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

Tertiary referral hospital experience of methanol poisoning in the COVID-19 era: a cross-sectional study in Northwestern Iran

Abstract Background Methanol poisoning is a worldwide phenomenon that has resulted in deaths and irreversible complications. However, studies show it is more prevalent in developing countries and areas with lower socioeconomic status. So, accurately recognizing socio-economic risk factors, increasing people's awareness about methanol poisoning, and proper hospital management will reduce its dangerous complications and mortality. Methods This cross-sectional study was conducted retrospectively at Sina Hospital, a poisoning center and tertiary referral center in northwestern Iran, and investigated demographic findings and hospital management indicators through systematic random sampling between February 20, 2020, and September 22, 2022 (or the COVID-19 era). We assessed variable correlations using Spearman's correlation coefficient, Mann–Whitney U, and Kruskall–Wallis. Results Out of 131 patients, 126 (96.2%) were males, and 5 (3.8%) were females. 45.5% and 30.3% of poisoning incidents occurred between the winter and spring, respectively. 67 patients (50.8%) were referred to this hospital due to vision complaints. Unfortunately, 10 patients (7.6%) passed away despite receiving care. Employed individuals were referred to the treatment facility more quickly than unemployed individuals (P-value = 0.01). Patients with medical insurance coverage were referred faster after consuming alcohol (P-value = 0.039). Older patients referred to the hospital later. (P-value = 0.006). Conclusions Mortality and morbidity following methanol poisoning are likely to be affected by factors including access to medical care, financial stability, and employment status. Consequently, reducing mortality and morbidity requires attention to these concerns