Is Artificial Intelligence the Cost-Saving Lens to Diabetic Retinopathy Screening in Low- and Middle-Income Countries?

Diabetes is a rapidly growing global health crisis disproportionately affecting low- and middle-income countries (LMICs). The emergence of diabetes as a global pandemic is one of the major challenges to human health, as long-term microvascular complications such as diabetic retinopathy (DR) can lead to irreversible blindness. Leveraging artificial intelligence (AI) technology may improve the diagnostic accuracy, efficiency, and accessibility of DR screenings across LMICs. However, there is a gap between the potential of AI technology and its implementation in clinical practice. The main objective of this systematic review is to summarize the currently available literature on the health economic assessments of AI implementation for DR screening in LMICs. The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We conducted an extensive systematic search of PubMed/MEDLINE, Scopus, and the Web of Science on July 15, 2023. Our review included full-text English-language articles from any publication year. The Joanna Briggs Institute's (JBI) critical appraisal checklist for economic evaluations was used to rate the quality and rigor of the selected articles. The initial search generated 1,423 records and was narrowed to five full-text articles through comprehensive inclusion and exclusion criteria. Of the five articles included in our systematic review, two used a cost-effectiveness analysis, two used a cost-utility analysis, and one used both a cost-effectiveness analysis and a cost-utility analysis. Across the five articles, LMICs such as China, Thailand, and Brazil were represented in the economic evaluations and models. Overall, three out of the five articles concluded that AI-based DR screening was more cost-effective in comparison to standard-of-care screening methods. Our systematic review highlights the need for more primary health economic analyses that carefully evaluate the economic implications of adopting AI technology for DR screening in LMICs. We hope this systematic review will offer valuable guidance to healthcare providers, scientists, and legislators to support appropriate decision-making regarding the implementation of AI algorithms for DR screening in healthcare workflows.

Analyzing the relationships between city opioid deaths and socioeconomic factors.

BACKGROUND: The opioid crisis has been increasing in severity over the past several decades. Every year, thousands of people die due to opioid-related issues. The factors that play into the causes of this are complex. Our study aimed to see how commute times, city budget for roads, and city budget for arts and culture contribute to the city's overall opioid death rates. We know that overdose rates are more common in cities rather than rural areas, therefore making one major city per state as part of our design. METHODS: We collected data from one city per state (n = 50) and ran two ANOVA tests and 11 logistical regression tests. Both types of tests were run on IBM SPSS Statistical software version 25 at its default settings with a confidence interval set to 95 percent. Opioid deaths were the dependent variable, whereas commute time, budget for roads, and budget for arts and culture were the independent variables. RESULTS: Commute time yielded a significant result in almost all the tests it was included in: Table 1, 0.033; Table 3, 0.000; Figure 4, 0.000; Figure 5, 0.000. Budget for roads also showed significant results in most of its tests as well: Table 1, 0.003; Table 2, 0.047; Figure 3, 0.001. Budget for arts and culture showed significant results but not in a pattern that we could interpret: Table 1, 0.002; Table 2, 0.021; Table 4, 0.013. CONCLUSIONS: Commute time and the budget for roads are likely to play a role in their city's opioid crisis. Understanding where a city fits in relation to these results may better help them prepare and reduce opioid death rates.

Leveraging big data for pattern recognition of socio-demographic and climatic factors in correlation with eye disorders in Telangana State, India.

Purpose: Big data is the new gold, especially in health care. Advances in collecting and processing electronic medical records (EMR) coupled with increasing computer capabilities have resulted in an increased interest in the use of big data in health care. Ophthalmology has been an area of focus where results have shown to be promising. The objective of this study was to determine whether the EMR at a multi-tier ophthalmology network in India can contribute to the management of patient care, through studying how climatic and socio-demographic factors relate to eye disorders and visual impairment in the State of Telangana. Methods: The study was designed by merging a dataset obtained from the Telangana State Development Society to an existing EMR of approximately 1 million patients, who presented themselves with different eye symptoms and diagnosed with several diseases from the years (2011-2019). The dataset obtained included weather and climatic variables to be tested alongside eye disorders. AI creative featuring techniques have been used to narrow down the variables most affected by climatic and demographic factors, with the application of the Cynefin Framework as a guide to simplify and structure the dataset for analysis. Results: Our findings revealed a high presence of cataract in the state of Telangana, mostly in rural areas and throughout the different weather seasons in India. Males tend to be the most affected as per the number of visits to the clinic, while home makers make the most visit to the hospital, in addition to employees, students, and laborers. While cataract is most dominant in the older age population, diseases such as astigmatism, conjunctivitis, and emmetropia, are more present in the younger age population. Conclusion: The study appeared useful for taking preventive measures in the future to manage the treatment of patients who present themselves with eye disorders in Telangana. The use of clinical big datasets helps to identify the burden of ocular disorders in the population. The overlaying of meteorological data on the clinical presentation of patients from a geographic region lends insight into the complex interaction of environmental factors on the prevalence of ocular disorders in them.

Soft computing in the design of nontoxic chemicals.

This research focuses on the use of soft computing to aid in the development of novel, state-of-the-art, nontoxic dyes which are of commercial importance to the U.S. textile industry. Where appropriate, modern molecular orbital (MO) and density functional (DF) techniques are employed to establish the necessary databases of molecular properties to be used in conjunction with the neural network approach. In this research, we focused on the following: (1) using molecular modeling to establish databases of various molecular properties of azo dyes required as input for our neural network approach; (2) designing and implementing a neural network architecture suitable to process these databases; and (3) investigating combinations of molecular descriptors needed to predict various properties of the azo dyes.