience to the office and study, it has also caused more frequent visual fatigue among people who use electronic devices. Moreover, studies have reported that the number of people with visual fatigue is showing a trend of increasing year by year. The range of people involved is also extensive, especially students, people who have been engaged in computer work and fine instruments (such as microscopes) for a long time, and older adults with aging eye function. More and more studies have proposed that supplementation with the proper nutrients can effectively relieve visual fatigue and promote eye health. This review discusses the physiological mechanisms of visual fatigue and the design ideas of animal experiments from the perspective of modern nutritional science. Functional food ingredients with the ability to alleviate visual fatigue are discussed in detail.

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.

Dietary counseling based on artificial intelligence for patients with nonalcoholic fatty liver disease

BACKGROUND

About 25% of the general population in Japan are reported to have nonalcoholic fatty liver disease (NAFLD). NAFLD and nonalcoholic steatohepatitis carry a risk of progressing further to hepatocellular carcinoma. The primary treatment for NAFLD is dietary therapy. Dietary counseling plays an essential role in dietary therapy. Although artificial intelligence (AI)-based nutrition management software applications have been developed and put into practical use in recent years, the majority focus on weight loss or muscle strengthening, and no software has been developed for patient use in clinical practice.

AIM

To examine whether effective dietary counseling is possible using AI-based nutrition management software.

METHODS

NAFLD patients who had been assessed using an AI-based nutrition management software application (Calomeal) that automatically analyzed images of meals photographed by patients and agreed to receive dietary counseling were given dietary counseling. Blood biochemistry tests were performed before (baseline) and 6 mo after (6M follow-up) dietary counseling. After the dietary counseling, the patients were asked to complete a questionnaire survey.

RESULTS

A total of 29 patients diagnosed with NAFLD between August 2020 and March 2022 were included. There were significant decreases in liver enzyme and triglyceride levels at the 6M follow-up compared to baseline. The food analysis capability of the AI used by Calomeal in this study was 75.1%. Patient satisfaction with the AI-based dietary counselling was high.

CONCLUSION

AI-based nutrition management appeared to raise awareness of dietary habits among NAFLD patients. However, it did not directly alleviate the burden of registered dietitians, and improvements are much anticipated.

Development and validation pathways of artificial intelligence tools evaluated in randomised clinical trials

Objective

Given the complexities of testing the translational capability of new artificial intelligence (AI) tools, we aimed to map the pathways of training/validation/testing in development process and external validation of AI tools evaluated in dedicated randomised controlled trials (AI-RCTs).

Methods

We searched for peer-reviewed protocols and completed AI-RCTs evaluating the clinical effectiveness of AI tools and identified development and validation studies of AI tools. We collected detailed information, and evaluated patterns of development and external validation of AI tools.

Results

We found 23 AI-RCTs evaluating the clinical impact of 18 unique AI tools (2009–2021). Standard-of-care interventions were used in the control arms in all but one AI-RCT. Investigators did not provide access to the software code of the AI tool in any of the studies. Considering the primary outcome, the results were in favour of the AI intervention in 82% of the completed AI-RCTs (14 out of 17). We identified significant variation in the patterns of development, external validation and clinical evaluation approaches among different AI tools. A published development study was found only for 10 of the 18 AI tools. Median time from the publication of a development study to the respective AI-RCT was 1.4 years (IQR 0.2–2.2).

Conclusions

We found significant variation in the patterns of development and validation for AI tools before their evaluation in dedicated AI-RCTs. Published peer-reviewed protocols and completed AI-RCTs were also heterogeneous in design and reporting. Upcoming guidelines providing guidance for the development and clinical translation process aim to improve these aspects.

Artificial intelligence and diabetes technology: A review

Artificial intelligence (AI) is widely discussed in the popular literature and is portrayed as impacting many aspects of human life, both in and out of the workplace. The potential for revolutionizing healthcare is significant because of the availability of increasingly powerful computational platforms and methods, along with increasingly informative sources of patient data, both in and out of clinical settings. This review aims to provide a realistic assessment of the potential for AI in understanding and managing diabetes, accounting for the state of the art in the methodology and medical devices that collect data, process data, and act accordingly. Acknowledging that many conflicting definitions of AI have been put forth, this article attempts to characterize the main elements of the field as they relate to diabetes, identifying the main perspectives and methods that can (i) affect basic understanding of the disease, (ii) affect understanding of risk factors (genetic, clinical, and behavioral) of diabetes development, (iii) improve diagnosis, (iv) improve understanding of the arc of disease (progression and personal/societal impact), and finally (v) improve treatment.

Artificial Intelligence in Nutrients Science Research: A Review

Artificial intelligence (AI) as a branch of computer science, the purpose of which is to imitate thought processes, learning abilities and knowledge management, finds more and more applications in experimental and clinical medicine. In recent decades, there has been an expansion of AI applications in biomedical sciences. The possibilities of artificial intelligence in the field of medical diagnostics, risk prediction and support of therapeutic techniques are growing rapidly. The aim of the article is to analyze the current use of AI in nutrients science research. The literature review was conducted in PubMed. A total of 399 records published between 1987 and 2020 were obtained, of which, after analyzing the titles and abstracts, 261 were rejected. In the next stages, the remaining records were analyzed using the full-text versions and, finally, 55 papers were selected. These papers were divided into three areas: AI in biomedical nutrients research (20 studies), AI in clinical nutrients research (22 studies) and AI in nutritional epidemiology (13 studies). It was found that the artificial neural network (ANN) methodology was dominant in the group of research on food composition study and production of nutrients. However, machine learning (ML) algorithms were widely used in studies on the influence of nutrients on the functioning of the human body in health and disease and in studies on the gut microbiota. Deep learning (DL) algorithms prevailed in a group of research works on clinical nutrients intake. The development of dietary systems using AI technology may lead to the creation of a global network that will be able to both actively support and monitor the personalized supply of nutrients.

Application of Artificial Intelligence in Diabetes Education and Management: Present Status and Promising Prospect

Despite the rapid development of science and technology in healthcare, diabetes remains an incurable lifelong illness. Diabetes education aiming to improve the self-management skills is an essential way to help patients enhance their metabolic control and quality of life. Artificial intelligence (AI) technologies have made significant progress in transforming available genetic data and clinical information into valuable knowledge. The application of AI tech in disease education would be extremely beneficial considering their advantages in promoting individualization and full-course education intervention according to the unique pictures of different individuals. This paper reviews and discusses the most recent applications of AI techniques to various aspects of diabetes education. With the information and evidence collected, this review attempts to provide insight and guidance for the development of prospective, data-driven decision support platforms for diabetes management, with a focus on individualized patient management and lifelong educational interventions.

Feasibility and Acceptability of a Web-Based Dietary Intervention with Text Messages for Colorectal Cancer: A Randomized Pilot Trial

BACKGROUND

Diet is associated with colorectal cancer survival. Yet, adherence to nutrition guidelines is low among colorectal cancer survivors.

METHODS

We conducted a pilot trial among colorectal cancer survivors to evaluate a 12-week remote dietary intervention. Participants received print materials and were randomized (1:1) to intervention (website, text messages) or wait-list control. Primary outcomes included feasibility and acceptability. We also explored change in diet from 0 to 12 and 24 weeks and change from 0 to 12 weeks in anthropometry and circulating biomarkers (Trial Registration: NCT02965521).

RESULTS

We randomized 50 colorectal cancer survivors (25 intervention, 25 control). Retention was 90% at 12 weeks and 84% at 24 weeks. Participants had a median age of 55 years and were 66% female, 70% non-Hispanic white, and 96% had a college degree. The intervention arm responded to a median 15 (71%) of 21 text messages that asked for a reply [interquartile range (IQR) = 8, 19] and visited the website a median of 13 (15%) days (IQR = 1, 33) of the 84 study days.

CONCLUSIONS

We developed a Web-based dietary intervention for colorectal cancer survivors. Our pilot results suggest that colorectal cancer survivors may engage more with text messages than a study website. Research to improve tailoring of text messages, while maintaining scalability, is needed.

IMPACT

Remote dietary interventions using text messages may be feasible for colorectal cancer survivors.See all articles in this CEBP Focus section, "Modernizing Population Science."