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

Worsening renal function in hospitalized patients with systolic heart failure: prevalence and risk factors

Abstract Aims Heart failure (HF) is usually associated with complications of other organs. Renal impairment is seen in a significant proportion of HF patients and is characterized by worsening renal function (WRF). WRF can be used for predicting symptom exacerbation in systolic HF. This study aimed to determine the prevalence and risk factors of WRF among hospitalized patients with systolic HF. Methods and results In this cross‐sectional study, data from medical records of 347 hospitalized patients diagnosed with HFrEF from 2019 to 2020, admitted to Tabriz Shahid Madani Heart Hospital, who met the predefined inclusion criteria, were retrieved. Patients were divided into two groups based on the in‐hospital occurrence of WRF. Laboratory tests and para‐clinical findings were collected and analysed using SPSS Version 20.0. Statistical significance was accepted at a P value of <0.05. In this study, 347 hospitalized patients with HFrEF were included. The mean (standard deviation) age was 62.34 (±18.87) years. The mean (SD) length of stay was 6.34 (±4) days. According to our findings, 117 patients (33.71%) had WRF. Following multivariate analysis of potential predictors of WRF occurrence, hyponatremia, haemoglobin concentration, white blood cell count and prior diuretic use were found to be independent predictors for WRF occurrence in patients with systolic heart failure. Conclusions This study revealed that in patients with WRF, mortality rate and length of stay were significantly greater than those of patients without WRF. Initial clinical characteristics of HF patients who developed WRF can help physicians identify patients with a higher risk of WRF