Artificial Intelligence in Assessing Cardiovascular Diseases and Risk Factors via Retinal Fundus Images: A Review of the Last Decade

Background: Cardiovascular diseases (CVDs) continue to be the leading cause of mortality on a global scale. In recent years, the application of artificial intelligence (AI) techniques, particularly deep learning (DL), has gained considerable popularity for evaluating the various aspects of CVDs. Moreover, using fundus images and optical coherence tomography angiography (OCTA) to diagnose retinal diseases has been extensively studied. To better understand heart function and anticipate changes based on microvascular characteristics and function, researchers are currently exploring the integration of AI with non-invasive retinal scanning. Leveraging AI-assisted early detection and prediction of cardiovascular diseases on a large scale holds excellent potential to mitigate cardiovascular events and alleviate the economic burden on healthcare systems. Method: A comprehensive search was conducted across various databases, including PubMed, Medline, Google Scholar, Scopus, Web of Sciences, IEEE Xplore, and ACM Digital Library, using specific keywords related to cardiovascular diseases and artificial intelligence. Results: A total of 87 English-language publications, selected for relevance were included in the study, and additional references were considered. This study presents an overview of the current advancements and challenges in employing retinal imaging and artificial intelligence to identify cardiovascular disorders and provides insights for further exploration in this field. Conclusion: Researchers aim to develop precise disease prognosis patterns as the aging population and global CVD burden increase. AI and deep learning are transforming healthcare, offering the potential for single retinal image-based diagnosis of various CVDs, albeit with the need for accelerated adoption in healthcare systems.Comment: 40 pages, 5 figures, 2 tables, 91 reference

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

DIETARY INTAKE OF CANADIANS IN ASSOCIATION WITH METABOLIC SYNDROME, DIABETES, AND RISK OF CARDIOVASCULAR DISEASE

Cardiovascular disease (CVD) is second leading cause of death in Canada. Diabetes is a major risk factor for CVD, which is affecting more than 7.5% of Canadians. Prevention is important to reducing the burden of diabetes and CVD on the individual, society and health care sector. In order to prevent these diseases, identifying people at high risk and using modifiable factors in prevention of these diseases are the priority. The metabolic syndrome (MetS), CVD risk and cardiovascular age gap (CAG) are concepts, which have been recommended by national health organizations for identifying individuals with high risk of developing these diseases. Diet has been recognized as an important modifiable factor in the prevention of metabolic disorders, diabetes and CVD. The aim of the present thesis was to determine the prevalence of diabetes, MetS components, MetS and the mean risk of 10-year atherosclerotic cardiovascular disease (ASCVD) and CAG. Further, the association between MetS, 10-year ASCVD risk and CAG and dietary patterns among Canadian adults were determined. The Canadian Health Measures Survey (CHMS) combined Cycles 1 & 2 (2007-11) data were used to address these research objectives. In CHMS, the FFQ was used to determine the usual dietary intake among Canadians. Principal component analysis method was applied to extract the dietary patterns from 32 food/food groups available from CHMS data. Controlling for potential covariates, logistic and linear regression was used to determine the association between MetS, 10-year ASCVD risk and CAG and dietary patterns. To produce nationally representative results, weighting and bootstrapping were applied. The MetS prevalence was 16.9% among a sample representative of 26,038,108 Canadians aged 12-79 years. Four prevalent dietary patterns were extracted and the “Fast food” dietary pattern with positive loadings of hotdogs, sausage/bacon, chips, fries, and diet soft drinks, had a significant association with MetS (odds ratio=1.26; 95% CI: 1.016 to 1.55; p=0.035) for older adults aged 50-79 year. The mean 10-year ASCVD risk was 6.9% for a sample representative of 13,655,671 Canadians aged 40-79y. The mean vascular age for men was 4.1 years older and for females was 0.4 years younger than their chronological age. Four dietary patterns emerged from this population of 40-79 years. Of note, the “High carbohydrate and protein” dietary pattern, which included potatoes, red meat, sausage, egg and ice-cream/frozen yoghurt, was adversely associated with 10-year ASCVD (Ptrend= 0.0128). Further, the “Healthy” and “Fast food” dietary patterns had an inverse (p<0.0001) and direct (p=0.005) association, respectively, with CAG adjusted for potential covariates. A considerable portion of Canadian adults had high relative and absolute ASCVD risk. Dietary patterns prevalent among the population that were associated with MetS, CAG and ASCVD 10-year risk were unhealthy. Thus, interventions with focus on educating Canadians, especially high-risk groups, with the aim of promoting a healthier balanced diet, along with increasing the physical activity and stop/preventing smoking, should be considered by researchers

The Convergence of Human and Artificial Intelligence on Clinical Care - Part I

This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innovative use of AI to improve our understanding of the public view; and (v) understanding of the attitude of providers in trusting insights from AI for complex cases. This collection is an excellent example of how technology can add value in healthcare settings and hints at some of the pressing challenges in the field. Artificial intelligence is gradually becoming a go-to technology in clinical care; therefore, it is important to work collaboratively and to shift from performance-driven outcomes to risk-sensitive model optimization, improved transparency, and better patient representation, to ensure more equitable healthcare for all

Identifying risk patterns for suicide attempts in individuals with diabetes : a data-driven approach using LASSO regression

Diabetes is a major health concern in the United States, with 34.2 million Americans affected in 2020. Unfortunately, the risk of suicide is also elevated in individuals with diabetes, with around 90,000 people with diabetes committing suicide each year. People with type 1 diabetes are three to four times more likely to attempt suicide, and those with newly diagnosed type 2 diabetes are twice as likely to attempt suicide compared to the general population. However, poor mental health comorbidity is still neglected, and more recommendations are needed to support for people with diabetes. It is widely acknowledged that the comorbidity of depression with diabetes is considered a higher risk factor for suicide attempts Previous studies have used logistic regression to identify risk factors for suicide attempts in individuals with diabetes. However, this technique can be prone to overfitting when the number of variables is high. To address this issue, we used the LASSO (Least Absolute Shrinkage and Selection Operator), a regularization technique, to reduce overfitting in a logistic regression model. It works by adding a penalty term ([lambda]) to the log-likelihood function, which shrinks the estimates of the coefficients. This process allows LASSO to act as a feature selection method, effectively setting coefficients that contribute most to the error to zero. Because few studies have focused on un derstanding the relationship between suicide attempts and diabetes, we used association rule mining ARM an explainable rule based machine learning technique, for knowledge discovery to reveal previously unknown relationships between suicide attempts and diabetes. This approach has already proved useful in the medical field, where it has been applied to electronic health record (EHR) data to discover associations such as disease co-occurrences, drug-disease associations, and symptomatic patterns of disease. However, no previous studies have used ARM to determine risk factors and predict suicide attempts in people with diabetes. The aim of this dissertation is to identify patterns of risk factors for suicide attempts in individuals with diabetes, with the long term goal of developing a clinical decision support system that can be integrated into EHRs. This system would allow healthcare providers to identify patients with diabetes at high risk of suicide attempts and provide appropriate preventive measures during outpatient clinic visits. To achieve this goal, we have three specific aims: (1) to identify potential risk factors for suicide attempts in individuals with diabetes through a literature review; (2) to investigate risk factors for suicide attempts in individuals with diabetes using LASSO regression; (3) to identify risk patterns for suicide attempts in individuals with diabetes using association rule mining. In this dissertation, we have reviewed the literature and compiled a list of data elements for suicide attempts in people with diabetes. We then retrieved data on patients with diabetes from Cerner Real-World Data [trade mark]. LASSO regression was used for feature selection, and ARM was used for investigating the risk patterns. We discovered risk patterns that are understandable and practical for healthcare providers. The findings of this research can inform suicide prevention efforts for people with diabetes and contribute to improved mental health outcomes.Includes bibliographical references

Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease

Background Information on electrocardiogram (ECG) has not been quantified in obstructive coronary artery disease (ObCAD), despite the deep learning (DL) algorithm being proposed as an effective diagnostic tool for acute myocardial infarction (AMI). Therefore, this study adopted a DL algorithm to suggest the screening of ObCAD from ECG. Methods ECG voltage-time traces within a week from coronary angiography (CAG) were extracted for the patients who received CAG for suspected CAD in a single tertiary hospital from 2008 to 2020. After separating the AMI group, those were classified into ObCAD and non-ObCAD groups based on the CAG results. A DL-based model adopting ResNet was built to extract information from ECG data in the patients with ObCAD relative to those with non-ObCAD, and compared the performance with AMI. Moreover, subgroup analysis was conducted using ECG patterns of computer-assisted ECG interpretation. Results The DL model demonstrated modest performance in suggesting the probability of ObCAD but excellent performance in detecting AMI. The AUC of the ObCAD model adopting 1D ResNet was 0.693 and 0.923 in detecting AMI. The accuracy, sensitivity, specificity, and F1 score of the DL model for screening ObCAD were 0.638, 0.639, 0.636, and 0.634, respectively, while the figures were up to 0.885, 0.769, 0.921, and 0.758 for detecting AMI, respectively. Subgroup analysis showed that the difference between normal and abnormal/borderline ECG groups was not notable. Conclusions ECG-based DL model showed fair performance for assessing ObCAD and it may serve as an adjunct to the pre-test probability in patients with suspected ObCAD during the initial evaluation. With further refinement and evaluation, ECG coupled with the DL algorithm may provide potential front-line screening support in the resource-intensive diagnostic pathways.The current study was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (2019M3E5D1A0206962012), the NRF funded by MIST (NRF-2022R1F1A1071574), (2022H1D8A3037396), INHA UNIVERSITY Research Grant, and Institute of Information & communications Technology Planning & Evaluation (IITP) funded by MSIT (RS-2022-00155915, Artificial Intelligence Convergence Innovation Human Resources Development (Inha University)). The funders had no role in study design, data collection, analysis, decision to publish, or manuscript preparation