{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T20:29:28Z","timestamp":1780086568844,"version":"3.54.0"},"reference-count":207,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T00:00:00Z","timestamp":1668816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD\/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD\/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD\/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD\/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD\/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD\/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD\/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD\/stroke in DFI patients.<\/jats:p>","DOI":"10.3390\/jcm11226844","type":"journal-article","created":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T04:33:32Z","timestamp":1669005212000},"page":"6844","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Cardiovascular\/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study"],"prefix":"10.3390","volume":"11","author":[{"given":"Narendra N.","family":"Khanna","sequence":"first","affiliation":[{"name":"Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0813-4906","authenticated-orcid":false,"given":"Mahesh A.","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":"Vijay","family":"Viswanathan","sequence":"additional","affiliation":[{"name":"MV Diabetes Centre, Royapuram, Chennai 600013, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6367-4067","authenticated-orcid":false,"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"}]},{"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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6804-5000","authenticated-orcid":false,"given":"Mrinalini","family":"Bhagawati","sequence":"additional","affiliation":[{"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, 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India"}],"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":"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"}]},{"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, 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Kapodistrian University of Athens, 15772 Athens, Greece"}],"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":"Kosmas I.","family":"Paraskevas","sequence":"additional","affiliation":[{"name":"Department of Vascular Surgery, Central Clinic of Athens, 15772 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5035-7396","authenticated-orcid":false,"given":"Durga Prasanna","family":"Misra","sequence":"additional","affiliation":[{"name":"Department of Immunology, SGPGIMS, 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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3912-7394","authenticated-orcid":false,"given":"Andrew","family":"Nicolaides","sequence":"additional","affiliation":[{"name":"Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Egkomi 2408, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijay","family":"Rathore","sequence":"additional","affiliation":[{"name":"AtheroPoint\u2122, Roseville, CA 95661, 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Delmenhorst, Germany"}],"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":"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\/0000-0002-5927-3201","authenticated-orcid":false,"given":"Klaudija","family":"Viskovic","sequence":"additional","affiliation":[{"name":"Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, 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Monitoring and Diagnostic Division, AtheroPoint\u2122, Roseville, CA 95661, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S181","DOI":"10.1002\/dmrr.848","article-title":"Practical guidelines on the management and prevention of the diabetic foot: Based upon the International Consensus on the Diabetic Foot (2007) Prepared by the International Working Group on the Diabetic Foot","volume":"24","author":"Apelqvist","year":"2008","journal-title":"Diabetes\/Metab. Res. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1719","DOI":"10.1016\/S0140-6736(05)67698-2","article-title":"The global burden of diabetic foot disease","volume":"366","author":"Boulton","year":"2005","journal-title":"Lancet"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.diabres.2011.06.016","article-title":"Molecular changes in diabetic foot ulcers","volume":"94","author":"Wang","year":"2011","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"ElTanboly, A., Shalaby, A., Mahmoud, A., Ghazal, M., Switala, A., Taher, F., Suri, J.S., Keynton, R., and El-Baz, A. (2019). Computer Aided Diagnosis System for Early Detection of Diabetic Retinopathy Using OCT Images. Big Data in Multimodal Medical Imaging, CRC Press.","DOI":"10.1201\/b22410-13"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"El-Baz, A., Gimel\u2019Farb, G., and Suri, J.S. (2015). Stochastic Modeling for Medical Image Analysis, CRC Press.","DOI":"10.1201\/b19253"},{"key":"ref_6","unstructured":"Centers for Disease Control and Prevention (2022). National Diabetes Fact Sheet: National Estimates and General Information on Diabetes and Prediabetes in the United States."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"American Diabetes Association (1999). Consensus Development Conference on Diabetic Foot Wound Care: 7\u20138 April 1999, Boston, Massachusetts. Diabetes Care, 22, 1354\u20131360.","DOI":"10.2337\/diacare.22.8.1354"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"513","DOI":"10.2337\/diacare.13.5.513","article-title":"Pathways to Diabetic Limb Amputation: Basis for Prevention","volume":"13","author":"Burgess","year":"1990","journal-title":"Diabetes Care"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1093\/qjmed\/hcn027","article-title":"The diabetic foot","volume":"101","author":"Khanolkar","year":"2008","journal-title":"QJM An. Int. J. Med."},{"key":"ref_10","first-page":"73","article-title":"Epidemiology of foot ulcers and amputations in the diabetic foot","volume":"71","author":"Reiber","year":"2001","journal-title":"Diabet. Foot"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1186\/s13098-017-0225-1","article-title":"Cardiovascular risk assessment in patients with diabetes","volume":"9","author":"Bertoluci","year":"2017","journal-title":"Diabetol. Metab. Syndr."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1007\/s40200-021-00761-y","article-title":"Cardiovascular diseases risk prediction in patients with diabetes: Posthoc analysis from a matched case-control study in Bangladesh","volume":"20","author":"Islam","year":"2021","journal-title":"J. Diabetes Metab. Disord."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1016\/j.jdiacomp.2015.12.018","article-title":"Diabetes mellitus: The linkage between oxidative stress, inflammation, hypercoagulability and vascular complications","volume":"30","author":"Domingueti","year":"2016","journal-title":"J. Diabetes Its Complicat."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2906","DOI":"10.1007\/s00125-012-2673-3","article-title":"The association of ulceration of the foot with cardiovascular and all-cause mortality in patients with diabetes: A meta-analysis","volume":"55","author":"Brownrigg","year":"2012","journal-title":"Diabetologia"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1007\/s11883-017-0680-z","article-title":"The Diabetic Foot as a Proxy for Cardiovascular Events and Mortality Review","volume":"19","author":"Dietrich","year":"2017","journal-title":"Curr. Atheroscler. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"268390","DOI":"10.1155\/2015\/268390","article-title":"Diabetic Foot Syndrome as a Possible Cardiovascular Marker in Diabetic Patients","volume":"2015","author":"Tuttolomondo","year":"2015","journal-title":"J. Diabetes Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1161\/01.CIR.74.5.1085","article-title":"Effect of a reduction in blood viscosity on maximal myocardial oxygen delivery distal to a moderate coronary stenosis","volume":"74","author":"Most","year":"1986","journal-title":"Circulation"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Munjral, S., Maindarkar, M., Ahluwalia, P., Puvvula, A., Jamthikar, A., Jujaray, T., Suri, N., Paul, S., Pathak, R., and Saba, L. (2022). Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19\/Non-COVID-19 Frameworks Using Artificial Intelligence Paradigm: A Narrative Review. Diagnostics, 12.","DOI":"10.3390\/diagnostics12051234"},{"key":"ref_20","first-page":"814","article-title":"The Role of Cutaneous Microcirculatory Responses in Tissue Injury, Inflammation and Repair at the Foot in Diabetes","volume":"28","author":"Balasubramanian","year":"2021","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1161\/ATVBAHA.110.213850","article-title":"Mechanisms of resolution of inflammation: A focus on cardio-vascular disease","volume":"31","author":"Maskrey","year":"2011","journal-title":"Arterioscler. Thromb. Vasc. Biol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e3565","DOI":"10.1002\/dmrr.3565","article-title":"Inflammation and vascular dysfunction: The negative synergistic combination of diabetes and COVID-19","volume":"38","author":"Bolla","year":"2022","journal-title":"Diabetes\/Metab. Res. Rev."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1007\/s00592-022-01932-w","article-title":"Below-the-ankle arterial disease: A new marker of coronary artery disease in patients with diabetes and foot ulcers","volume":"59","author":"Meloni","year":"2022","journal-title":"Acta Diabetol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mishra, S.C., Chhatbar, K.C., Kashikar, A., and Mehndiratta, A. (2017). Diabetic foot. BMJ, 359.","DOI":"10.1136\/bmj.j5064"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1016\/j.metabol.2008.01.004","article-title":"Cardiovascular risk profile and morbidity in subjects affected by type 2 diabetes mellitus with and without diabetic foot","volume":"57","author":"Pinto","year":"2008","journal-title":"Metabolism"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1186\/s12933-021-01337-z","article-title":"Assessment of heart rate variability (HRV) in subjects with type 2 diabetes mellitus with and without diabetic foot: Correlations with endothelial dysfunction indices and markers of adipo-inflammatory dysfunction","volume":"20","author":"Tuttolomondo","year":"2021","journal-title":"Cardiovasc. Diabetol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Al-Rubeaan, K., al Derwish, M., Ouizi, S., Youssef, A.M., Subhani, S.N., Ibrahim, H.M., and Alamri, B.N. (2015). Diabetic foot com-plications and their risk factors from a large retrospective cohort study. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0124446"},{"key":"ref_28","first-page":"171","article-title":"Diabetic infections: Clinical and bacteriological study at Tikur Anbessa Specialized University Hospital, Addis Ababa, Ethiopia","volume":"45","author":"Feleke","year":"2007","journal-title":"Ethiop. Med. J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.bbe.2022.03.001","article-title":"Fourier transform-based data augmentation in deep learning for diabetic foot ther-mograph classification","volume":"42","year":"2022","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1109\/TETCI.2018.2866254","article-title":"DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification","volume":"4","author":"Goyal","year":"2018","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s10916-017-0745-0","article-title":"Plaque Tissue Morphology-Based Stroke Risk Stratification Using Carotid Ultrasound: A Polling-Based PCA Learning Paradigm","volume":"41","author":"Saba","year":"2017","journal-title":"J. Med. Syst."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.compbiomed.2017.10.019","article-title":"Wall-based measurement features provides an improved IVUS coronary artery risk assessment when fused with plaque texture-based features during machine learning paradigm","volume":"91","author":"Banchhor","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_34","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 intra-vascular ultrasound: A link between carotid and coronary grayscale plaque morphology","volume":"128","author":"Araki","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.echo.2012.09.005","article-title":"Can Carotid Bulb Plaque Assessment Rule Out Significant Coronary Artery Disease? A Comparison of Plaque Quantification by Two- and Three-Dimensional Ultrasound","volume":"26","author":"Johri","year":"2013","journal-title":"J. Am. Soc. Echocardiogr."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.ejrad.2019.02.038","article-title":"The present and future of deep learning in radiology","volume":"114","author":"Saba","year":"2019","journal-title":"Eur. J. Radiol."},{"key":"ref_37","unstructured":"Ayman, J.S.S., and El-Baz, S. (2021). Machine Learning in Medicine\u2014Chapman & Hall\/CRC Healthcare Informatics Series, CRC Press."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.cmpb.2017.09.004","article-title":"Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm","volume":"152","author":"Maniruzzaman","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1007\/s10916-017-0797-1","article-title":"Extreme Learning Machine Framework for Risk Stratification of Fatty Liver Disease Using Ultrasound Tissue Characterization","volume":"41","author":"Kuppili","year":"2017","journal-title":"J. Med. Syst."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.cmpb.2017.07.011","article-title":"A novel and robust Bayesian approach for segmentation of psoriasis lesions and its risk stratification","volume":"150","author":"Shrivastava","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_44","first-page":"1805","article-title":"Moving beyond regression techniques in cardiovascular risk prediction: Ap-plying machine learning to address analytic challenges","volume":"38","author":"Goldstein","year":"2016","journal-title":"Eur. Heart J."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Khanna, N.N., Maindarkar, M., Puvvula, A., Paul, S., Bhagawati, M., Ahluwalia, P., Ruzsa, Z., Sharma, A., Munjral, S., and Kolluri, R. (2022). Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report. J. Cardiovasc. Dev. Dis., 9.","DOI":"10.3390\/jcdd9080268"},{"key":"ref_46","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_47","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_48","doi-asserted-by":"crossref","unstructured":"Khanna, N.N., Maindarkar, M., Saxena, A., Ahluwalia, P., Paul, S., Srivastava, S.K., Cuadrado-Godia, E., Sharma, A., Omerzu, T., and Saba, L. (2022). Cardiovascular\/Stroke Risk Assessment in Patients with Erectile Dysfunction\u2014A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review. Diagnostics, 12.","DOI":"10.3390\/diagnostics12051249"},{"key":"ref_49","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_50","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_51","doi-asserted-by":"crossref","first-page":"15655","DOI":"10.1007\/s11042-019-07820-w","article-title":"DFU_QUTNet: Diabetic foot ulcer classification using novel deep convolutional neural network","volume":"79","author":"Alzubaidi","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_52","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_53","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_54","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_55","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_56","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_57","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1097\/WON.0000000000000856","article-title":"Influence of Foot Ulceration on All-Cause and Cardiovascular Mortality in Diabetic Patients: A Case-Control Study","volume":"49","author":"Qiu","year":"2022","journal-title":"J. Wound Ostomy Cont. Nurs."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"4499","DOI":"10.2147\/IJGM.S323015","article-title":"Associations of Estimated Glomerular Filtration Rate with All-Cause Mortality and Cardiovascular Mortality in Patients with Diabetic Foot Osteomyelitis","volume":"14","author":"Huang","year":"2021","journal-title":"Int. J. Gen. Med."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1161\/ATVBAHA.116.306256","article-title":"Pathology of Human Coronary and Carotid Artery Atherosclerosis and Vascular Calcification in Diabetes Mellitus","volume":"37","author":"Yahagi","year":"2017","journal-title":"Arter. Thromb. Vasc. Biol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2606","DOI":"10.1111\/j.1743-6109.2011.02365.x","article-title":"Relationship between erectile dysfunction and silent myocardial ischemia in type 2 diabetic pa-tients with no known macrovascular complications","volume":"8","author":"Jover","year":"2011","journal-title":"J. Sex. Med."},{"key":"ref_61","first-page":"266","article-title":"Ischemic stroke in patients with diabetic foot","volume":"26","author":"Pinto","year":"2007","journal-title":"Int. Angiol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1136\/bmj.322.7299.1389","article-title":"Excess mortality in a population with diabetes and the impact of material deprivation: Longitudinal, population based study","volume":"322","author":"Roper","year":"2001","journal-title":"BMJ"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/S0002-9149(99)00635-9","article-title":"Elevated systolic blood pressure as a cardiovascular risk factor","volume":"85","author":"Kannel","year":"2000","journal-title":"Am. J. Cardiol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1111\/j.1463-1326.2006.00594.x","article-title":"Cardiovascular metabolic syndrome\u2013an interplay of, obesity, inflamma-tion, diabetes and coronary heart disease","volume":"9","author":"Rana","year":"2007","journal-title":"Diabetes Obes. Metab."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2058","DOI":"10.1016\/S0140-6736(05)67029-8","article-title":"The role of proinflammatory cytokines in the cause of neuropathic osteoar-thropathy (acute Charcot foot) in diabetes","volume":"366","author":"Jeffcoate","year":"2005","journal-title":"Lancet"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.2337\/dc08-2318","article-title":"Immune Mediators in Patients With Acute Diabetic Foot Syndrome","volume":"32","author":"Weigelt","year":"2009","journal-title":"Diabetes Care"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/j.exer.2007.06.013","article-title":"Pro-inflammatory cytokines increase reactive oxygen species through mitochondria and NADPH oxidase in cultured RPE cells","volume":"85","author":"Yang","year":"2007","journal-title":"Exp. Eye Res."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1161\/ATVBAHA.120.314557","article-title":"Endothelial HMGB1 Is a Critical Regulator of LDL Transcytosis via an SREBP2\u2013SR-BI Axis","volume":"41","author":"Ghaffari","year":"2020","journal-title":"Arter. Thromb. Vasc. Biol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1038\/nri3520","article-title":"Macrophages in atherosclerosis: A dynamic balance","volume":"13","author":"Moore","year":"2013","journal-title":"Nat. Rev. Immunol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.cca.2013.06.006","article-title":"Foam cells in atherosclerosis","volume":"424","author":"Yu","year":"2013","journal-title":"Clin. Chim. Acta"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.1073\/pnas.86.4.1372","article-title":"Low density lipoprotein undergoes oxidative modification in vivo","volume":"86","author":"Palinski","year":"1989","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_72","first-page":"103","article-title":"George Lyman Duff memorial lecture. Progression and regression of atherosclerotic lesions. Insights from lipid physical biochemistry","volume":"8","author":"Small","year":"1988","journal-title":"Arter. Off. J. Am. Heart Assoc. Inc."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"S12","DOI":"10.1038\/sj.cdd.4401444","article-title":"Apoptosis and plaque destabilization in atherosclerosis: The role of macrophage apoptosis induced by cholesterol","volume":"11","author":"Atherogenesis","year":"2004","journal-title":"Cell Death Differ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"714653","DOI":"10.1155\/2013\/714653","article-title":"The Role of Oxidized Low-Density Lipoproteins in Atherosclerosis: The Myths and the Facts","volume":"2013","author":"Maiolino","year":"2013","journal-title":"Mediat. Inflamm."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1007\/s00125-014-3462-y","article-title":"Epigenetic mechanisms in diabetic complications and metabolic memory","volume":"58","author":"Reddy","year":"2014","journal-title":"Diabetologia"},{"key":"ref_76","first-page":"ra-18","article-title":"Role of hyperglycemia-induced advanced glycation end product (AGE) accumulation in atherosclerosis","volume":"67","author":"Yamagishi","year":"2018","journal-title":"Ann. Vasc. Dis."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1515\/cclm-2012-0886","article-title":"Augmentation of blood lipid glycation and lipid oxidation in diabetic patients","volume":"52","author":"Suzuki","year":"2014","journal-title":"Clin. Chem. Lab. Med. (CCLM)"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"6396","DOI":"10.3390\/ijms23126396","article-title":"The Role of Pol-yphenol in Modulating Associated Genes in Diabetes-Induced Vascular Disorders","volume":"23","author":"Nor","year":"2022","journal-title":"Int. J. Mol. Sci."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Barbu, E., Popescu, M.-R., Popescu, A.-C., and Balanescu, S.-M. (2022). Inflammation as A precursor of atherothrombosis, diabetes and early vascular aging. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms23020963"},{"key":"ref_80","first-page":"318","article-title":"Resolution of inflammation: Therapeutic potential of pro-resolving lipids in type 2 diabetes mellitus and associated renal complications","volume":"3","author":"Godson","year":"2012","journal-title":"Front. Immunol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"112579","DOI":"10.1016\/j.biopha.2021.112579","article-title":"Activated monocytes as a therapeutic target to attenuate vascular inflammation and lower cardiovascular disease-risk in patients with type 2 diabetes: A systematic review of preclinical and clinical studies","volume":"146","author":"Ngcobo","year":"2021","journal-title":"Biomed. Pharmacother."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"3066","DOI":"10.2337\/db10-0054","article-title":"Transgenic Restoration of Long-Chain n-3 Fatty Acids in Insulin Target Tissues Improves Resolution Capacity and Alleviates Obesity-Linked Inflammation and Insulin Resistance in High-Fat\u2013Fed Mice","volume":"59","author":"White","year":"2010","journal-title":"Diabetes"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"653789","DOI":"10.1155\/2013\/653789","article-title":"Impact of Diabetes on Cardiovascular Disease: An Update","volume":"2013","author":"Matheus","year":"2013","journal-title":"Int. J. Hypertens."},{"key":"ref_84","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_85","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4239\/wjd.v9.i1.1","article-title":"Cardiac autonomic neuropathy: Risk factors, diagnosis and treatment","volume":"9","author":"Serhiyenko","year":"2018","journal-title":"World J. Diabetes"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"e13441","DOI":"10.1111\/jsr.13441","article-title":"Diabetes and cardiovascular diseases are associated with the worsening of intermittent hypoxaemia","volume":"31","author":"Karhu","year":"2021","journal-title":"J. Sleep Res."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"S123","DOI":"10.1055\/a-1624-3593","article-title":"Diabetes mellitus and the heart","volume":"130","author":"Schuett","year":"2022","journal-title":"Exp. Clin. Endocrinol. Diabetes"},{"key":"ref_88","unstructured":"Winston, P.H. (1992). Artificial Intelligence, Addison-Wesley Longman Publishing Co., Inc."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1308\/147870804290","article-title":"Artificial intelligence in medicine","volume":"86","author":"Ramesh","year":"2004","journal-title":"Ann. R. Coll. Surg. Engl."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Jain, P.K., Sharma, N., Kalra, M.K., Viskovic, K., Saba, L., and Suri, J.S. (2022). Four Types of Multiclass Frameworks for Pneumonia Classification and Its Validation in X-ray Scans Using Seven Types of Deep Learning Artificial Intelligence Models. Diagnostics, 12.","DOI":"10.3390\/diagnostics12030652"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1007\/s11548-021-02317-0","article-title":"Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs","volume":"16","author":"Saba","year":"2021","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1007\/s11517-018-1897-x","article-title":"Deep learning fully convolution network for lumen characterization in diabetic patients using carotid ultrasound: A tool for stroke risk","volume":"57","author":"Biswas","year":"2018","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1007\/s10278-019-00227-x","article-title":"Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges","volume":"32","author":"Hesamian","year":"2019","journal-title":"J. Digit. Imaging"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.jormas.2019.06.002","article-title":"Deep learning in medical image analysis: A third eye for doctors","volume":"120","author":"Fourcade","year":"2019","journal-title":"J. Stomatol. Oral Maxillofac. Surg."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/s00296-021-05062-4","article-title":"Cardiovascular disease detection using machine learning and carotid\/femoral arterial imaging frameworks in rheumatoid arthritis patients","volume":"42","author":"Konstantonis","year":"2022","journal-title":"Rheumatol. Int."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"106018","DOI":"10.1016\/j.compbiomed.2022.106018","article-title":"Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization","volume":"150","author":"Johri","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_97","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_98","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_99","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_100","doi-asserted-by":"crossref","first-page":"104838","DOI":"10.1016\/j.compbiomed.2021.104838","article-title":"A machine learning model for early detection of diabetic foot using thermogram images","volume":"137","author":"Khandakar","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Dinh, A., Miertschin, S., Young, A., and Mohanty, S.D. (2019). A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. BMC Med. Inform. Decis. Mak., 19.","DOI":"10.1186\/s12911-019-0918-5"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Kim, H., Lim, D., and Kim, Y. (2021). Classification and Prediction on the Effects of Nutritional Intake on Overweight\/Obesity, Dyslipidemia, Hypertension and Type 2 Diabetes Mellitus Using Deep Learning Model: 4\u20137th Korea National Health and Nutrition Examination Survey. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18115597"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"3608","DOI":"10.1109\/JBHI.2021.3065756","article-title":"A Deep Learning Approach to Predict Diabetes\u2019 Cardiovascular Complications From Administrative Claims","volume":"25","author":"Longato","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Goyal, M., Yap, M.H., Reeves, N.D., Rajbhandari, S., and Spragg, J. (2017, January 5\u20138). Fully convolutional networks for diabetic foot ulcer seg-mentation. Proceedings of the 2017 IEEE International conference on Systems, Man, and Cybernetics (SMC), Banff, AB, Canada.","DOI":"10.1109\/SMC.2017.8122675"},{"key":"ref_105","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_106","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.jneumeth.2007.08.023","article-title":"Ambulatory monitoring of freezing of gait in Parkinson\u2019s disease","volume":"167","author":"Moore","year":"2008","journal-title":"J. Neurosci. Methods"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1080\/00207721.2012.724114","article-title":"New machine-learning algorithms for prediction of Parkinson\u2019s disease","volume":"45","author":"Mandal","year":"2012","journal-title":"Int. J. Syst. Sci."},{"key":"ref_108","first-page":"1568","article-title":"Intelligent Parkinson disease prediction using machine learning algorithms","volume":"3","author":"Sriram","year":"2013","journal-title":"Int. J. Eng. Innov. Technol. (IJEIT)"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Pereira, C.R., Pereira, D.R., da Silva, F.A., Hook, C., Weber, S.A., Pereira, L.A., and Papa, J.P. (2015, January 22\u201325). A Step Towards the Automated Diagnosis of Parkinson\u2019s Disease: Analyzing Handwriting Movements. Proceedings of the 2015 IEEE 28th International Symposium on Computer-Based Medical Systems, Ribeir\u00e3o Preto, Brazil.","DOI":"10.1109\/CBMS.2015.34"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Adams, W.R. (2017). High-accuracy detection of early Parkinson\u2019s Disease using multiple characteristics of finger movement while typing. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0188226"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"1921","DOI":"10.1007\/s00296-020-04691-5","article-title":"Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging","volume":"40","author":"Jamthikar","year":"2020","journal-title":"Rheumatol. Int."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Sharma, N., Saba, L., Khanna, N.N., Kalra, M.K., Fouda, M.M., and Suri, J.S. (2022). Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans. Diagnostics, 12.","DOI":"10.3390\/diagnostics12092132"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.2741\/4850","article-title":"Low-cost preventive screening using carotid ultrasound in patients with diabetes","volume":"25","author":"Viswanathan","year":"2020","journal-title":"Front. Biosci. Landark"},{"key":"ref_114","first-page":"404","article-title":"Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: A narrative review of in-tegrated approaches using carotid ultrasound","volume":"12","author":"Jamthikar","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.procs.2020.11.029","article-title":"Machine Learning-Based Predictive Modeling of Complications of Chronic Diabetes","volume":"178","author":"Derevitskii","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.1016\/j.bbe.2020.05.001","article-title":"Heart rate variability features from nonlinear cardiac dynamics in identification of diabetes using artificial neural network and support vector machine","volume":"40","author":"Aggarwal","year":"2020","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Suri, J.S., Maindarkar, M.A., Paul, S., Ahluwalia, P., Bhagawati, M., Saba, L., Faa, G., Saxena, S., Singh, I.M., and Chadha, P.S. (2022). Deep Learning Paradigm for Cardiovascular Disease\/Stroke Risk Stratification in Parkinson\u2019s Disease Affected by COVID-19: A Narrative Review. Diagnostics, 12.","DOI":"10.3390\/diagnostics12071543"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1007\/s10278-021-00461-2","article-title":"A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular\/Stroke Risk Monitoring: Artificial Intelligence Framework","volume":"34","author":"Biswas","year":"2021","journal-title":"J. Digit. Imaging"},{"key":"ref_119","first-page":"9","article-title":"Automated deep learning-based paradigm for high-risk plaque detection in B-mode common carotid ultrasound scans: An asymptomatic Jap-anese cohort study","volume":"41","author":"Jain","year":"2021","journal-title":"Int. Angiol. A J. Int. Union Angiol."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Sanagala, S.S., Nicolaides, A., Gupta, S.K., Koppula, V.K., Saba, L., Agarwal, S., Johri, A.M., Kalra, M.S., and Suri, J.S. (2021). Ten Fast Transfer Learning Models for Carotid Ultrasound Plaque Tissue Characterization in Augmentation Framework Embedded with Heatmaps for Stroke Risk Stratification. Diagnostics, 11.","DOI":"10.3390\/diagnostics11112109"},{"key":"ref_121","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 multi-center cardiovascular study. Diagnostics, 11.","DOI":"10.3390\/diagnostics11122257"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"6184","DOI":"10.1016\/j.eswa.2015.03.014","article-title":"Reliable and accurate psoriasis disease classification in dermatology images using comprehensive feature space in machine learning paradigm","volume":"42","author":"Shrivastava","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"105639","DOI":"10.1016\/j.compbiomed.2022.105639","article-title":"NeoAI 1.0: Machine learning-based paradigm for prediction of neonatal and infant risk of death","volume":"147","author":"Teji","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"105102","DOI":"10.1016\/j.compbiomed.2021.105102","article-title":"A machine learning framework for risk prediction of multi-label cardiovascular events based on focused carotid plaque B-Mode ultrasound: A Canadian study","volume":"140","author":"Jamthikar","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.cmpb.2012.09.008","article-title":"Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization","volume":"110","author":"Acharya","year":"2013","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"541","DOI":"10.31083\/j.rcm.2020.04.236","article-title":"In-tegration of cardiovascular risk assessment with COVID-19 using artificial intelligence","volume":"21","author":"Suri","year":"2020","journal-title":"Rev. Cardiovasc. Med."},{"key":"ref_127","first-page":"25","article-title":"Applying Machine Learning Methods in Diagnosing Heart Disease for Diabetic Patients","volume":"3","author":"Parthiban","year":"2012","journal-title":"Int. J. Appl. Inf. Syst."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4172\/2475-3211.1000108","article-title":"Machine Learning Methods for Automated Detection of Severe Diabetic Neuropathy","volume":"1","author":"Jelinek","year":"2016","journal-title":"J. Diabet. Complicat. Med."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1109\/JBHI.2017.2765639","article-title":"Comparison of Machine Learning Approaches Toward Assessing the Risk of Developing Cardiovascular Disease as a Long-Term Diabetes Complication","volume":"22","author":"Zarkogianni","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"604","DOI":"10.2337\/dc17-2252","article-title":"Characteristics Associated With Decreased or Increased Mortality Risk From Glycemic Therapy Among Patients With Type 2 Diabetes and High Cardiovascular Risk: Machine Learning Analysis of the ACCORD Trial","volume":"41","author":"Basu","year":"2017","journal-title":"Diabetes Care"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"2298","DOI":"10.2337\/dc19-0587","article-title":"Machine Learning to Predict the Risk of Incident Heart Failure Hospitalization Among Patients With Diabetes: The WATCH-DM Risk Score","volume":"42","author":"Segar","year":"2019","journal-title":"Diabetes Care"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"113918","DOI":"10.1016\/j.eswa.2020.113918","article-title":"Network analytics and machine learning for predictive risk modelling of cardiovas-cular disease in patients with type 2 diabetes","volume":"164","author":"Hossain","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1186\/s13054-020-03179-9","article-title":"Prediction of the development of acute kidney injury following cardiac surgery by machine learning","volume":"24","author":"Tseng","year":"2020","journal-title":"Crit. Care"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1109\/ICDAR.1995.598994","article-title":"Random decision forests","volume":"Volume 1","author":"Ho","year":"1995","journal-title":"Proceedings of the 3rd International Conference on Document Analysis and Recognition"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"962","DOI":"10.4103\/1673-5374.233433","article-title":"How random is the random forest? Random forest algorithm on the service of structural imaging biomarkers for Alzheimer\u2019s disease: From Alzheimer\u2019s disease neuroimaging initiative (ADNI) database","volume":"13","author":"Dimitriadis","year":"2018","journal-title":"Neural Regen. Res."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1021\/acs.jcim.6b00753","article-title":"Comparison of the predictive performance and in-terpretability of random forest and linear models on benchmark data sets","volume":"57","author":"Robinson","year":"2017","journal-title":"J. Chem. Inf. Model."},{"key":"ref_137","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_138","doi-asserted-by":"crossref","unstructured":"Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1985). Learning Internal Representations by Error Propagation, California Univ San Diego La Jolla Inst for Cognitive Science.","DOI":"10.21236\/ADA164453"},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Durstewitz, D. (2017). A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements. PLOS Comput. Biol., 13.","DOI":"10.1371\/journal.pcbi.1005542"},{"key":"ref_140","unstructured":"Razaghi, H.S., and Paninski, L. (2019, January 13). Filtering normalizing flows. Proceedings of the Bayesian Deep Learning Workshop at NeurIPS, Vancouver, BC, Canada."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"15965","DOI":"10.1007\/s00521-019-04650-7","article-title":"Brain tumor detection: A long short-term memory (LSTM)-based learning model","volume":"32","author":"Amin","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"An, Y., Tang, K., and Wang, J. (2021). Time-Aware Multi-Type Data Fusion Representation Learning Framework for Risk Prediction of Cardiovascular Diseases. IEEE\/ACM Trans. Comput. Biol. Bioinform.","DOI":"10.1109\/TCBB.2021.3118418"},{"key":"ref_144","first-page":"1","article-title":"Toward real-time and efficient cardiovascular monitoring for COVID-19 patients by 5G-enabled wearable medical devices: A deep learning approach","volume":"4","author":"Tan","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1111\/coin.12405","article-title":"A hybrid recurrent neural network - logistic chaos-based whale optimization framework for heart disease prediction with electronic health records","volume":"37","author":"Priyanga","year":"2020","journal-title":"Comput. Intell."},{"key":"ref_146","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_147","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1080\/09593985.2019.1683922","article-title":"Low back pain and calf pain in a recreational runner masking peripheral artery disease: A case report","volume":"37","author":"Brindisino","year":"2021","journal-title":"Physiother. Theory Pract."},{"key":"ref_148","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_149","doi-asserted-by":"crossref","unstructured":"Sanches, J.M., Laine, A.F., and Suri, J.S. (2012). Ultrasound Imaging, Springer.","DOI":"10.1007\/978-1-4614-1180-2"},{"key":"ref_150","first-page":"222","article-title":"Texture analysis and its applications in biomedical imaging: A survey","volume":"15","author":"Khaksar","year":"2021","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_151","first-page":"1045","article-title":"An accurate and generalized approach to plaque characterization in 346 carotid ultrasound scans","volume":"61","author":"Sree","year":"2011","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_152","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_153","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1161\/HYPERTENSIONAHA.121.18444","article-title":"Carotid Artery Stiffening With Aging: Structural Versus Load-Dependent Mechanisms in MESA (the Multi-Ethnic Study of Atherosclerosis)","volume":"79","author":"Pewowaruk","year":"2022","journal-title":"Hypertension"},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"El-Baz, A.S., Acharya, R., Mirmehdi, M., and Suri, J.S. (2011). Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies, Springer Science & Business Media.","DOI":"10.1007\/978-1-4419-8195-0"},{"key":"ref_155","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_156","doi-asserted-by":"crossref","first-page":"105204","DOI":"10.1016\/j.compbiomed.2021.105204","article-title":"Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review","volume":"142","author":"Suri","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.cmpb.2016.07.009","article-title":"Five multiresolution-based calcium volume measurement techniques from coronary IVUS videos: A comparative approach","volume":"134","author":"Banchhor","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_158","first-page":"TC09","article-title":"Relationship between automated coronary calcium volumes and a set of manual coronary lumen volume, vessel volume and atheroma volume in japanese diabetic cohort","volume":"11","author":"Banchhor","year":"2017","journal-title":"J. Clin. Diagn. Res. JCDR"},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.jacc.2022.04.055","article-title":"Implications of ethnicity in subclinical atherosclerosis in underserved populations: Inadequacy of coronary risk score models","volume":"80","author":"Vyas","year":"2022","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"104803","DOI":"10.1016\/j.compbiomed.2021.104803","article-title":"Artificial intelligence-based hybrid deep learning models for image classification: The first narrative review","volume":"137","author":"Jena","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"105131","DOI":"10.1016\/j.compbiomed.2021.105131","article-title":"A hybrid deep learning paradigm for carotid plaque tissue characterization and its validation in multicenter cohorts using a supercomputer framework","volume":"141","author":"Skandha","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_162","doi-asserted-by":"crossref","unstructured":"Suri, J., Agarwal, S., Pathak, R., Ketireddy, V., Columbu, M., Saba, L., Gupta, S., Faa, G., Singh, I., and Turk, M. (2021). COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models. Diagnostics, 11.","DOI":"10.3390\/diagnostics11081405"},{"key":"ref_163","first-page":"1006","article-title":"Automatic segmentation of diabetic foot ulcer from mask region-based convolu-tional neural networks","volume":"1","author":"Montalvo","year":"2020","journal-title":"J. Biomed. Res. Clin. Investig."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1177\/1932296815599004","article-title":"An automatic assessment system of diabetic foot ulcers based on wound area determination, color segmentation, and healing score evaluation","volume":"10","author":"Wang","year":"2016","journal-title":"J. Diabetes Sci. Tech-Nology"},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Jawahar, M., Anbarasi, L.J., Jasmine, S.G., and Narendra, M. (2020, January 10\u201312). Diabetic foot ulcer segmentation using color space models. Proceedings of the 2020 5th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India.","DOI":"10.1109\/ICCES48766.2020.9138024"},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1007\/s10554-020-02124-9","article-title":"Ultrasound-based internal carotid artery plaque characterization using deep learning paradigm on a supercomputer: A cardiovascular disease\/stroke risk assessment system","volume":"37","author":"Saba","year":"2021","journal-title":"Int. J. Cardiovasc. Imaging"},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.cmpb.2014.11.006","article-title":"A comparative approach of four different image registration techniques for quantitative assessment of coronary artery calcium lesions using intravascular ultrasound","volume":"118","author":"Araki","year":"2015","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"2005","DOI":"10.21037\/cdt-20-156","article-title":"Cardiac computed tomography radi-omics: An emerging tool for the non-invasive assessment of coronary atherosclerosis","volume":"10","author":"Murgia","year":"2020","journal-title":"Cardiovasc. Diagn. Ther."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.ejrad.2017.01.031","article-title":"Relationship between white matter hyperintensities volume and the circle of Willis configurations in patients with carotid artery pathology","volume":"89","author":"Saba","year":"2017","journal-title":"Eur. J. Radiol."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"103804","DOI":"10.1016\/j.compbiomed.2020.103804","article-title":"Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm","volume":"122","author":"Tandel","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"105273","DOI":"10.1016\/j.compbiomed.2022.105273","article-title":"An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review","volume":"143","author":"Das","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_172","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_173","doi-asserted-by":"crossref","unstructured":"Suri, J.S., Agarwal, S., Carriero, A., Pasch\u00e8, A., Danna, P.S., Columbu, M., Saba, L., Viskovic, K., Mehmedovi\u0107, A., and Agarwal, S. (2021). COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomog-raphy Lung Segmentation in Italian and Croatian Cohorts. Diagnostics, 11.","DOI":"10.3390\/diagnostics11122367"},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1086\/424846","article-title":"Diagnosis and treatment of diabetic foot infections","volume":"1","author":"Lipsky","year":"2004","journal-title":"Clin. Infect. Dis."},{"key":"ref_175","first-page":"61","article-title":"Effect of extensive debridement and treatment on the healing of diabetic foot ulcers. Diabetic Ulcer Study Group","volume":"183","author":"Steed","year":"1996","journal-title":"J. Am. Coll. Surg."},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1046\/j.1464-5491.1999.00053.x","article-title":"Digital imaging: An accurate and easy method of measuring foot ulcers","volume":"16","author":"Rajbhandari","year":"1999","journal-title":"Diabet. Med."},{"key":"ref_177","doi-asserted-by":"crossref","unstructured":"Kolesnik, M., and Fexa, A. (2005). Multi-dimensional Color Histograms for Segmentation of Wounds in Images. International Conference Image Analysis and Recognition, Springer.","DOI":"10.1007\/11559573_123"},{"key":"ref_178","doi-asserted-by":"crossref","unstructured":"Kolesnik, M., and Fexa, A. (2006, January 7\u20139). How robust is the SVM wound segmentation?. Proceedings of the 7th Nordic Signal. Processing Symposium-NORSIG, Piscataway, NJ, USA.","DOI":"10.1109\/NORSIG.2006.275274"},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1111\/j.1524-475X.2010.00594.x","article-title":"Image analysis of chronic wounds for determining the surface area","volume":"18","author":"Papazoglou","year":"2010","journal-title":"Wound Repair Regen."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1109\/TMI.2009.2033595","article-title":"Binary tissue classification on wound images with neural networks and bayesian clas-sifiers","volume":"29","author":"Veredas","year":"2009","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_181","doi-asserted-by":"crossref","unstructured":"Alzubaidi, L., Fadhel, M.A., Al-Shamma, O., Zhang, J., Santamar\u00eda, J., Duan, Y., and Oleiwi, S.R. (2020). Towards a better under-standing of transfer learning for medical imaging: A case study. Appl. Sci., 10.","DOI":"10.3390\/app10134523"},{"key":"ref_182","doi-asserted-by":"crossref","unstructured":"Sarp, S., Kuzlu, M., Wilson, E., Cali, U., and Guler, O. (2021). The Enlightening Role of Explainable Artificial Intelligence in Chronic Wound Classification. Electronics, 10.","DOI":"10.20944\/preprints202101.0346.v1"},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1007\/s11517-021-02322-0","article-title":"Wilson disease tissue classification and characterization using seven artificial intelligence models embedded with 3D opti-mization paradigm on a weak training brain magnetic resonance imaging datasets: A supercomputer application","volume":"59","author":"Agarwal","year":"2021","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"220","DOI":"10.3389\/fnagi.2019.00220","article-title":"Deep Learning in Alzheimer\u2019s Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data","volume":"11","author":"Jo","year":"2019","journal-title":"Front. Aging Neurosci."},{"key":"ref_185","first-page":"1107","article-title":"Learning a Metric for Multimodal Medical Image Registration without Supervision Based on Cycle Constraints","volume":"3","author":"Hanna","year":"2022","journal-title":"Sensors"},{"key":"ref_186","doi-asserted-by":"crossref","unstructured":"Saba, L., Sanagala, S.S., Gupta, S.K., Koppula, V.K., Johri, A.M., Khanna, N.N., Mavrogeni, S., Laird, J.R., Pareek, G., and Miner, M. (2021). Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: A nar-rative review for stroke application. Ann. Transl. Med., 9.","DOI":"10.21037\/atm-20-7676"},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"101","DOI":"10.2337\/dc16-1400","article-title":"Role of Type 2 Diabetes in Determining Retinal, Renal, and Cardiovascular Outcomes in Women With Previous Gestational Diabetes Mellitus","volume":"40","author":"Retnakaran","year":"2016","journal-title":"Diabetes Care"},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/s13410-020-00866-9","article-title":"Managing diabetic foot in times of COVID-19: Time to put the best \u2018foot\u2019forward","volume":"40","author":"Atri","year":"2020","journal-title":"Int. J. Diabetes Dev. Ctries."},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1007\/s12020-012-9619-x","article-title":"Diagnostics and treatment of the diabetic foot","volume":"41","author":"Apelqvist","year":"2012","journal-title":"Endocrine"},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"103960","DOI":"10.1016\/j.compbiomed.2020.103960","article-title":"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","volume":"124","author":"Suri","year":"2020","journal-title":"Comput. Biol."},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.2337\/dc20-1176","article-title":"Saving the Diabetic Foot During the COVID-19 Pandemic: A Tale of Two Cities","volume":"43","author":"Shin","year":"2020","journal-title":"Diabetes Care"},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1111\/iwj.13416","article-title":"Recommendations for management of diabetic foot ulcers during COVID-19 outbreak","volume":"17","author":"Kelahmetoglu","year":"2020","journal-title":"Int. Wound J."},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.dsx.2020.06.023","article-title":"Redefining diabetic foot disease management service during COVID-19 pandemic","volume":"14","author":"Jaly","year":"2020","journal-title":"Diabetes Metab. Syndr. Clin. Res. Rev."},{"key":"ref_194","doi-asserted-by":"crossref","unstructured":"Boulton, A. (2021). Diabetic Foot Disease during the COVID-19 Pandemic. Medicina, 57.","DOI":"10.3390\/medicina57020097"},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"107622","DOI":"10.1016\/j.jdiacomp.2020.107622","article-title":"Surgical treatment of diabetic foot ulcers during the COVID-19 pandemic in China","volume":"34","author":"Tao","year":"2020","journal-title":"J. Diabetes Its Complicat."},{"key":"ref_196","first-page":"e27","article-title":"Diabetic Foot Care Before and During the COVID-19 Epidemic: What Really Matters?","volume":"44","year":"2020","journal-title":"Diabetes Care"},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"e14577","DOI":"10.1111\/dme.14577","article-title":"Impact of the COVID-19 Epidemic on hospi-talization for diabetic foot ulcers during lockdown: A French nationwide population-based study","volume":"38","author":"Mariet","year":"2021","journal-title":"Diabet. Med."},{"key":"ref_198","doi-asserted-by":"crossref","unstructured":"Suri, J.S., and Rangayyan, R.M. (2006). Breast Imaging, Mammography, and Computer-Aided Diagnosis of Breast Cancer, SPIE.","DOI":"10.1117\/3.651880"},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1007\/s11517-006-0119-0","article-title":"Heart rate variability: A review","volume":"44","author":"Acharya","year":"2006","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_200","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\u2019sDisease: A Narrative Review. Diagnostics, 12.","DOI":"10.3390\/diagnostics12010166"},{"key":"ref_201","first-page":"140","article-title":"Five Strategies for Bias Estimation in Artificial Intelligence-based Hybrid Deep Learning for Acute Respiratory Distress Syn-drome COVID-19 Lung Infected Patients using AP(ai)Bias 2.0: A Systematic Review","volume":"27","author":"Suri","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1016\/S0140-6736(96)07492-2","article-title":"Alternative projections of mortality and disability by cause 1990\u20132020: Global Burden of Disease Study","volume":"349","author":"Murray","year":"1997","journal-title":"Lancet"},{"key":"ref_203","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_204","unstructured":"Khalifa, F., Beache, G.M., Gimel\u2019farb, G., Suri, J.S., and El-Baz, A. (2011). State-of-the-Art Medical Image, Springer."},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"105571","DOI":"10.1016\/j.compbiomed.2022.105571","article-title":"Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0","volume":"146","author":"Agarwal","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_206","first-page":"237","article-title":"Evolutionary algorithm-based classifier parameter tuning for automatic ovarian cancer tissue characterization and classification","volume":"35","author":"Acharya","year":"2014","journal-title":"Ultraschall Med. -Eur. J. Ultrasound"},{"key":"ref_207","doi-asserted-by":"crossref","first-page":"2967","DOI":"10.1109\/JBHI.2021.3060163","article-title":"Deep Learning-Based Measurement of Total Plaque Area in B-Mode Ultrasound Images","volume":"25","author":"Zhou","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/11\/22\/6844\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:22:04Z","timestamp":1760145724000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/11\/22\/6844"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,19]]},"references-count":207,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["jcm11226844"],"URL":"https:\/\/doi.org\/10.3390\/jcm11226844","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,19]]}}}