{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T13:39:13Z","timestamp":1792503553041,"version":"4.5.0"},"reference-count":82,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"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: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all &lt; 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet &gt; PSP Net &gt; VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.<\/jats:p>","DOI":"10.3390\/diagnostics11112025","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T22:21:08Z","timestamp":1635805268000},"page":"2025","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography"],"prefix":"10.3390","volume":"11","author":[{"given":"Jasjit","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":"Pranav","family":"Elavarthi","sequence":"additional","affiliation":[{"name":"Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA"},{"name":"Thomas Jefferson High School for Science and Technology, Alexandria, VA 22312, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajesh","family":"Pathak","sequence":"additional","affiliation":[{"name":"Department of Computer Science Engineering, Rawatpura Sarkar University, Raipur 492001, 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.), 10015 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Saba","sequence":"additional","affiliation":[{"name":"Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0757-1290","authenticated-orcid":false,"given":"Suneet","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Bennett University, Noida 201310, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0189-8612","authenticated-orcid":false,"given":"Gavino","family":"Faa","sequence":"additional","affiliation":[{"name":"Department of Pathology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Inder","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","family":"Chadha","sequence":"additional","affiliation":[{"name":"Stroke Diagnostic and Monitoring Division, AtheroPoint\u2122, Roseville, CA 95661, 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Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Laird","sequence":"additional","affiliation":[{"name":"Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gyan","family":"Pareek","sequence":"additional","affiliation":[{"name":"Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Miner","sequence":"additional","affiliation":[{"name":"Men\u2019s Health Center, Miriam Hospital, Providence, RI 02906, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0448-4765","authenticated-orcid":false,"given":"David","family":"Sobel","sequence":"additional","affiliation":[{"name":"Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, 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UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jagjit","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 L4Z 4C4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Surinder","family":"Dhanjil","sequence":"additional","affiliation":[{"name":"AtheroPoint LLC, Roseville, CA 95611, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Nicolaides","sequence":"additional","affiliation":[{"name":"Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Nicosia 2368, 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":"AtheroPoint LLC, Roseville, CA 95611, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6603-9077","authenticated-orcid":false,"given":"Mostafa","family":"Fatemi","sequence":"additional","affiliation":[{"name":"Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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","family":"Krishnan","sequence":"additional","affiliation":[{"name":"Neurology Department, Fortis Hospital, Bangalore 560076, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3512-5850","authenticated-orcid":false,"given":"Ferenc","family":"Nagy","sequence":"additional","affiliation":[{"name":"Internal Medicine Department, University of Szeged, 6725 Szeged, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zoltan","family":"Ruzsa","sequence":"additional","affiliation":[{"name":"Zoltan Invasive Cardiology Division, University of Szeged, 6725 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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8544-8397","authenticated-orcid":false,"given":"Subbaram","family":"Naidu","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"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":[[2021,11,1]]},"reference":[{"key":"ref_1","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 optimization paradigm on a weak training brain magnetic resonance imaging datasets: A supercomputer application","volume":"59","author":"Agarwal","year":"2021","journal-title":"Med. 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