{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:41:07Z","timestamp":1783010467003,"version":"3.54.6"},"reference-count":274,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T00:00:00Z","timestamp":1652486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/diagnostics12051234","type":"journal-article","created":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T09:48:22Z","timestamp":1652608102000},"page":"1234","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19\/Non-COVID-19 Frameworks Using Artificial Intelligence Paradigm: A Narrative Review"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1941-8357","authenticated-orcid":false,"given":"Smiksha","family":"Munjral","sequence":"first","affiliation":[{"name":"Stroke Monitoring and Diagnostic Division, AtheroPoint\u2122, Roseville, CA 95661, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahesh","family":"Maindarkar","sequence":"additional","affiliation":[{"name":"Stroke Monitoring and Diagnostic Division, AtheroPoint\u2122, Roseville, CA 95661, USA"},{"name":"Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Puneet","family":"Ahluwalia","sequence":"additional","affiliation":[{"name":"Max Institute of Cancer Care, Max Super Specialty Hospital, New Delhi 110017, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anudeep","family":"Puvvula","sequence":"additional","affiliation":[{"name":"Stroke Monitoring and Diagnostic Division, AtheroPoint\u2122, Roseville, CA 95661, USA"},{"name":"Annu\u2019s Hospitals for Skin and Diabetes, Nellore 524101, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ankush","family":"Jamthikar","sequence":"additional","affiliation":[{"name":"Stroke Monitoring and Diagnostic Division, AtheroPoint\u2122, Roseville, CA 95661, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tanay","family":"Jujaray","sequence":"additional","affiliation":[{"name":"Stroke Monitoring and Diagnostic Division, AtheroPoint\u2122, Roseville, CA 95661, USA"},{"name":"Department of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, CA 95616, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neha","family":"Suri","sequence":"additional","affiliation":[{"name":"Mira Loma High School, Sacramento, CA 95821, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9856-539X","authenticated-orcid":false,"given":"Sudip","family":"Paul","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajesh","family":"Pathak","sequence":"additional","affiliation":[{"name":"Department of Computer Science Engineering, Rawatpura Sarkar University, Raipur 492015, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Saba","sequence":"additional","affiliation":[{"name":"Department of Radiology, Azienda Ospedaliero Universitaria, 40138 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3452-5633","authenticated-orcid":false,"given":"Renoh Johnson","family":"Chalakkal","sequence":"additional","affiliation":[{"name":"oDocs Eye Care Research Laboratory, Dunedin 9013, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suneet","family":"Gupta","sequence":"additional","affiliation":[{"name":"CSE Department, Bennett University, Greater Noida 201310, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0189-8612","authenticated-orcid":false,"given":"Gavino","family":"Faa","sequence":"additional","affiliation":[{"name":"Department of Pathology, Azienda Ospedaliero Universitaria, 09124 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Inder M.","family":"Singh","sequence":"additional","affiliation":[{"name":"Stroke Monitoring and Diagnostic Division, AtheroPoint\u2122, Roseville, CA 95661, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paramjit S.","family":"Chadha","sequence":"additional","affiliation":[{"name":"Stroke Monitoring and Diagnostic Division, AtheroPoint\u2122, Roseville, CA 95661, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monika","family":"Turk","sequence":"additional","affiliation":[{"name":"The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amer M.","family":"Johri","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Cardiology, Queen\u2019s University, Kingston, ON K7L 3N6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Narendra N.","family":"Khanna","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5927-3201","authenticated-orcid":false,"given":"Klaudija","family":"Viskovic","sequence":"additional","affiliation":[{"name":"Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10 000 Zagreb, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophie","family":"Mavrogeni","sequence":"additional","affiliation":[{"name":"Cardiology Clinic, Onassis Cardiac Surgery Centre, 17674 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John R.","family":"Laird","sequence":"additional","affiliation":[{"name":"Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gyan","family":"Pareek","sequence":"additional","affiliation":[{"name":"Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Miner","sequence":"additional","affiliation":[{"name":"Men\u2019s Health Centre, Miriam Hospital Providence, Providence, RI 02906, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0448-4765","authenticated-orcid":false,"given":"David W.","family":"Sobel","sequence":"additional","affiliation":[{"name":"Rheumatology Unit, National Kapodistrian University of Athens, 15772 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonella","family":"Balestrieri","sequence":"additional","affiliation":[{"name":"Department of Radiology, Azienda Ospedaliero Universitaria, 40138 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Petros P.","family":"Sfikakis","sequence":"additional","affiliation":[{"name":"Rheumatology Unit, National Kapodistrian University of Athens, 15772 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5043-7962","authenticated-orcid":false,"given":"George","family":"Tsoulfas","sequence":"additional","affiliation":[{"name":"Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3825-532X","authenticated-orcid":false,"given":"Athanasios","family":"Protogerou","sequence":"additional","affiliation":[{"name":"Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Durga Prasanna","family":"Misra","sequence":"additional","affiliation":[{"name":"Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4508-1233","authenticated-orcid":false,"given":"Vikas","family":"Agarwal","sequence":"additional","affiliation":[{"name":"Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, 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Lurie Children\u2019s Hospital of Chicago, Chicago, IL 60611, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mustafa","family":"Al-Maini","sequence":"additional","affiliation":[{"name":"Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Surinder K.","family":"Dhanjil","sequence":"additional","affiliation":[{"name":"Stroke Monitoring and Diagnostic Division, AtheroPoint\u2122, Roseville, CA 95661, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meyypan","family":"Sockalingam","sequence":"additional","affiliation":[{"name":"MV Centre of Diabetes, Chennai 600013, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ajit","family":"Saxena","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aditya","family":"Sharma","sequence":"additional","affiliation":[{"name":"Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22904, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijay","family":"Rathore","sequence":"additional","affiliation":[{"name":"Nephrology Department, Kaiser Permanente, Sacramento, CA 95119, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6603-9077","authenticated-orcid":false,"given":"Mostafa","family":"Fatemi","sequence":"additional","affiliation":[{"name":"Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Azra","family":"Alizad","sequence":"additional","affiliation":[{"name":"Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijay","family":"Viswanathan","sequence":"additional","affiliation":[{"name":"MV Hospital for Diabetes and Professor MVD Research Centre, Chennai 600013, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Padukode R.","family":"Krishnan","sequence":"additional","affiliation":[{"name":"Neurology Department, Fortis Hospital, Bangalore 560076, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomaz","family":"Omerzu","sequence":"additional","affiliation":[{"name":"Department of Neurology, University Medical Centre Maribor, 1262 Maribor, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8544-8397","authenticated-orcid":false,"given":"Subbaram","family":"Naidu","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Nicolaides","sequence":"additional","affiliation":[{"name":"Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Nicosia 2408, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1790-8640","authenticated-orcid":false,"given":"Mostafa M.","family":"Fouda","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jasjit S.","family":"Suri","sequence":"additional","affiliation":[{"name":"Stroke Monitoring and Diagnostic Division, AtheroPoint\u2122, Roseville, CA 95661, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,14]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2022, February 22). Cardiovascular Diseases. Available online: https:\/\/www.who.int\/health-topics\/cardiovascular-diseases\/#tab=tab_1."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1186\/1741-7015-11-117","article-title":"Immunity, atherosclerosis and cardiovascular disease","volume":"11","year":"2013","journal-title":"BMC Med."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Suri, J.S., Kathuria, C., and Molinari, F. (2010). Atherosclerosis Disease Management, Springer Science & Business Media.","DOI":"10.1007\/978-1-4419-7222-4"},{"key":"ref_4","unstructured":"(2022, February 26). Diabetes. Available online: http:\/\/www.who.int\/news-room\/fact-sheets\/detail\/diabetes."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1097\/01.med.0000433056.76699.5d","article-title":"Lifestyle medicine: The future of chronic disease management","volume":"20","author":"Kushner","year":"2013","journal-title":"J. Curr. Opin. Endocrinol. Diabetes Obes."},{"key":"ref_6","unstructured":"(2022, February 28). National Diabetes Statistics Report, Available online: https:\/\/www.cdc.gov\/diabetes\/data\/statistics-report\/index.html."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.ajo.2010.10.014","article-title":"Diabetic Retinopathy in the Developing World: How to Approach Identifying and Treating Underserved Populations","volume":"151","author":"Friedman","year":"2011","journal-title":"Am. J. Ophthalmol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1161\/CIRCRESAHA.118.311371","article-title":"Heart failure in type 2 diabetes mellitus: Impact of glucose-lowering agents, heart failure therapies, and novel therapeutic strategies","volume":"124","author":"Kenny","year":"2019","journal-title":"Circ. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"215","DOI":"10.4239\/wjd.v12.i3.215","article-title":"Bidirectional link between diabetes mellitus and coronavirus disease 2019 leading to cardiovascular disease: A narrative review","volume":"12","author":"Viswanathan","year":"2021","journal-title":"World J. Diabetes"},{"key":"ref_10","unstructured":"PAHO (2022, January 03). About Diabetes. Available online: https:\/\/www3.paho.org\/hq\/index.php?option=com_content&view=article&id=6717:2012-about-diabetes&Itemid=39447&lang=en."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"556","DOI":"10.2337\/dc11-1909","article-title":"Global Prevalence and Major Risk Factors of Diabetic Retinopathy","volume":"35","author":"Yau","year":"2012","journal-title":"Diabetes Care"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"87","DOI":"10.24095\/hpcdp.35.6.01","article-title":"Prevalence and patterns of chronic disease multimorbidity and associated determinants in Canada","volume":"35","author":"Roberts","year":"2015","journal-title":"Health Promot. Chronic Dis. Prev. Can."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"850","DOI":"10.4239\/wjd.v6.i6.850","article-title":"Diabetes mellitus: The epidemic of the century","volume":"6","author":"Kharroubi","year":"2015","journal-title":"World J. Diabetes"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"27","DOI":"10.7257\/1053-816X.2016.36.1.27","article-title":"Diabetes Mellitus Review","volume":"36","author":"Blair","year":"2016","journal-title":"Urol. Nurs."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101040","DOI":"10.1016\/j.arr.2020.101040","article-title":"Perivascular adipose tissue in age-related vascular disease","volume":"59","author":"Queiroz","year":"2020","journal-title":"Ageing Res. Rev."},{"key":"ref_16","unstructured":"Mahabadi, N., and al Khalili, Y. (2020). Neuroanatomy, Retina, StatPearls."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, W., and Lo, A.C.Y. (2018). Diabetic Retinopathy: Pathophysiology and Treatments. Int. J. Mol. Sci., 19.","DOI":"10.3390\/ijms19061816"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.preteyeres.2015.08.001","article-title":"The progress in understanding and treatment of diabetic retinopathy","volume":"51","author":"Stitt","year":"2016","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"46","DOI":"10.2337\/diab.26.1.46","article-title":"Cigarette smoking and diabetic retinopathy","volume":"26","author":"Paetkau","year":"1977","journal-title":"Diabetes"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1900\/RDS.2007.4.242","article-title":"The Incidence of Microalbuminuria and its Associated Risk Factors in Type 2 Diabetic Patients in Isfahan, Iran","volume":"4","author":"Amini","year":"2007","journal-title":"Rev. Diabet. Stud."},{"key":"ref_21","first-page":"589","article-title":"Associations between diabetic retinopathy and systemic risk factors","volume":"22","author":"Wat","year":"2016","journal-title":"Hong Kong Med. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.diabres.2010.11.005","article-title":"Diabetic retinopathy is associated with subclinical atherosclerosis in newly diagnosed type 2 diabetes mellitus","volume":"91","author":"Son","year":"2011","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"689","DOI":"10.3389\/fendo.2019.00689","article-title":"Diabetic Retinopathy May Indicate an Increased Risk of Cardiovascular Disease in Patients with Type 1 Diabetes\u2014A Nested Case-Control Study in Brazil","volume":"10","author":"Melo","year":"2019","journal-title":"Front. Endocrinol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1159\/000486264","article-title":"Diabetic Retinopathy and Heart Disease","volume":"Volume 27","author":"Yun","year":"2019","journal-title":"Diabetic Retinopathy and Cardiovascular Disease"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4343","DOI":"10.1210\/jc.2017-01922","article-title":"Diabetic Microvascular Disease: An Endocrine Society Scientific Statement","volume":"102","author":"Barrett","year":"2017","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1159\/000486269","article-title":"Novel Retinal Imaging in Assessment of Cardiovascular Risk Factors and Systemic Vascular Diseases","volume":"Volume 27","author":"Ting","year":"2019","journal-title":"Diabetic Retinopathy and Cardiovascular Disease"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13098-021-00666-z","article-title":"Proliferative diabetic retinopathy in patients with type 2 diabetes correlates with the presence of atherosclerosis cardiovascular disease","volume":"13","author":"Gao","year":"2021","journal-title":"Diabetol. Metab. Syndr."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e12702","DOI":"10.1111\/micc.12702","article-title":"Carotid stiffness is associated with retinal microvascular dysfunction\u2014The Maastricht study","volume":"28","author":"Zhou","year":"2021","journal-title":"Microcirculation"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.2337\/dc20-3104","article-title":"Associations of Microvascular Complications With the Risk of Cardiovascular Disease in Type 1 Diabetes","volume":"44","author":"Gao","year":"2021","journal-title":"Diabetes Care"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1016\/j.ophtha.2020.12.019","article-title":"Severity of diabetic retinopathy and the risk of future cerebrovascular disease, cardiovascular disease, and all-cause mortality","volume":"128","author":"Modjtahedi","year":"2021","journal-title":"Ophthalmology"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1016\/j.freeradbiomed.2011.05.004","article-title":"Reactive oxygen species in cardiovascular disease","volume":"51","author":"Sugamura","year":"2011","journal-title":"Free Radic. Biol. Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1161\/CIRCULATIONAHA.105.535294","article-title":"Role of angiogenesis in cardiovascular disease: A critical appraisal","volume":"112","author":"Khurana","year":"2005","journal-title":"J. Circ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chalakkal, R.J., Abdulla, W.H., and Hong, S.C. (2020). Fundus Retinal Image Analyses for Screening and Diagnosing Diabetic Retinopathy, Macular Edema, and Glaucoma Disorders. Diabetes and Fundus OCT, Elsevier.","DOI":"10.1016\/B978-0-12-817440-1.00003-6"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1109\/RBME.2010.2084567","article-title":"Retinal imaging and image analysis","volume":"3","author":"Garvin","year":"2010","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"20130832","DOI":"10.1259\/bjr.20130832","article-title":"Retinal imaging as a source of biomarkers for diagnosis, characterization and prognosis of chronic illness or long-term conditions","volume":"87","author":"MacGillivray","year":"2014","journal-title":"Br. J. Radiol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1016\/j.neurol.2010.03.024","article-title":"Optical coherence tomography (OCT) in optic neuritis and multiple sclerosis","volume":"166","author":"Lamirel","year":"2010","journal-title":"Rev. Neurol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12933-015-0196-1","article-title":"Type 2 diabetes-associated carotid plaque burden is increased in patients with retinopathy compared to those without retinopathy","volume":"14","author":"Alonso","year":"2015","journal-title":"Cardiovasc. Diabetol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e000845","DOI":"10.1136\/bmjdrc-2019-000845","article-title":"Diabetic retinopathy as an independent predictor of subclinical cardiovascular disease: Baseline results of the PRECISED study","volume":"7","author":"Valente","year":"2019","journal-title":"BMJ Open Diabetes Res. Care"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"132","DOI":"10.14740\/cr1179","article-title":"Heart, Eye, and Artificial Intelligence: A Review","volume":"12","author":"Gupta","year":"2021","journal-title":"Cardiol. Res."},{"key":"ref_40","unstructured":"Ayman, J.S.S., and El-Baz, S. (2021). Machine Learning in Medicine-Chapman & Hall\/CRC Healthcare Informatics Series, CRC Press."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ayman El-Baz, J.S.S. (2019). Big Data in Multimodal Medical Imaging, Chapman and Hall.","DOI":"10.1201\/b22410"},{"key":"ref_42","first-page":"1805","article-title":"Moving beyond regression techniques in cardiovascular risk prediction: Applying machine learning to address analytic challenges","volume":"38","author":"Goldstein","year":"2016","journal-title":"Eur. Heart J."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1186\/s13058-019-1158-4","article-title":"Machine learning techniques for personalized breast cancer risk prediction: Comparison with the BCRAT and BOADICEA models","volume":"21","author":"Ming","year":"2019","journal-title":"Breast Cancer Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"392","DOI":"10.2741\/4725","article-title":"State-of-the-art review on deep learning in medical imaging","volume":"24","author":"Biswas","year":"2019","journal-title":"Front. Biosci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"104128","DOI":"10.1016\/j.compbiomed.2020.104128","article-title":"An efficient framework for automated screening of Clinically Significant Macular Edema","volume":"130","author":"Chalakkal","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Karbhari, Y., Basu, A., Geem, Z.W., Han, G.-T., and Sarkar, R. (2021). Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach. Diagnostics, 11.","DOI":"10.3390\/diagnostics11050895"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"e306","DOI":"10.1016\/S2589-7500(21)00043-1","article-title":"Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs","volume":"3","author":"Rim","year":"2021","journal-title":"Lancet Digit. Health"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1038\/s41551-020-00626-4","article-title":"A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre","volume":"5","author":"Cheung","year":"2020","journal-title":"Nat. Biomed. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.oret.2018.10.014","article-title":"Deep Learning\u2013Based Algorithms in Screening of Diabetic Retinopathy: A Systematic Review of Diagnostic Performance","volume":"3","author":"Nielsen","year":"2018","journal-title":"Ophthalmol. Retin."},{"key":"ref_50","unstructured":"Chalakkal, R.J. (2019). Automatic Retinal Image Analysis to Triage Retinal Pathologies, University of Auckland."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1007\/s13246-021-01012-3","article-title":"Diabetic retinopathy classification based on multipath CNN and machine learning classifiers","volume":"44","author":"Gayathri","year":"2021","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ur Rehman, M., and Zoha, A. (2022). Detecting Alzheimer\u2019s Disease Using Machine Learning Methods. Body Area Networks. Smart IoT and Big Data for Intelligent Health Management. BODYNETS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Springer.","DOI":"10.1007\/978-3-030-95593-9"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"100900","DOI":"10.1016\/j.preteyeres.2020.100900","article-title":"Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective","volume":"82","author":"Li","year":"2021","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.compbiomed.2019.01.002","article-title":"Performance evaluation of 10-year ultrasound image-based stroke\/cardiovascular (CV) risk calculator by comparing against ten conventional CV risk calculators: A diabetic study","volume":"105","author":"Khanna","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1142\/S0219519409003115","article-title":"Automatic computer-based tracings (act) in longitudinal 2-d ultrasound images using different scanners","volume":"9","author":"Molinari","year":"2009","journal-title":"J. Mech. Med. Biol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1016\/j.jacc.2010.05.007","article-title":"Myocardial Scar Visualized by Cardiovascular Magnetic Resonance Imaging Predicts Major Adverse Events in Patients With Hypertrophic Cardiomyopathy","volume":"56","author":"Bruder","year":"2010","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1016\/j.jacc.2010.06.030","article-title":"Coronary Risk Stratification, Discrimination, and Reclassification Improvement Based on Quantification of Subclinical Coronary Atherosclerosis: The Heinz Nixdorf Recall Study","volume":"56","author":"Erbel","year":"2010","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2744","DOI":"10.1109\/JBHI.2020.3040225","article-title":"Deep Learning for Diabetes: A Systematic Review","volume":"25","author":"Zhu","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"S62","DOI":"10.2337\/dc10-S062","article-title":"Diagnosis and classification of diabetes mellitus","volume":"33","author":"Association","year":"2010","journal-title":"Diabetes Care"},{"key":"ref_60","first-page":"19","article-title":"Insulin and insulin resistance","volume":"26","author":"Wilcox","year":"2005","journal-title":"Clin. Biochem. Rev."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1676","DOI":"10.1056\/NEJM199306103282306","article-title":"Long-Term Complications of Diabetes Mellitus","volume":"328","author":"Nathan","year":"1993","journal-title":"N. Engl. J. Med."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"92","DOI":"10.4239\/wjd.v6.i1.92","article-title":"Ocular complications of diabetes mellitus","volume":"6","author":"Sayin","year":"2015","journal-title":"World J. Diabetes"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Papatheodorou, K., Papanas, N., Banach, M., Papazoglou, D., and Edmonds, M. (2016). Complications of Diabetes 2016, Hindawi.","DOI":"10.1155\/2016\/6989453"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.ijdm.2009.08.001","article-title":"Chronic complications of diabetes mellitus in newly diagnosed patients","volume":"2","author":"Heydari","year":"2010","journal-title":"Int. J. Diabetes Mellit."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"489","DOI":"10.4239\/wjd.v6.i3.489","article-title":"Diabetic retinopathy-ocular complications of diabetes mellitus","volume":"6","author":"Nentwich","year":"2015","journal-title":"World J. Diabetes"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.dsx.2014.09.011","article-title":"Diabetic retinopathy\u2013Biomolecules and multiple pathophysiology","volume":"9","author":"Ahsan","year":"2015","journal-title":"Diabetes Metab. Syndr. Clin. Res. Rev."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1093\/fampra\/cmh417","article-title":"The impact of diabetic retinopathy: Perspectives from patient focus groups","volume":"21","author":"Coyne","year":"2004","journal-title":"Fam. Pract."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Amin, J., Sharif, M., and Yasmin, M. (2016). A Review on Recent Developments for Detection of Diabetic Retinopathy. Scientifica, 2016.","DOI":"10.1155\/2016\/6838976"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2653","DOI":"10.2337\/diacare.26.9.2653","article-title":"Diabetic retinopathy and diabetic macular edema: Pathophysiology, screening, and novel therapies","volume":"26","author":"Ciulla","year":"2003","journal-title":"Diabetes Care"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"3107","DOI":"10.2337\/diabetes.51.10.3107","article-title":"Pericytes and the Pathogenesis of Diabetic Retinopathy","volume":"51","author":"Hammes","year":"2002","journal-title":"Diabetes"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Zhu, J., Zhang, E., and del Rio-Tsonis, K. (2012). Eye Anatomy. eLS, John Wiley & Sons, Ltd.","DOI":"10.1002\/9780470015902.a0000108.pub2"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Hildebrand, G.D., and Fielder, A.R. (2011). Anatomy and physiology of the retina. Pediatric Retina, Springer.","DOI":"10.1007\/978-3-642-12041-1_2"},{"key":"ref_73","unstructured":"Maggs, D., Miller, P., and Ofri, R. (2017). Slatter\u2019s Fundamentals of Veterinary Ophthalmology E-Book, Elsevier Health Sciences."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Murugan, R. (2020). Implementation of Deep Learning Neural Network for Retinal Images. Handbook of Research on Applications and Implementations of Machine Learning Techniques, IGI Global.","DOI":"10.4018\/978-1-5225-9902-9.ch005"},{"key":"ref_75","first-page":"362","article-title":"Diabetes and Retinal Vascular Dysfunction","volume":"9","author":"Shin","year":"2015","journal-title":"J. Ophthalmic Vis. Res."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"801269","DOI":"10.1155\/2014\/801269","article-title":"Molecular mechanisms of diabetic retinopathy, general preventive strategies, and novel therapeutic targets","volume":"2014","author":"Safi","year":"2014","journal-title":"BioMed Res. Int."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1161\/CIRCRESAHA.110.223545","article-title":"Oxidative Stress and Diabetic Complications","volume":"107","author":"Giacco","year":"2010","journal-title":"Circ. Res."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Yumnamcha, T., Guerra, M., Singh, L.P., and Ibrahim, A.S. (2020). Metabolic Dysregulation and Neurovascular Dysfunction in Diabetic Retinopathy. Antioxidants, 9.","DOI":"10.3390\/antiox9121244"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"3420187","DOI":"10.1155\/2018\/3420187","article-title":"The Oxidative Stress and Mitochondrial Dysfunction during the Pathogenesis of Diabetic Retinopathy","volume":"2018","author":"Wu","year":"2018","journal-title":"Oxidative Med. Cell. Longev."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"179","DOI":"10.4103\/0301-4738.40355","article-title":"Diabetic retinopathy: An update","volume":"56","author":"Singh","year":"2008","journal-title":"Indian J. Ophthalmol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1159\/000491897","article-title":"Involvement of Advanced Glycation End Products in the Pathogenesis of Diabetic Retinopathy","volume":"48","author":"Xu","year":"2018","journal-title":"Cell. Physiol. Biochem."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Bandello, F., Lattanzio, R., Aragona, E., Marchese, A., Querques, G., and Zucchiatti, I. (2019). Nonproliferative Diabetic Retinopathy. Clinical Strategies in the Management of Diabetic Retinopathy, Springer.","DOI":"10.1007\/978-3-319-96157-6"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Silva, P.A., Cavallerano, J.D., Sun, J.K., Blodi, B.A., Davis, M.D., Aiello, L.M., and Aiello, L.P. (2013). Proliferative diabetic retinopathy. Retina, Elsevier.","DOI":"10.1016\/B978-1-4557-0737-9.00048-5"},{"key":"ref_84","first-page":"989","article-title":"Diabetic macular edema: A review","volume":"93","author":"Bresnick","year":"1986","journal-title":"J. Ophthalmol."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1001\/jamaophthalmol.2017.0988","article-title":"Association of diabetic macular edema and proliferative diabetic retinopathy with cardiovascular disease: A systematic review and meta-analysis","volume":"135","author":"Xie","year":"2017","journal-title":"JAMA Ophthalmol."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"301","DOI":"10.4155\/fmc.12.206","article-title":"Microvascular complications and diabetic retinopathy: Recent advances and future implications","volume":"5","author":"Barot","year":"2013","journal-title":"Futur. Med. Chem."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12933-018-0706-z","article-title":"Diabetic retinopathy is associated with the presence and burden of subclinical carotid atherosclerosis in type 1 diabetes","volume":"17","author":"Carbonell","year":"2018","journal-title":"Cardiovasc. Diabetol."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"706","DOI":"10.3389\/fimmu.2018.00706","article-title":"Vasa Vasorum Angiogenesis: Key Player in the Initiation and Progression of Atherosclerosis and Potential Target for the Treatment of Cardiovascular Disease","volume":"9","author":"Sedding","year":"2018","journal-title":"Front. Immunol."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"980","DOI":"10.2337\/dc18-2483","article-title":"Dysglycemia and the Density of the Coronary Vasa Vasorum","volume":"42","author":"Gerstein","year":"2019","journal-title":"Diabetes Care"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.1007\/s00125-020-05195-4","article-title":"Diabetic retinopathy: Looking beyond the eyes","volume":"63","author":"Stehouwer","year":"2020","journal-title":"Diabetologia"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.cjca.2017.12.005","article-title":"Diabetes, Hypertension, and Cardiovascular Disease: Clinical Insights and Vascular Mechanisms","volume":"34","author":"Petrie","year":"2017","journal-title":"Can. J. Cardiol."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"679","DOI":"10.4239\/wjd.v6.i5.679","article-title":"Endothelial dysfunction as a predictor of cardiovascular disease in type 1 diabetes","volume":"6","author":"Bertoluci","year":"2015","journal-title":"J. World J. Diabetes"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1159\/000262671","article-title":"Current approaches to retinopathy as a predictor of cardiovascular risk","volume":"Volume 20","author":"Cheung","year":"2010","journal-title":"Experimental Approaches to Diabetic Retinopathy"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1016\/j.cardiores.2004.05.001","article-title":"Advanced glycation end products and vascular inflammation: Implications for accelerated atherosclerosis in diabetes","volume":"63","author":"Basta","year":"2004","journal-title":"Cardiovasc. Res."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"5245308","DOI":"10.1155\/2020\/5245308","article-title":"Mechanistic Insights into the Oxidized Low-Density Lipoprotein-Induced Atherosclerosis","volume":"2020","author":"Khatana","year":"2020","journal-title":"Oxidative Med. Cell. Longev."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1038\/nature00804","article-title":"Subendothelial retention of atherogenic lipoproteins in early atherosclerosis","volume":"417","author":"Gustafsson","year":"2002","journal-title":"Nature"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.1161\/CIRCULATIONAHA.106.676890","article-title":"Subendothelial lipoprotein retention as the initiating process in atherosclerosis: Update and therapeutic implications","volume":"116","author":"Tabas","year":"2007","journal-title":"Circulation"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1111\/j.1542-4758.2012.00687.x","article-title":"Oxidized low-density lipoprotein and lipoprotein(a) levels in chronic kidney disease patients under hemodialysis: Influence of adiponectin and of a polymorphism in the apolipoprotein(a) gene","volume":"16","author":"Ribeiro","year":"2012","journal-title":"Hemodial. Int."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"49982","DOI":"10.1074\/jbc.M209649200","article-title":"Scavenger Receptors Class A-I\/II and CD36 Are the Principal Receptors Responsible for the Uptake of Modified Low Density Lipoprotein Leading to Lipid Loading in Macrophages","volume":"277","author":"Kunjathoor","year":"2002","journal-title":"J. Biol. Chem."},{"key":"ref_100","first-page":"389","article-title":"Inflammation and its resolution in atherosclerosis: Mediators and therapeutic opportunities","volume":"16","author":"Yurdagul","year":"2019","journal-title":"Nat. Rev. Cardiol."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.cmet.2010.06.008","article-title":"Myeloid Type I Interferon Signaling Promotes Atherosclerosis by Stimulating Macrophage Recruitment to Lesions","volume":"12","author":"Goossens","year":"2010","journal-title":"Cell Metab."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1111\/joim.12296","article-title":"Thrombosis formation on atherosclerotic lesions and plaque rupture","volume":"276","author":"Badimon","year":"2014","journal-title":"J. Intern. Med."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.2337\/diacare.23.9.1310","article-title":"Carotid intima-media thickness in Japanese type 2 diabetic subjects: Predictors of progression and relationship with incident coronary heart disease","volume":"23","author":"Yamasaki","year":"2000","journal-title":"Diabetes Care"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"2487","DOI":"10.2337\/dc18-0476","article-title":"The Association of Severe Diabetic Retinopathy With Cardiovascular Outcomes in Long-standing Type 1 Diabetes: A Longitudinal Follow-up","volume":"41","author":"Barlovic","year":"2018","journal-title":"Diabetes Care"},{"key":"ref_105","first-page":"513","article-title":"Diabetic retinopathy, diabetic macular edema, and cardiovascular risk: The importance of a long-term perspective and a multidisciplinary approach to optimal intravitreal therapy","volume":"57","author":"Bandello","year":"2019","journal-title":"Geol. Rundsch."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.2337\/diacare.28.6.1383","article-title":"Diabetic retinopathy is associated with mortality and cardiovascular disease incidence: The EURODIAB prospective complications study","volume":"28","author":"Dekker","year":"2005","journal-title":"Diabetes Care"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1177\/1741826711409324","article-title":"Cause-specific mortality in diabetes: Recent changes in trend mortality","volume":"19","author":"Khalil","year":"2011","journal-title":"Eur. J. Prev. Cardiol."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1089\/dia.2014.0141","article-title":"Relationship of Diabetic Retinopathy with Coronary Artery Disease in Asian Indians with Type 2 Diabetes: The Chennai Urban Rural Epidemiology Study (CURES) Eye Study\u20143","volume":"17","author":"Pradeepa","year":"2015","journal-title":"Diabetes Technol. Ther."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1016\/j.ophtha.2012.08.029","article-title":"Risk of cardiovascular diseases is increased even with mild diabetic retinopathy: The Japan Diabetes Complications Study","volume":"120","author":"Kawasaki","year":"2013","journal-title":"Ophthalmology"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"146","DOI":"10.2174\/1573399811309020006","article-title":"Diabetic retinopathy and atherosclerosis: Is there a link?","volume":"9","author":"Ellis","year":"2013","journal-title":"Curr. Diabetes Rev."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"1742","DOI":"10.2337\/dc07-0264","article-title":"Diabetic retinopathy and the risk of coronary heart disease: The Atherosclerosis Risk in Communities Study","volume":"30","author":"Cheung","year":"2007","journal-title":"Diabetes Care"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.3346\/jkms.2016.31.8.1292","article-title":"The Degree of Diabetic Retinopathy in Patients with Type 2 Diabetes Correlates with the Presence and Severity of Coronary Heart Disease","volume":"31","author":"Um","year":"2016","journal-title":"J. Korean Med. Sci."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1007\/s12031-020-01524-9","article-title":"Association Between Stroke and Parkinson\u2019s Disease: A Meta-analysis","volume":"70","author":"Liu","year":"2020","journal-title":"J. Mol. Neurosci."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.ophtha.2005.10.040","article-title":"Relation of Visual Function to Retinal Nerve Fiber Layer Thickness in Multiple Sclerosis","volume":"113","author":"Fisher","year":"2006","journal-title":"Ophthalmology"},{"key":"ref_115","first-page":"11","article-title":"Relation of CIMT to various Micro & Macro-vascular complications of Diabetes","volume":"3","year":"2018","journal-title":"Int. J. Med. Res. 2018"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1016\/j.athoracsur.2007.07.066","article-title":"Diabetic Retinopathy and Coronary Artery Disease From the Cardiac Surgeon\u2019s Perspective","volume":"85","author":"Ohno","year":"2008","journal-title":"Ann. Thorac. Surg."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Baumal, C.R., and Duker, J.S. (2017). Current Management of Diabetic Retinopathy, Elsevier Health Sciences.","DOI":"10.1016\/B978-0-323-48452-7.00004-4"},{"key":"ref_118","first-page":"36","article-title":"Image processing with ImageJ","volume":"11","author":"Ram","year":"2004","journal-title":"Biophotonics Int."},{"key":"ref_119","unstructured":"Facey, K., Cummins, E., Macpherson, K., Morris, A., Reay, L., and Slattery, J. (2002). Health Technology Assessment Report 1: Organisations of Services for Diabetic Retinopathy Screening, Health Technology Board."},{"key":"ref_120","first-page":"100","article-title":"Techniques of fundus imaging","volume":"33","author":"Jain","year":"2015","journal-title":"Med. Vis. Res. Found."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1002\/ana.25564","article-title":"Associations between cerebrovascular risk factors and parkinson disease","volume":"86","author":"Kummer","year":"2019","journal-title":"Ann. Neurol."},{"key":"ref_122","first-page":"169","article-title":"Retinal Imaging: Choosing the Right Method","volume":"3","author":"Karmel","year":"2014","journal-title":"EyeNet Mag."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1016\/j.jada.2009.05.016","article-title":"Action for Health in Diabetes (Look AHEAD) Trial: Baseline Evaluation of Selected Nutrients and Food Group Intake","volume":"109","author":"Vitolins","year":"2009","journal-title":"J. Am. Diet. Assoc."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1016\/j.acvd.2009.02.008","article-title":"Retinal microvascularisation abnormalities and cardiovascular risk","volume":"102","author":"Mimoun","year":"2009","journal-title":"Arch. Cardiovasc. Dis."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"1270","DOI":"10.1093\/eurheartj\/eht023","article-title":"The eye and the heart","volume":"34","author":"Flammer","year":"2013","journal-title":"Eur. Heart J."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1161\/CIRCULATIONAHA.116.023425","article-title":"Retinal vessel calibers in predicting long-term cardiovascular outcomes: The atherosclerosis risk in communities study","volume":"134","author":"Seidelmann","year":"2016","journal-title":"Circulation"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1590\/S0004-27492011000500003","article-title":"Color Doppler imaging of the ophthalmic artery in patients with chronic heart failure","volume":"74","author":"Paranhos","year":"2011","journal-title":"Arq. Bras. De Oftalmol."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1093\/eurheartj\/ehx565","article-title":"Retinal microvascular dysfunction in heart failure","volume":"39","author":"Naegele","year":"2018","journal-title":"Eur. Heart J."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1161\/01.STR.0000120310.43457.AD","article-title":"Relationship between Carotid Artery Stiffness and Retinal Arteriolar Narrowing in Healthy Middle-Aged Persons","volume":"35","author":"Liao","year":"2004","journal-title":"Stroke"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"374.e1","DOI":"10.1016\/j.amjmed.2009.05.030","article-title":"The Relationship between Retinal Microvascular Abnormalities and Coronary Heart Disease: A Review","volume":"123","author":"McClintic","year":"2010","journal-title":"Am. J. Med."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1093\/eurheartj\/ehm244","article-title":"Carotid intima-media thickness by B-mode ultrasound as surrogate of coronary atherosclerosis: Correlation with quantitative coronary angiography and coronary intravascular ultrasound findings","volume":"28","author":"Amato","year":"2007","journal-title":"Eur. Heart J."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"2181","DOI":"10.1185\/030079906X148472","article-title":"Carotid intima-media thickness as a surrogate marker for cardiovascular disease in intervention studies","volume":"22","author":"Bots","year":"2006","journal-title":"Curr. Med. Res. Opin."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.atherosclerosissup.2018.04.040","article-title":"Carotid Plaque is a Strong Surrogate Marker for CAC and Subclinical CHD in the General Population","volume":"32","author":"Gudmundsson","year":"2018","journal-title":"Atheroscler. Suppl."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1093\/eurheartj\/ehq431","article-title":"Fractal analysis of retinal microvasculature and coronary heart disease mortality","volume":"32","author":"Liew","year":"2010","journal-title":"Eur. Heart J."},{"key":"ref_135","first-page":"e61865","article-title":"A Murine Model of Ischemic Retinal Injury Induced by Transient Bilateral Common Carotid Artery Occlusion","volume":"165","author":"Lee","year":"2020","journal-title":"J. Vis. Exp."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"3034","DOI":"10.2337\/dc20-0370","article-title":"Carotid Disease and Retinal Optical Coherence Tomography Angiography Parameters in Type 2 Diabetes: The Fremantle Diabetes Study Phase II","volume":"43","author":"Drinkwater","year":"2020","journal-title":"Diabetes Care"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"e14407","DOI":"10.1111\/dme.14407","article-title":"The association between carotid disease, arterial stiffness and diabetic retinopathy in type 2 diabetes: The Fremantle Diabetes Study Phase II","volume":"38","author":"Drinkwater","year":"2020","journal-title":"Diabet. Med."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12933-020-01023-6","article-title":"The relationship between carotid disease and retinopathy in diabetes: A systematic review","volume":"19","author":"Drinkwater","year":"2020","journal-title":"Cardiovasc. Diabetol."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1089\/dia.2019.0251","article-title":"Time in Range Is Associated with Carotid Intima-Media Thickness in Type 2 Diabetes","volume":"22","author":"Lu","year":"2020","journal-title":"Diabetes Technol. Ther."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"135","DOI":"10.2147\/CLEP.S30621","article-title":"Heart disease as a risk factor for dementia","volume":"5","author":"Hakim","year":"2013","journal-title":"Clin. Epidemiol."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1186\/s12916-014-0130-5","article-title":"Cardiovascular risk factors and future risk of Alzheimer\u2019s disease","volume":"12","author":"Bruijn","year":"2014","journal-title":"BMC Med."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1016\/j.ultrasmedbio.2014.12.024","article-title":"Improved Correlation between Carotid and Coronary Atherosclerosis SYNTAX Score Using Automated Ultrasound Carotid Bulb Plaque IMT Measurement","volume":"41","author":"Ikeda","year":"2015","journal-title":"Ultrasound Med. Biol."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.cmpb.2016.02.004","article-title":"PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology","volume":"128","author":"Araki","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1038\/s41598-017-00989-w","article-title":"Imaging atherosclerosis in rheumatoid arthritis: Evidence for increased prevalence, altered phenotype and a link between systemic and localised plaque inflammation","volume":"7","author":"Skeoch","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1586\/14779072.5.1.69","article-title":"MRI of atherosclerosis: Diagnosis and monitoring therapy","volume":"5","author":"Kramer","year":"2007","journal-title":"Expert Rev. Cardiovasc. Ther."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1002\/jmri.20070","article-title":"MRI of atherosclerosis","volume":"19","author":"Yuan","year":"2004","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"2306","DOI":"10.1161\/01.STR.0000141426.63959.cc","article-title":"Systematic Review of Computed Tomographic Angiography for Assessment of Carotid Artery Disease","volume":"35","author":"Koelemay","year":"2004","journal-title":"Stroke"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s11883-018-0736-8","article-title":"A Survey on Coronary Atherosclerotic Plaque Tissue Characterization in Intravascular Optical Coherence Tomography","volume":"20","author":"Boi","year":"2018","journal-title":"Curr. Atheroscler. Rep."},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Laine, A., Sanches, J.M., and Suri, J.S. (2012). Ultrasound Imaging: Advances and Applications, Springer.","DOI":"10.1007\/978-1-4614-1180-2"},{"key":"ref_150","unstructured":"Suri, J.S. (2008). Advances in Diagnostic and Therapeutic Ultrasound Imaging, Artech House."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"151","DOI":"10.2214\/AJR.11.6955","article-title":"Association Between Carotid Artery Plaque Volume, Composition, and Ulceration: A Retrospective Assessment With MDCT","volume":"199","author":"Saba","year":"2012","journal-title":"Am. J. Roentgenol."},{"key":"ref_152","doi-asserted-by":"crossref","unstructured":"Nicolaides, A., Beach, K.W., Kyriacou, E., and Pattichis, C.S. (2012). Principles of Ultrasonic Imaging and Instrumentation. Ultrasound and Carotid Bifurcation Atherosclerosis, Springer.","DOI":"10.1007\/978-1-84882-688-5"},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"451","DOI":"10.23736\/S0392-9590.19.04267-6","article-title":"Global perspective on carotid intima-media thickness and plaque: Should the current measurement guidelines be revisited?","volume":"38","author":"Saba","year":"2020","journal-title":"Int. Angiol."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.diabres.2018.07.028","article-title":"Echolucency-based phenotype in carotid atherosclerosis disease for risk stratification of diabetes patients","volume":"143","author":"Kotsis","year":"2018","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1111\/echo.14242","article-title":"Nonlinear model for the carotid artery disease 10-year risk prediction by fusing conventional cardiovascular factors to carotid ultrasound image phenotypes: A Japanese diabetes cohort study","volume":"36","author":"Khanna","year":"2019","journal-title":"Echocardiography"},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.1007\/s11517-019-01975-2","article-title":"Effect of carotid image-based phenotypes on cardiovascular risk calculator: AECRS1.0","volume":"57","author":"Khanna","year":"2019","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"420","DOI":"10.21037\/cdt.2019.09.03","article-title":"A low-cost machine learning-based cardiovascular\/stroke risk assessment system: Integration of conventional factors with image phenotypes","volume":"9","author":"Jamthikar","year":"2019","journal-title":"Cardiovasc. Diagn. Ther."},{"key":"ref_158","unstructured":"Liu, K., and Suri, J.S. (2001). Automatic Vessel Indentification for Angiographic Screening. (90800901-A), U.S. Patent."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"1112","DOI":"10.1109\/TUFFC.2010.1522","article-title":"Intima-media thickness: Setting a standard for a completely automated method of ultrasound measurement","volume":"57","author":"Molinari","year":"2010","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1109\/TIP.2011.2169270","article-title":"Completely Automated Multiresolution Edge Snapper\u2014A New Technique for an Accurate Carotid Ultrasound IMT Measurement: Clinical Validation and Benchmarking on a Multi-Institutional Database","volume":"21","author":"Molinari","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1007\/s11517-011-0781-8","article-title":"Completely automated robust edge snapper for carotid ultrasound IMT measurement on a multi-institutional database of 300 images","volume":"49","author":"Molinari","year":"2011","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1118\/1.3670373","article-title":"Carotid artery recognition system: A comparison of three automated paradigms for ultrasound images","volume":"39","author":"Molinari","year":"2012","journal-title":"Med. Phys."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"946","DOI":"10.1016\/j.cmpb.2012.05.008","article-title":"Ultrasound IMT measurement on a multi-ethnic and multi-institutional database: Our review and experience using four fully automated and one semi-automated methods","volume":"108","author":"Molinari","year":"2012","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_164","doi-asserted-by":"crossref","unstructured":"Molinari, F., Acharya, U.R., Saba, L., Nicolaides, A., and Suri, J.S. (2014). Hypothesis validation of far wall brightness in carotid artery ultrasound for feature-based IMT measurement using a combination of level set segmentation and registration. Multi-Modality Atherosclerosis Imaging and Diagnosis, Springer.","DOI":"10.1007\/978-1-4614-7425-8_21"},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1093\/ehjci\/jez070","article-title":"Carotid intraplaque neovascularization predicts coronary artery disease and cardiovascular events","volume":"20","author":"Mantella","year":"2019","journal-title":"Eur. Heart J. Cardiovasc. Imaging"},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.jvssci.2021.03.001","article-title":"Review of imaging biomarkers for the vulnerable carotid plaque","volume":"2","author":"Saba","year":"2021","journal-title":"JVS Vasc. Sci."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.ejim.2015.10.018","article-title":"The effect of smoking on carotid intima\u2013media thickness progression rate and rate of lumen diameter reduction","volume":"28","author":"Hansen","year":"2015","journal-title":"Eur. J. Intern. Med."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"e344","DOI":"10.18295\/squmj.2015.15.03.007","article-title":"Correlation between Carotid Artery Intima-Media Thickness and Luminal Diameter with Body Mass Index and Other Cardiovascular Risk Factors in Adults","volume":"15","author":"Rashid","year":"2015","journal-title":"Sultan Qaboos Univ. Med. J. [SQUMJ]"},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1161\/01.STR.0000258003.31194.0a","article-title":"Predictors of carotid intima-media thickness progression in young adults: The Bogalusa Heart Study","volume":"38","author":"Johnson","year":"2007","journal-title":"Stroke"},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"826095","DOI":"10.1155\/2014\/826095","article-title":"Cardiovascular Disease Risk amongst African Black Patients with Rheumatoid Arthritis: The Need for Population Specific Stratification","volume":"2014","author":"Solomon","year":"2014","journal-title":"BioMed Res. Int."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1016\/j.atherosclerosis.2015.01.030","article-title":"Risk factors for the progression of carotid intima-media thickness over a 16-year follow-up period: The Malm\u00f6 Diet and Cancer Study","volume":"239","author":"Rosvall","year":"2015","journal-title":"Atherosclerosis"},{"key":"ref_172","first-page":"339","article-title":"Automated carotid intima-media thickness and its link for prediction of SYNTAX score in Japanese coronary artery disease patients","volume":"32","author":"Ikeda","year":"2013","journal-title":"Int. Angiol."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1177\/0003319720941730","article-title":"Does the Carotid Bulb Offer a Better 10-Year CVD\/Stroke Risk Assessment Compared to the Common Carotid Artery? A 1516 Ultrasound Scan Study","volume":"71","author":"Viswanathan","year":"2020","journal-title":"Angiology"},{"key":"ref_174","first-page":"392","article-title":"Link between automated coronary calcium volumes from intravascular ultrasound to automated carotid IMT from B-mode ultrasound in coronary artery disease population","volume":"33","author":"Araki","year":"2014","journal-title":"Int. Angiol."},{"key":"ref_175","first-page":"290","article-title":"Integration of eGFR biomarker in image-based CV\/Stroke risk calculator: A south Asian-Indian diabetes cohort with moderate chronic kidney disease","volume":"39","author":"Viswanathan","year":"2020","journal-title":"Int. Angiol. A J. Int. Union Angiol."},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.52586\/5026","article-title":"Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: A narrative review","volume":"26","author":"Munjral","year":"2021","journal-title":"Front. Biosci."},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.cmpb.2015.10.022","article-title":"A new method for IVUS-based coronary artery disease risk stratification: A link between coronary & carotid ultrasound plaque burdens","volume":"124","author":"Araki","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"2935","DOI":"10.1016\/j.jacc.2013.11.005","article-title":"2013 ACC\/AHA guideline on the assessment of cardiovascular risk: A report of the American College of Cardiology\/American Heart Association Task Force on Practice Guidelines","volume":"63","author":"Goff","year":"2014","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.1136\/bmj.39554.624086.AD","article-title":"Risk assessment and lipid modification for primary and secondary prevention of cardiovascular disease: Summary of NICE guidance","volume":"336","author":"Cooper","year":"2008","journal-title":"BMJ"},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"378","DOI":"10.3399\/bjgp15X685933","article-title":"Cardiovascular risk assessment and lipid modification: NICE guideline","volume":"65","author":"Duerden","year":"2015","journal-title":"Br. J. Gen. Pract."},{"key":"ref_181","unstructured":"NICE (2022, April 13). Cardiovascular Disease: Risk Assessment and Reduction, Including Lipid Modifification. Available online: https:\/\/www.nice.org.uk\/guidance\/cg181."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1016\/j.cjca.2016.07.510","article-title":"2016 Canadian Cardiovascular Society Guidelines for the Management of Dyslipidemia for the Prevention of Cardiovascular Disease in the Adult","volume":"32","author":"Anderson","year":"2016","journal-title":"Can. J. Cardiol."},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.jcct.2009.07.003","article-title":"What are the basic concepts of temporal, contrast, and spatial resolution in cardiac CT?","volume":"3","author":"Lin","year":"2009","journal-title":"J. Cardiovasc. Comput. Tomogr."},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1161\/CIRCULATIONAHA.107.699579","article-title":"General cardiovascular risk profile for use in primary care: The Framingham Heart Study","volume":"117","author":"Vasan","year":"2008","journal-title":"Circulation"},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1016\/S0195-668X(03)00114-3","article-title":"Estimation of ten-year risk of fatal cardiovascular disease in Europe: The SCORE project","volume":"24","author":"Conroy","year":"2003","journal-title":"Eur. Heart J."},{"key":"ref_186","first-page":"j2099","article-title":"Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: Prospective cohort study","volume":"357","author":"Coupland","year":"2017","journal-title":"BMJ"},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1007\/s10916-020-01675-7","article-title":"Low-Cost Office-Based Cardiovascular Risk Stratification Using Machine Learning and Focused Carotid Ultrasound in an Asian-Indian Cohort","volume":"44","author":"Jamthikar","year":"2020","journal-title":"J. Med. Syst."},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1007\/s10554-020-02099-7","article-title":"Multiclass machine learning vs. conventional calculators for stroke\/CVD risk assessment using carotid plaque predictors with coronary angiography scores as gold standard: A 500 participants study","volume":"37","author":"Jamthikar","year":"2020","journal-title":"Int. J. Cardiovasc. Imaging"},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"3145","DOI":"10.1007\/s10554-021-02294-0","article-title":"Role of artificial intelligence in cardiovascular risk prediction and outcomes: Comparison of machine-learning and conventional statistical approaches for the analysis of carotid ultrasound features and intra-plaque neovascularization","volume":"37","author":"Johri","year":"2021","journal-title":"Int. J. Cardiovasc. Imaging"},{"key":"ref_190","doi-asserted-by":"crossref","unstructured":"Kigka, V.I., Sakellarios, A.I., Mantzaris, M.D., Tsakanikas, V.D., Potsika, V.T., Palombo, D., and Fotiadis, D.I. (November, January 31). A Machine Learning Model for the Identification of High risk Carotid Atherosclerotic Plaques. Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Virtual.","DOI":"10.1109\/EMBC46164.2021.9630654"},{"key":"ref_191","doi-asserted-by":"crossref","unstructured":"Weng, S.F., Reps, J.M., Kai, J., Garibaldi, J.M., and Qureshi, N. (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data?. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0174944"},{"key":"ref_192","doi-asserted-by":"crossref","unstructured":"Alaa, A.M., Bolton, T., di Angelantonio, E., Rudd, J.H., and van der Schaar, M. (2019). Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0213653"},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"e009476","DOI":"10.1161\/JAHA.118.009476","article-title":"Machine Learning Outperforms ACC\/AHA CVD Risk Calculator in MESA","volume":"7","author":"Kakadiaris","year":"2018","journal-title":"J. Am. Heart Assoc."},{"key":"ref_194","doi-asserted-by":"crossref","unstructured":"El-Baz, A., and Suri, J.S. (2019). Big Data in Multimodal Medical Imaging, CRC Press.","DOI":"10.1201\/b22410"},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.bspc.2016.03.001","article-title":"Speckle reduction in medical ultrasound images using an unbiased non-local means method","volume":"28","author":"Sudeep","year":"2016","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"79354","DOI":"10.1109\/ACCESS.2019.2923547","article-title":"Thyroid ultrasound texture classification using autoregressive features in conjunction with machine learning approaches","volume":"7","author":"Poudel","year":"2019","journal-title":"IEEE Access"},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1007\/s10916-018-0940-7","article-title":"Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers","volume":"42","author":"Maniruzzaman","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.compbiomed.2017.08.014","article-title":"Lung disease stratification using amalgamation of Riesz and Gabor transforms in machine learning framework","volume":"89","author":"Than","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.cmpb.2011.10.001","article-title":"ThyroScreen system: High resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform","volume":"107","author":"Acharya","year":"2011","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1177\/0954411913483637","article-title":"Diagnosis of Hashimoto\u2019s thyroiditis in ultrasound using tissue characterization and pixel classification","volume":"227","author":"Acharya","year":"2013","journal-title":"Proc. Inst. Mech. Eng. Part H J. Eng. Med."},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"4255","DOI":"10.1118\/1.4725759","article-title":"Data mining framework for fatty liver disease classification in ultrasound: A hybrid feature extraction paradigm","volume":"39","author":"Acharya","year":"2012","journal-title":"Med. Phys."},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.cmpb.2017.12.016","article-title":"Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm","volume":"155","author":"Biswas","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_203","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.measurement.2017.01.016","article-title":"Risk stratification of 2D ultrasound-based breast lesions using hybrid feature selection in machine learning paradigm","volume":"105","author":"Singh","year":"2017","journal-title":"Measurement"},{"key":"ref_204","doi-asserted-by":"crossref","first-page":"541","DOI":"10.31083\/j.rcm.2020.04.236","article-title":"Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence","volume":"21","author":"Suri","year":"2020","journal-title":"Rev. Cardiovasc. Med."},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"104043","DOI":"10.1016\/j.compbiomed.2020.104043","article-title":"Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: A narrative review of integrated approaches using carotid ultrasound","volume":"126","author":"Jamthikar","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_206","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/MC.2016.339","article-title":"Machine Learning in Cardiac Health Monitoring and Decision Support","volume":"49","author":"Hijazi","year":"2016","journal-title":"Computer"},{"key":"ref_207","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1109\/TIM.2011.2174897","article-title":"An Accurate and Generalized Approach to Plaque Characterization in 346 Carotid Ultrasound Scans","volume":"61","author":"Acharya","year":"2011","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_208","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1007\/s11517-012-1019-0","article-title":"Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: A paradigm for stroke risk assessment","volume":"51","author":"Acharya","year":"2013","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_209","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1007\/s10554-012-0124-3","article-title":"Association between conventional risk factors and different ultrasound-based markers of atherosclerosis at carotid and femoral levels in a middle-aged population","volume":"29","author":"Yerly","year":"2012","journal-title":"Int. J. Cardiovasc. Imaging"},{"key":"ref_210","doi-asserted-by":"crossref","first-page":"919","DOI":"10.21037\/cdt.2020.01.07","article-title":"Cardiovascular\/stroke risk predictive calculators: A comparison between statistical and machine learning models","volume":"10","author":"Jamthikar","year":"2020","journal-title":"Cardiovasc. Diagn. Ther."},{"key":"ref_211","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.ihj.2020.06.004","article-title":"Cardiovascular\/stroke risk prevention: A new machine learning framework integrating carotid ultrasound image-based phenotypes and its harmonics with conventional risk factors","volume":"72","author":"Jamthikar","year":"2020","journal-title":"Indian Heart J."},{"key":"ref_212","doi-asserted-by":"crossref","first-page":"289","DOI":"10.7785\/tcrt.2012.500381","article-title":"A Review on Ultrasound-Based Thyroid Cancer Tissue Characterization and Automated Classification","volume":"13","author":"Acharya","year":"2014","journal-title":"Technol. Cancer Res. Treat."},{"key":"ref_213","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1016\/j.ultrasmedbio.2010.07.011","article-title":"Characterization of Single Thyroid Nodules by Contrast-Enhanced 3-D Ultrasound","volume":"36","author":"Molinari","year":"2010","journal-title":"Ultrasound Med. Biol."},{"key":"ref_214","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.cmpb.2015.11.013","article-title":"Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: A first comparative study of its kind","volume":"126","author":"Shrivastava","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_215","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1109\/TMI.2016.2535865","article-title":"Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network","volume":"35","author":"Ebner","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_216","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.gaitpost.2021.06.017","article-title":"Machine-learning models for activity class prediction: A comparative study of feature selection and classification algorithms","volume":"89","author":"Chong","year":"2021","journal-title":"Gait Posture"},{"key":"ref_217","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_218","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/JBHI.2016.2631401","article-title":"A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound","volume":"21","author":"Lekadir","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_219","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.compbiomed.2018.05.014","article-title":"Deep learning strategy for accurate carotid intima-media thickness measurement: An ultrasound study on Japanese diabetic cohort","volume":"98","author":"Biswas","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_220","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1109\/72.914517","article-title":"An introduction to kernel-based learning algorithms","volume":"12","author":"Muller","year":"2001","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_221","doi-asserted-by":"crossref","first-page":"104721","DOI":"10.1016\/j.compbiomed.2021.104721","article-title":"Hybrid deep learning segmentation models for atherosclerotic plaque in internal carotid artery B-mode ultrasound","volume":"136","author":"Jain","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_222","doi-asserted-by":"crossref","first-page":"3242","DOI":"10.1038\/s41467-021-23458-5","article-title":"A deep learning system for detecting diabetic retinopathy across the disease spectrum","volume":"12","author":"Dai","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_223","doi-asserted-by":"crossref","first-page":"138882","DOI":"10.1016\/j.scitotenv.2020.138882","article-title":"COVID-19 outbreak: Migration, effects on society, global environment and prevention","volume":"728","author":"Chakraborty","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_224","unstructured":"(2022, March 06). Coronavirus Worldometers [Internet]. Available online: https:\/\/www.worldometers.info\/coronavirus\/."},{"key":"ref_225","unstructured":"World Health Organization (WHO) (2021, June 22). Naming the Coronavirus Disease (COVID-19) and the Virus That Causes It. Available online: https:\/\/www.who.int\/emergencies\/diseases\/novel-coronavirus-2019\/technical-guidance\/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-that-causes-it."},{"key":"ref_226","doi-asserted-by":"crossref","unstructured":"Suri, J.S., Puvvula, A., Biswas, M., Majhail, M., Saba, L., Faa, G., Singh, I.M., Oberleitner, R., Turk, M., and Chadha, P.S. (2020). COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review. Comput. Biol. Med., 124.","DOI":"10.1016\/j.compbiomed.2020.103960"},{"key":"ref_227","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/S2213-8587(20)30405-8","article-title":"Risks of and risk factors for COVID-19 disease in people with diabetes: A cohort study of the total population of Scotland","volume":"9","author":"McGurnaghan","year":"2020","journal-title":"Lancet Diabetes Endocrinol."},{"key":"ref_228","doi-asserted-by":"crossref","first-page":"4128","DOI":"10.1109\/JBHI.2021.3103839","article-title":"Systematic Review of Artificial Intelligence in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients: A Biomedical Imaging Perspective","volume":"25","author":"Suri","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_229","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1136\/heartjnl-2020-317901","article-title":"Impact of cardiovascular disease and risk factors on fatal outcomes in patients with COVID-19 according to age: A systematic review and meta-analysis","volume":"107","author":"Bae","year":"2020","journal-title":"Heart"},{"key":"ref_230","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1038\/s41569-020-0413-9","article-title":"COVID-19 and cardiovascular disease: From basic mechanisms to clinical perspectives","volume":"17","author":"Nishiga","year":"2020","journal-title":"Nat. Rev. Cardiol."},{"key":"ref_231","doi-asserted-by":"crossref","unstructured":"Samidurai, A., and Das, A. (2020). Cardiovascular Complications Associated with COVID-19 and Potential Therapeutic Strategies. Int. J. Mol. Sci., 21.","DOI":"10.3390\/ijms21186790"},{"key":"ref_232","doi-asserted-by":"crossref","first-page":"1666","DOI":"10.1093\/cvr\/cvaa106","article-title":"COVID-19 and the cardiovascular system: Implications for risk assessment, diagnosis, and treatment options","volume":"116","author":"Guzik","year":"2020","journal-title":"Cardiovasc. Res."},{"key":"ref_233","first-page":"166","article-title":"Computed tomography findings of COVID-19 pneumonia in Intensive Care Unit-patients","volume":"22","author":"Cau","year":"2021","journal-title":"J. Public Health Res."},{"key":"ref_234","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1080\/09273948.2020.1772313","article-title":"COVID-19 and the Ocular Surface: A Review of Transmission and Manifestations","volume":"28","author":"Ho","year":"2020","journal-title":"Ocul. Immunol. Inflamm."},{"key":"ref_235","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40662-020-00189-0","article-title":"SARS-CoV-2 in the ocular surface of COVID-19 patients","volume":"7","author":"Xie","year":"2020","journal-title":"Eye Vis."},{"key":"ref_236","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1016\/S1474-4422(21)00182-4","article-title":"Post-viral effects of COVID-19 in the olfactory system and their implications","volume":"20","author":"Xydakis","year":"2021","journal-title":"Lancet Neurol."},{"key":"ref_237","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1002\/lio2.291","article-title":"The association between diabetes and olfactory impairment in adults: A systematic review and meta-analysis","volume":"4","author":"Kim","year":"2019","journal-title":"Laryngoscope"},{"key":"ref_238","doi-asserted-by":"crossref","first-page":"108529","DOI":"10.1016\/j.diabres.2020.108529","article-title":"Diabetic retinopathy is independently associated with increased risk of intubation: A single centre cohort study of patients with diabetes hospitalised with COVID-19","volume":"171","author":"Corcillo","year":"2020","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_239","doi-asserted-by":"crossref","first-page":"11085","DOI":"10.1038\/s41598-021-90482-2","article-title":"Ocular findings among patients surviving COVID-19","volume":"11","author":"Costa","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_240","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1177\/1932296820930045","article-title":"Personal Experience with COVID-19 and Community Screening of Diabetic Retinopathy in Iran","volume":"14","author":"Jamali","year":"2020","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_241","doi-asserted-by":"crossref","unstructured":"Malerbi, F.K., Morales, P.H.A., and Regatieri, C.V.S. (2020). Diabetic retinopathy screening and the COVID-19 pandemic in Brazil. Arq. Bras. de Oftalmol., 83.","DOI":"10.5935\/0004-2749.20200070"},{"key":"ref_242","doi-asserted-by":"crossref","first-page":"9036847","DOI":"10.1155\/2020\/9036847","article-title":"The Importance of Telemedicine during COVID-19 Pandemic: A Focus on Diabetic Retinopathy","volume":"2020","author":"Galiero","year":"2020","journal-title":"J. Diabetes Res."},{"key":"ref_243","unstructured":"MedicMind (2022, March 10). Teleophthalmology. Available online: https:\/\/www.medicmind.tech\/."},{"key":"ref_244","unstructured":"Care1 (2022, March 14). Pandemic Eyecare Response Canada (PERC). Available online: https:\/\/care1.ca\/."},{"key":"ref_245","doi-asserted-by":"crossref","unstructured":"Wentzel, A., and Mchiza, Z.J.-R. (2021). Exploring Factors Associated with Diabetic Retinopathy Treatment Compliance Behaviour in Cape Town, South Africa. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph182212209"},{"key":"ref_246","doi-asserted-by":"crossref","first-page":"2944","DOI":"10.4103\/ijo.IJO_1242_21","article-title":"Situational analysis of diabetic retinopathy screening in India: How has it changed in the last three years?","volume":"69","author":"Murthy","year":"2021","journal-title":"Indian J. Ophthalmol."},{"key":"ref_247","doi-asserted-by":"crossref","first-page":"104310","DOI":"10.1016\/j.mvr.2021.104310","article-title":"What is the impact of microvascular complications of diabetes on severe COVID-19?","volume":"140","author":"Basra","year":"2021","journal-title":"Microvasc. Res."},{"key":"ref_248","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11892-021-01411-6","article-title":"The Impact of COVID-19 on Diabetic Retinopathy Monitoring and Treatment","volume":"21","author":"Ahmed","year":"2021","journal-title":"Curr. Diabetes Rep."},{"key":"ref_249","first-page":"e14831","article-title":"Effect of COVID-19-Associated Lockdown on Patients With Diabetic Retinopathy","volume":"13","author":"Chatziralli","year":"2021","journal-title":"Cureus"},{"key":"ref_250","first-page":"e19148","article-title":"Effect of COVID-19 Pandemic on Presentation of Patients with Diabetic Retinopathy in a Multitier Ophthalmology Network in India","volume":"13","author":"Das","year":"2021","journal-title":"Cureus"},{"key":"ref_251","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.2147\/TCRM.S316265","article-title":"The Influence of COVID-19 Lockdown in Jordan on Patients with Diabetic Retinopathy: A Case\u2013Control Study","volume":"17","author":"Rwashdeh","year":"2021","journal-title":"Ther. Clin. Risk Manag."},{"key":"ref_252","doi-asserted-by":"crossref","first-page":"3664","DOI":"10.4103\/ijo.IJO_1474_21","article-title":"Sight-threatening intraocular infection in patients with COVID-19 in India","volume":"69","author":"Nayak","year":"2021","journal-title":"Indian J. Ophthalmol."},{"key":"ref_253","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.ajo.2021.03.033","article-title":"A Smart Mobile Application to Monitor Visual Function in Diabetic Retinopathy and Age-Related Macular Degeneration: The CLEAR Study","volume":"227","author":"Khurana","year":"2021","journal-title":"Am. J. Ophthalmol."},{"key":"ref_254","doi-asserted-by":"crossref","first-page":"483","DOI":"10.4103\/ijo.IJO_3798_20","article-title":"Vitamin D supplementation in diabetic retinopathy in the era of COVID-19","volume":"69","author":"Saxena","year":"2021","journal-title":"Indian J. Ophthalmol."},{"key":"ref_255","doi-asserted-by":"crossref","first-page":"4015","DOI":"10.2147\/OPTH.S294428","article-title":"A systematic review of current teleophthalmology services in New Zealand compared to the four comparable countries of the United Kingdom, Australia, United States of America (USA) and Canada","volume":"15","author":"Walsh","year":"2021","journal-title":"Clin. Ophthalmol."},{"key":"ref_256","doi-asserted-by":"crossref","unstructured":"Kumari, S., Venkatesh, P., Tandon, N., Chawla, R., Takkar, B., and Kumar, A. (2021). Selfie fundus imaging for diabetic retinopathy screening. Eye, 1\u20136.","DOI":"10.2139\/ssrn.3785992"},{"key":"ref_257","doi-asserted-by":"crossref","first-page":"110164","DOI":"10.1016\/j.ejrad.2022.110164","article-title":"Long-COVID diagnosis: From diagnostic to advanced AI-driven models","volume":"148","author":"Cau","year":"2022","journal-title":"Eur. J. Radiol."},{"key":"ref_258","doi-asserted-by":"crossref","first-page":"1935","DOI":"10.1111\/dom.14057","article-title":"COVID-19 infection may cause ketosis and ketoacidosis","volume":"22","author":"Li","year":"2020","journal-title":"Diabetes Obes. Metab."},{"key":"ref_259","doi-asserted-by":"crossref","first-page":"108166","DOI":"10.1016\/j.diabres.2020.108166","article-title":"Diabetic ketoacidosis precipitated by COVID-19 in a patient with newly diagnosed diabetes mellitus","volume":"164","author":"Chee","year":"2020","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_260","doi-asserted-by":"crossref","first-page":"h5660","DOI":"10.1136\/bmj.h5660","article-title":"Diabetic ketoacidosis in adults","volume":"351","author":"Misra","year":"2015","journal-title":"BMJ"},{"key":"ref_261","first-page":"100589","article-title":"COVID-19 cardiovascular epidemiology, cellular pathogenesis, clinical manifestations and management","volume":"29","author":"Sattar","year":"2020","journal-title":"Int. J. Cardiol. Heart Vasc."},{"key":"ref_262","doi-asserted-by":"crossref","first-page":"9","DOI":"10.23736\/S0392-9590.21.04771-4","article-title":"Automated deep learning-based paradigm for high-risk plaque detection in B-mode common carotid ultrasound scans: An asymptomatic Japanese cohort study","volume":"41","author":"Jain","year":"2022","journal-title":"Int. Angiol."},{"key":"ref_263","first-page":"12609","article-title":"Molecular pathways triggered by COVID-19 in different organs: ACE2 receptor-expressing cells under attack? A review","volume":"24","author":"Saba","year":"2020","journal-title":"Eur. Rev. Med. Pharmacol. Sci."},{"key":"ref_264","doi-asserted-by":"crossref","first-page":"1352","DOI":"10.21037\/cdt-20-561","article-title":"Can COVID-19 trigger the plaque vulnerability\u2014a Kounis syndrome warning for asymptomatic subjects","volume":"10","author":"Saba","year":"2020","journal-title":"Cardiovasc. Diagn. Ther."},{"key":"ref_265","doi-asserted-by":"crossref","unstructured":"Bihri, H., Nejjari, R., Azzouzi, S., and Charaf, M.E.H. (2021). An Artificial Neural Network-Based System to Predict Cardiovascular Disease. The International Conference on Information, Communication & Cybersecurity, Springer.","DOI":"10.1007\/978-3-030-91738-8_36"},{"key":"ref_266","doi-asserted-by":"crossref","first-page":"106190","DOI":"10.1016\/j.cmpb.2021.106190","article-title":"A review of risk prediction models in cardiovascular disease: Conventional approach vs. artificial intelligent approach","volume":"207","author":"Faizal","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_267","doi-asserted-by":"crossref","unstructured":"Suri, J.S., Paul, S., Maindarkar, M.A., Puvvula, A., Saxena, S., Saba, L., Turk, M., Laird, J.R., Khanna, N.N., and Viskovic, K. (2022). Cardiovascular\/Stroke Risk Stratification in Parkinson\u2019s Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review. Metabolites, 12.","DOI":"10.3390\/metabo12040312"},{"key":"ref_268","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.clinimag.2021.05.016","article-title":"Complications in COVID-19 patients: Characteristics of pulmonary embolism","volume":"77","author":"Cau","year":"2021","journal-title":"Clin. Imaging"},{"key":"ref_269","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1007\/s40622-021-00289-3","article-title":"The role of data-driven artificial intelligence on COVID-19 disease management in public sphere: A review","volume":"48","author":"Pillai","year":"2021","journal-title":"Decision"},{"key":"ref_270","doi-asserted-by":"crossref","unstructured":"Jelinek, H., and Cesar, R. (2003). Segmentation of Retinal Fundus Vasculature in Nonmydriatic Camera Images Using Wavelets. Angiogr. Plaque Imaging, 193\u2013224.","DOI":"10.1201\/9780203490907.ch5"},{"key":"ref_271","doi-asserted-by":"crossref","unstructured":"Paul, S., Maindarkar, M., Saxena, S., Saba, L., Turk, M., Kalra, M., Krishnan, P.R., and Suri, J.S. (2022). Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson\u2019s Disease: A Narrative Review. Diagnostics, 12.","DOI":"10.3390\/diagnostics12010166"},{"key":"ref_272","doi-asserted-by":"crossref","first-page":"e10","DOI":"10.1016\/S2589-7500(20)30250-8","article-title":"Predicting the risk of developing diabetic retinopathy using deep learning","volume":"3","author":"Bora","year":"2020","journal-title":"Lancet Digit. Health"},{"key":"ref_273","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1038\/s41551-018-0195-0","article-title":"Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning","volume":"2","author":"Poplin","year":"2018","journal-title":"Nat. Biomed. Eng."},{"key":"ref_274","doi-asserted-by":"crossref","unstructured":"Jain, P.K., Sharma, N., Saba, L., Paraskevas, K.I., Kalra, M.K., Johri, A., Laird, J.R., Nicolaides, A.N., and Suri, J.S. (2021). Unseen Artificial Intelligence\u2014Deep Learning Paradigm for Segmentation of Low Atherosclerotic Plaque in Carotid Ultrasound: A Multicenter Cardiovascular Study. Diagnostics, 11.","DOI":"10.3390\/diagnostics11122257"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/12\/5\/1234\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:10:52Z","timestamp":1760137852000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/12\/5\/1234"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,14]]},"references-count":274,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["diagnostics12051234"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics12051234","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,14]]}}}