{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T14:06:04Z","timestamp":1780668364221,"version":"3.54.1"},"reference-count":69,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T00:00:00Z","timestamp":1628035200000},"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>Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint\u2122, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value &lt; 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet &gt; VGG-SegNet &gt; NIH &gt; SegNet. The HDL runs in &lt;1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.<\/jats:p>","DOI":"10.3390\/diagnostics11081405","type":"journal-article","created":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T03:57:41Z","timestamp":1628049461000},"page":"1405","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models"],"prefix":"10.3390","volume":"11","author":[{"given":"Jasjit S.","family":"Suri","sequence":"first","affiliation":[{"name":"Stroke Diagnostic and Monitoring Division, AtheroPoint\u2122, Roseville, CA 95661, USA"},{"name":"Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2360-3886","authenticated-orcid":false,"given":"Sushant","family":"Agarwal","sequence":"additional","affiliation":[{"name":"Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA"},{"name":"Department of Computer Science Engineering, PSIT, Kanpur 209305, 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":"Vedmanvitha","family":"Ketireddy","sequence":"additional","affiliation":[{"name":"Mira Loma High School, Sacramento, CA 95821, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marta","family":"Columbu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Saba","sequence":"additional","affiliation":[{"name":"Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0757-1290","authenticated-orcid":false,"given":"Suneet K.","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Bennett University, Noida 201310, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gavino","family":"Faa","sequence":"additional","affiliation":[{"name":"Department of Pathology\u2014AOU of Cagliari, 09124 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Inder M.","family":"Singh","sequence":"additional","affiliation":[{"name":"Stroke Diagnostic and Monitoring 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":"Paramjit S.","family":"Chadha","sequence":"additional","affiliation":[{"name":"Stroke Diagnostic and Monitoring Division, AtheroPoint\u2122, Roseville, CA 95661, USA"}],"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 208011, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klaudija","family":"Viskovic","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophie","family":"Mavrogeni","sequence":"additional","affiliation":[{"name":"Cardiology Clinic, Onassis Cardiac Surgery Center, 176 74 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 City, RI 02912, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Miner","sequence":"additional","affiliation":[{"name":"Men\u2019s Health Center, 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":"Minimally Invasive Urology Institute, Brown University, Providence City, RI 02912, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonella","family":"Balestrieri","sequence":"additional","affiliation":[{"name":"Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Petros P.","family":"Sfikakis","sequence":"additional","affiliation":[{"name":"Rheumatology Unit, National Kapodistrian University of Athens, 157 72 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 Transplantation Surgery, Aristoteleion University of Thessaloniki, 541 24 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":"National & Kapodistrian University of Athens, 157 72 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, 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, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George D.","family":"Kitas","sequence":"additional","affiliation":[{"name":"Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK"},{"name":"Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jagjit S.","family":"Teji","sequence":"additional","affiliation":[{"name":"Ann and Robert H. 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 M5G 1N8, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Surinder K.","family":"Dhanjil","sequence":"additional","affiliation":[{"name":"Athero Point LLC, Roseville, CA 95611, USA"}],"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, Nicosia 2408, Cyprus"}],"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":"Athero Point LLC, Roseville, CA 95611, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mostafa","family":"Fatemi","sequence":"additional","affiliation":[{"name":"Department of Physiology & Biomedical Engg., Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7658-1572","authenticated-orcid":false,"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":"Pudukode R.","family":"Krishnan","sequence":"additional","affiliation":[{"name":"Neurology Department, Fortis Hospital, Bangalore 560076, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nagy","family":"Frence","sequence":"additional","affiliation":[{"name":"Department of Internal Medicines, Invasive Cardiology Division, University of Szeged, 6720 Szeged, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zoltan","family":"Ruzsa","sequence":"additional","affiliation":[{"name":"Department of Internal Medicines, Invasive Cardiology Division, University of Szeged, 6720 Szeged, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Archna","family":"Gupta","sequence":"additional","affiliation":[{"name":"Radiology Department, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Subbaram","family":"Naidu","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, University of Minnesota, Duluth, MN 55455, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mannudeep","family":"Kalra","sequence":"additional","affiliation":[{"name":"Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,4]]},"reference":[{"key":"ref_1","unstructured":"(2021, July 28). WHO Coronavirus (COVID-19) Dashboard. Available online: https:\/\/covid19.who.int\/."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1007\/s10554-020-02089-9","article-title":"Imaging in COVID-19-related myocardial injury","volume":"37","author":"Cau","year":"2021","journal-title":"Int. J. Cardiovasc. Imaging"},{"key":"ref_4","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_5","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_6","doi-asserted-by":"crossref","first-page":"104210","DOI":"10.1016\/j.compbiomed.2021.104210","article-title":"A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence","volume":"130","author":"Suri","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cau, R., Falaschi, Z., Pasch\u00e8, A., Danna, P., Arioli, R., Arru, C.D., Zagaria, D., Tricca, S., Suri, J.S., and Karla, M.K. (2021). Computed tomography findings of COVID-19 pneumonia in Intensive Care Unit-patients. J. Public Health Res., 2019.","DOI":"10.4081\/jphr.2021.2270"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"El-Baz, A., and Suri, J. (2019). Lung Imaging and CADx, CRC Press.","DOI":"10.1201\/9780429055959"},{"key":"ref_9","unstructured":"El-Baz, A., and Suri, J.S. (2011). Lung Imaging and Computer Aided Diagnosis, CRC Press."},{"key":"ref_10","unstructured":"Saba, L., and Suri, J.S. (2013). Multi-Detector CT Imaging: Principles, Head, Neck, and Vascular Systems, CRC Press."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"439","DOI":"10.21037\/cdt.2019.09.01","article-title":"Ultrasound-based carotid stenosis measurement and risk stratification in diabetic cohort: A deep learning paradigm","volume":"9","author":"Saba","year":"2019","journal-title":"Cardiovasc. Diagn. Ther."},{"key":"ref_13","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_14","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_15","doi-asserted-by":"crossref","unstructured":"El-Baz, A., and Suri, J. (2021). Machine Learning in Medicine, CRC Press.","DOI":"10.1201\/9781315101323"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Suri, J.S., and Rangayyan, R.M. (2006). Recent Advances in Breast Imaging, Mammography, and Computer-Aided Diagnosis of Breast Cancer, SPIE Press.","DOI":"10.1117\/3.651880"},{"key":"ref_17","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_18","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_19","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.ultras.2011.11.003","article-title":"Non-invasive automated 3D thyroid lesion classification in ultrasound: A class of ThyroScan\u2122 systems","volume":"52","author":"Acharya","year":"2012","journal-title":"Ultrasonics"},{"key":"ref_20","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_21","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_22","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_23","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_24","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.knosys.2012.09.008","article-title":"Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: A hybrid feature extraction approach","volume":"39","author":"Mookiah","year":"2013","journal-title":"Knowl. Based Syst."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1016\/j.cmpb.2013.07.012","article-title":"Automated classification of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images","volume":"112","author":"Acharya","year":"2013","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"1861","DOI":"10.1007\/s10916-010-9645-2","article-title":"Symptomatic vs. asymptomatic plaque classification in carotid ultrasound","volume":"36","author":"Acharya","year":"2012","journal-title":"J. Med. Syst."},{"key":"ref_29","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_30","unstructured":"Suri, J.S. (2011). Imaging Based Symptomatic Classification and Cardiovascular Stroke Risk Score Estimation. (13053971), Google Patent."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"302","DOI":"10.5853\/jos.2017.02922","article-title":"Cerebral Small Vessel Disease: A Review Focusing on Pathophysiology, Biomarkers, and Machine Learning Strategies","volume":"20","author":"Dwivedi","year":"2018","journal-title":"J. Stroke"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","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_33","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1016\/j.bspc.2013.08.008","article-title":"Application of higher order statistics for atrial arrhythmia classification","volume":"8","author":"Martis","year":"2013","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_34","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. Med."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-021-01707-w","article-title":"A novel block imaging technique using nine artificial intelligence models for COVID-19 disease classification, characterization and severity measurement in lung computed tomography scans on an Italian cohort","volume":"45","author":"Agarwal","year":"2021","journal-title":"J. Med. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Mansoor, A., Bagci, U., Foster, B., Xu, Z., Douglas, D., Solomon, J.M., Udupa, J.K., and Mollura, D.J. (2014, January 26\u201330). CIDI-lung-seg: A single-click annotation tool for automatic delineation of lungs from CT scans. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA.","DOI":"10.1109\/EMBC.2014.6943783"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/j.ihj.2018.01.024","article-title":"Intra- and inter-operator reproducibility of automated cloud-based carotid lumen diameter ultrasound measurement","volume":"70","author":"Saba","year":"2018","journal-title":"Indian Heart J."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.compbiomed.2016.06.010","article-title":"Accurate cloud-based smart IMT measurement, its validation and stroke risk stratification in carotid ultrasound: A web-based point-of-care tool for multicenter clinical trial","volume":"75","author":"Saba","year":"2016","journal-title":"Comput. Biol. Med."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1007\/s10916-016-0504-7","article-title":"Inter-observer Variability Analysis of Automatic Lung Delineation in Normal and Disease Patients","volume":"40","author":"Saba","year":"2016","journal-title":"J. Med. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_43","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.compbiomed.2017.10.022","article-title":"Web-based accurate measurements of carotid lumen diameter and stenosis severity: An ultrasound-based clinical tool for stroke risk assessment during multicenter clinical trials","volume":"91","author":"Saba","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_45","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_46","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_47","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_48","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_49","unstructured":"Qualtrics, X. (2021, July 28). Determining Sample Size: How to Ensure You Get the Correct Sample Size. Available online: https:\/\/www.qualtrics.com\/au\/experience-management\/research\/determine-sample-size\/."},{"key":"ref_50","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_51","unstructured":"Riffenburgh, R.H., and Gillen, D.L. (2020). Contents. Statistics in Medicine, Academic Press. [4th ed.]."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Priya, C., Sithi Shameem Fathima, S.M.H., Kirubanandasarathy, N., Valanarasid, A., Safana Begam, M.H., and Aiswarya, N. (2021). Automatic Optimized Cnn Based Covid-19 Lung Infection Segmentation from Ct Image. Mater. Today Proc.","DOI":"10.1016\/j.matpr.2021.01.820"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12880-020-00529-5","article-title":"COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet","volume":"21","author":"Saood","year":"2021","journal-title":"BMC Med. Imaging"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1109\/TNNLS.2021.3054746","article-title":"Anam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images","volume":"32","author":"Paluru","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1016\/j.acra.2020.09.004","article-title":"CT Quantification and Machine-learning Models for Assessment of Disease Severity and Prognosis of COVID-19 Patients","volume":"27","author":"Cai","year":"2020","journal-title":"Acad. Radiol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"014502","DOI":"10.1117\/1.JMI.8.S1.014502","article-title":"Deep CNN models for predicting COVID-19 in CT and X-ray images","volume":"8","author":"Chaddad","year":"2021","journal-title":"J. Med. Imaging"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"608525","DOI":"10.3389\/fmed.2020.608525","article-title":"COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images","volume":"7","author":"Gunraj","year":"2020","journal-title":"Front. Med."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"e368","DOI":"10.7717\/peerj-cs.368","article-title":"Performance analysis of lightweight CNN models to segment infectious lung tissues of COVID-19 cases from tomographic images","volume":"7","author":"Iyer","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"5544742","DOI":"10.1155\/2021\/5544742","article-title":"Lung Infection Segmentation for COVID-19 Pneumonia Based on a Cascade Convolutional Network from CT Images","volume":"2021","author":"Ranjbarzadeh","year":"2021","journal-title":"Biomed. Res. Int."},{"key":"ref_60","first-page":"379","article-title":"Research progress in lung parenchyma segmentation based on computed tomography","volume":"38","author":"Xiao","year":"2021","journal-title":"J. Biomed. Eng."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"179317","DOI":"10.1109\/ACCESS.2020.3028012","article-title":"Novel feature selection and voting classifier algorithms for COVID-19 classification in CT images","volume":"8","author":"Ibrahim","year":"2020","journal-title":"IEEE Access"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"15297","DOI":"10.1007\/s11042-019-7162-y","article-title":"Deep learning approach to detect malaria from microscopic images","volume":"79","author":"Vijayalakshmi","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3052577","article-title":"A Multicenter Study on Carotid Ultrasound Plaque Tissue Characterization and Classification Using Six Deep Artificial Intelligence Models: A Stroke Application","volume":"70","author":"Saba","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"103958","DOI":"10.1016\/j.compbiomed.2020.103958","article-title":"3-D optimized classification and characterization artificial intelligence paradigm for cardiovascular\/stroke risk stratification using carotid ultrasound-based delineated plaque: Atheromatic\u2122 2.0","volume":"125","author":"Skandha","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1","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_66","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_67","doi-asserted-by":"crossref","unstructured":"El-Baz, A., and Suri, J.S. (2019). Big Data in Multimodal Medical Imaging, Boca Raton.","DOI":"10.1201\/b22410"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-015-0214-6","article-title":"Automatic lung segmentation using control feedback system: Morphology and texture paradigm","volume":"39","author":"Noor","year":"2015","journal-title":"J. Med. Syst."},{"key":"ref_69","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."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/11\/8\/1405\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:40:07Z","timestamp":1760164807000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/11\/8\/1405"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,4]]},"references-count":69,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["diagnostics11081405"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics11081405","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,4]]}}}