Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/Non-COVID-19 Frameworks Using Artificial Intelligence Paradigm: A Narrative Review
Author
Munjral SMaindarkar M
Ahluwalia P
Puvvula A
Jamthikar A
Jujaray T
Suri N
Paul S
Pathak R
Saba L
Chalakkal RJ
Gupta S
Faa G
Singh IM
Chadha PS
Turk M
Johri AM
Khanna NN
Viskovic K
Mavrogeni S
Laird JR
Pareek G
Miner M
Sobel DW
Balestrieri A
Sfikakis PP
Tsoulfas G
Protogerou A
Misra DP
Agarwal V
Kitas GD
Kolluri R
Teji J
Al-Maini M
Dhanjil SK
Sockalingam M
Saxena A
Sharma A
Rathore V
Fatemi M
Alizad A
Viswanathan V
Krishnan PR
Omerzu T
Naidu S
Nicolaides A
Fouda MM
Suri JS.
Publication date
2022-05-28Subject
Endocrinology
Metadata
Show full item recordAbstract
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.Citation
Diagnostics (Basel). 2022 May 14;12(5):1234. doi: 10.3390/diagnostics12051234.Type
ArticlePMID
35626389Journal
DiagnosticsPublisher
MDPIae974a485f413a2113503eed53cd6c53
10.3390/diagnostics12051234