Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/Non-COVID-19 Frameworks Using Artificial Intelligence Paradigm: A Narrative Review

Abstract

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for low-income countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, low-cost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework.

Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity

“…We considered various papers related to RS based on image similarity in our benchmarking strategy. This included Ullah et al [ 17 ], Chen et al [ 18 ], Tuinhof et al [ 19 ], and Geng et al [ 40 ]. In ref.…”

“…Ref. [ 18 ] provided a neural network-based framework for product selection based on a specific input query image. A neural network was used in the proposed system to classify the supplied input query image, followed by another neural network that used the Jaccard similarity measure to determine the most comparable product image to that input image.…”

“…However, hospitals’ capability to provide adequate treatments to hospitalized patients is sometimes limited by the availability of doctors and resources. In this scenario, a recommender system (RS) using machine learning (ML) approaches might be used to administer the best treatment while working with limited resources [ 17 , 18 ]. As the mortality rate and recovery rate of seriously hospitalized COVID-19 patients generally depend upon the amount of infection in the lungs [ 19 , 20 , 21 ], the radiographic lung images of those patients can be used to recommend proper treatment in terms of a doctor, medicine, and other related resources.…”

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

“…( b ) Imaging device where the carotid artery is being scanned with the linear ultrasound probe. The middle panel shows the B-mode carotid longitudinal US scan and IVUS-based artery cross-sectional scan [ 250 ].…”

Diabetic Retinopathy May Covariate With Stroke in Diabetes Mellitus

“…Retinoscopy is a macrovascular dysfunction due to endothelial dysfunction, which results in leakage from the artery wall. It causes lipid accumulation, thereby leading to an episode of atherosclerosis [20]. Diabetic patients with DR have more symptoms and increased cardiovascular dysfunction, including a 70% risk of thromboembolic stroke events [21].…”