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

Prevalence of celiac disease in Iranian patients with type 1 diabetes: A systematic review and meta-analysis

Patients with type 1 diabetes mellitus (T1DM) are at high risk for celiac disease (CD) due to the common genetic background and interaction between environmental and immunological factors. The purpose of this systematic review and meta-analysis was to estimate the prevalence of CD among Iranian patients with type 1 diabetes. The search for articles was conducted using the following keywords: ``celiac disease,'' ``celiac,'' ``coeliac disease,'' ``diabetes,'' ``Iran,'' and all other possible combinations of these terms. The following databases were searched from inception to June 2019: Scientific Information Database (SID), MagIran, Web of Science, PubMed, and Scopus. Meta-analysis was performed using the random-effects models, and the heterogeneity of results across the studies was assessed using the Cochran's Q test and quantified by the I-2 statistic. Data analysis was performed by Stata version 14. A total of 14 papers were included in the meta-analysis, involving 2030 Iranian patients with T1DM. The pooled prevalence of CD in patients with T1DM was 5% (95% CI 3-7). The prevalence of CD in Tehran (4%; 95% CI 1-6) was lower than in other provinces of the country (6%; 95% CI 4-8). Meta-regression analysis showed that, with increasing sample size, the prevalence of CD was significantly reduced (p = 0.018).Given the adverse effects of CD , such as osteoporosis and malignancy (especially lymphoma), patients with T1DM must be screened for CD