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

Smiksha Munjral; Mahesh Maindarkar; Puneet Ahluwalia; Anudeep Puvvula; Ankush Jamthikar; Tanay Jujaray; Neha Suri; Sudip Paul; Rajesh Pathak; Luca Saba; Renoh Johnson Chalakkal; Suneet Gupta; Gavino Faa; Inder M. Singh; Paramjit S. Chadha; Monika Turk; Amer M. Johri; Narendra N. Khanna; Klaudija Viskovic; Sophie Mavrogeni; John R. Laird; Gyan Pareek; Martin Miner; David W. Sobel; Antonella Balestrieri; Petros P. Sfikakis; George Tsoulfas; Athanasios Protogerou; Durga Prasanna Misra; Vikas Agarwal; George D. Kitas; Raghu Kolluri; Jagjit Teji; Mustafa Al-Maini; Surinder K. Dhanjil; Meyypan Sockalingam; Ajit Saxena; Aditya Sharma; Vijay Rathore; Mostafa Fatemi; Azra Alizad; Vijay Viswanathan; Padukode R. Krishnan; Tomaz Omerzu; Subbaram Naidu; Andrew Nicolaides; Mostafa M. Fouda; Jasjit S. Suri

doi:10.3390/diagnostics12051234

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

Smiksha Munjral, Mahesh Maindarkar, Puneet Ahluwalia, Anudeep Puvvula, Ankush Jamthikar, Tanay Jujaray, Neha Suri, Sudip Paul, Rajesh Pathak, Luca Saba, Renoh Johnson Chalakkal, Suneet Gupta, Gavino Faa, Inder M. Singh, Paramjit S. Chadha, Monika Turk, Amer M. Johri, Narendra N. Khanna, Klaudija Viskovic, Sophie MavrogeniJohn R. Laird, Gyan Pareek, Martin Miner, David W. Sobel, Antonella Balestrieri, Petros P. Sfikakis, George Tsoulfas, Athanasios Protogerou, Durga Prasanna Misra, Vikas Agarwal, George D. Kitas, Raghu Kolluri, Jagjit Teji, Mustafa Al-Maini, Surinder K. Dhanjil, Meyypan Sockalingam, Ajit Saxena, Aditya Sharma, Vijay Rathore, Mostafa Fatemi, Azra Alizad, Vijay Viswanathan, Padukode R. Krishnan, Tomaz Omerzu, Subbaram Naidu, Andrew Nicolaides, Mostafa M. Fouda, Jasjit S. Suri

Physiology & Biomedical Engineering

Research output: Contribution to journal › Review article › peer-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 lowincome 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, lowcost, 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.

Original language	English (US)
Article number	1234
Journal	Diagnostics
Volume	12
Issue number	5
DOIs	https://doi.org/10.3390/diagnostics12051234
State	Published - May 2022

Keywords

artificial intelligence
atherosclerosis
cardiovascular disease
diabetic retinopathy
risk assessment
risk stratification
surrogate biomarkers

ASJC Scopus subject areas

Clinical Biochemistry

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10.3390/diagnostics12051234

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Munjral, S., Maindarkar, M., Ahluwalia, P., Puvvula, A., Jamthikar, A., Jujaray, T., Suri, N., Paul, S., Pathak, R., Saba, L., Chalakkal, R. J., Gupta, S., Faa, G., Singh, I. M., Chadha, P. S., Turk, M., Johri, A. M., Khanna, N. N., Viskovic, K., ... Suri, J. S. (2022). Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/non-COVID-19 Frameworks using Artificial Intelligence Paradigm: A Narrative Review. Diagnostics, 12(5), Article 1234. https://doi.org/10.3390/diagnostics12051234

Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/non-COVID-19 Frameworks using Artificial Intelligence Paradigm: A Narrative Review. / Munjral, Smiksha; Maindarkar, Mahesh; Ahluwalia, Puneet et al.
In: Diagnostics, Vol. 12, No. 5, 1234, 05.2022.

Research output: Contribution to journal › Review article › peer-review

Munjral, S, Maindarkar, 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 2022, 'Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/non-COVID-19 Frameworks using Artificial Intelligence Paradigm: A Narrative Review', Diagnostics, vol. 12, no. 5, 1234. https://doi.org/10.3390/diagnostics12051234

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title = "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 lowincome 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, lowcost, 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.",

keywords = "artificial intelligence, atherosclerosis, cardiovascular disease, diabetic retinopathy, risk assessment, risk stratification, surrogate biomarkers",

author = "Smiksha Munjral and Mahesh Maindarkar and Puneet Ahluwalia and Anudeep Puvvula and Ankush Jamthikar and Tanay Jujaray and Neha Suri and Sudip Paul and Rajesh Pathak and Luca Saba and Chalakkal, {Renoh Johnson} and Suneet Gupta and Gavino Faa and Singh, {Inder M.} and Chadha, {Paramjit S.} and Monika Turk and Johri, {Amer M.} and Khanna, {Narendra N.} and Klaudija Viskovic and Sophie Mavrogeni and Laird, {John R.} and Gyan Pareek and Martin Miner and Sobel, {David W.} and Antonella Balestrieri and Sfikakis, {Petros P.} and George Tsoulfas and Athanasios Protogerou and Misra, {Durga Prasanna} and Vikas Agarwal and Kitas, {George D.} and Raghu Kolluri and Jagjit Teji and Mustafa Al-Maini and Dhanjil, {Surinder K.} and Meyypan Sockalingam and Ajit Saxena and Aditya Sharma and Vijay Rathore and Mostafa Fatemi and Azra Alizad and Vijay Viswanathan and Krishnan, {Padukode R.} and Tomaz Omerzu and Subbaram Naidu and Andrew Nicolaides and Fouda, {Mostafa M.} and Suri, {Jasjit S.}",

note = "Publisher Copyright: {\textcopyright} 2022 by the authors. Licensee MDPI, Basel, Switzerland.",

year = "2022",

month = may,

doi = "10.3390/diagnostics12051234",

language = "English (US)",

volume = "12",

journal = "Diagnostics",

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T2 - A Narrative Review

AU - Munjral, Smiksha

AU - Maindarkar, Mahesh

AU - Ahluwalia, Puneet

AU - Puvvula, Anudeep

AU - Jamthikar, Ankush

AU - Jujaray, Tanay

AU - Suri, Neha

AU - Paul, Sudip

AU - Pathak, Rajesh

AU - Saba, Luca

AU - Chalakkal, Renoh Johnson

AU - Gupta, Suneet

AU - Faa, Gavino

AU - Singh, Inder M.

AU - Chadha, Paramjit S.

AU - Turk, Monika

AU - Johri, Amer M.

AU - Khanna, Narendra N.

AU - Viskovic, Klaudija

AU - Mavrogeni, Sophie

AU - Laird, John R.

AU - Pareek, Gyan

AU - Miner, Martin

AU - Sobel, David W.

AU - Balestrieri, Antonella

AU - Sfikakis, Petros P.

AU - Tsoulfas, George

AU - Protogerou, Athanasios

AU - Misra, Durga Prasanna

AU - Agarwal, Vikas

AU - Kitas, George D.

AU - Kolluri, Raghu

AU - Teji, Jagjit

AU - Al-Maini, Mustafa

AU - Dhanjil, Surinder K.

AU - Sockalingam, Meyypan

AU - Saxena, Ajit

AU - Sharma, Aditya

AU - Rathore, Vijay

AU - Fatemi, Mostafa

AU - Alizad, Azra

AU - Viswanathan, Vijay

AU - Krishnan, Padukode R.

AU - Omerzu, Tomaz

AU - Naidu, Subbaram

AU - Nicolaides, Andrew

AU - Fouda, Mostafa M.

AU - Suri, Jasjit S.

PY - 2022/5

Y1 - 2022/5

N2 - 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 lowincome 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, lowcost, 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.

AB - 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 lowincome 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, lowcost, 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.

KW - artificial intelligence

KW - atherosclerosis

KW - cardiovascular disease

KW - diabetic retinopathy

KW - risk assessment

KW - risk stratification

KW - surrogate biomarkers

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

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