COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts.

Autor:	Suri JS; Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA.; Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA., Agarwal S; Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA.; Department of Computer Science Engineering, Pranveer Singh Institute of Technology, Kanpur 209305, India., Carriero A; Department of Radiology, 'Maggiore della Carità' Hospital, University of Piemonte Orientale (UPO), 28100 Novara, Italy., Paschè A; Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy., Danna PSC; Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy., Columbu M; Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy., Saba L; Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy., Viskovic K; Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10 000 Zagreb, Croatia., Mehmedović A; Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10 000 Zagreb, Croatia., Agarwal S; Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA.; Department of Computer Science Engineering, Pranveer Singh Institute of Technology, Kanpur 209305, India., Gupta L; Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA., Faa G; Department of Pathology, AOU of Cagliari, 09124 Cagliari, Italy., Singh IM; Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA., Turk M; The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany., Chadha PS; Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA., Johri AM; Department of Medicine, Division of Cardiology, Queen's University, Kingston, ON K7L 3N6, Canada., Khanna NN; Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India., Mavrogeni S; Cardiology Clinic, Onassis Cardiac Surgery Center, 17674 Athens, Greece., Laird JR; Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA., Pareek G; Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA., Miner M; Men's Health Center, Miriam Hospital, Providence, RI 02906, USA., Sobel DW; Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA., Balestrieri A; Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy., Sfikakis PP; Rheumatology Unit, National Kapodistrian University of Athens, 15772 Athens, Greece., Tsoulfas G; Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece., Protogerou A; Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece., Misra DP; Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India., Agarwal V; Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India., Kitas GD; Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK.; Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK., Teji JS; Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL 60611, USA., Al-Maini M; Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada., Dhanjil SK; AtheroPoint LLC, Roseville, CA 95611, USA., Nicolaides A; Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Nicosia 2408, Cyprus., Sharma A; Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22904, USA., Rathore V; AtheroPoint LLC, Roseville, CA 95611, USA., Fatemi M; Department of Physiology & Biomedical Engg., Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA., Alizad A; Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA., Krishnan PR; Neurology Department, Fortis Hospital, Bangalore 560076, India., Nagy F; Internal Medicine Department, University of Szeged, 6725 Szeged, Hungary., Ruzsa Z; Invasive Cardiology Division, University of Szeged, 6725 Szeged, Hungary., Gupta A; Radiology Department, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India., Naidu S; Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA., Paraskevas KI; Department of Vascular Surgery, Central Clinic of Athens, 14122 Athens, Greece., Kalra MK; Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA.
Jazyk:	angličtina
Zdroj:	Diagnostics (Basel, Switzerland) [Diagnostics (Basel)] 2021 Dec 15; Vol. 11 (12). Date of Electronic Publication: 2021 Dec 15.
DOI:	10.3390/diagnostics11122367
Abstrakt:	(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020-2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPoint TM , Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland-Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg.
Databáze:	MEDLINE
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