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COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts
(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 Lu...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8699928/ https://www.ncbi.nlm.nih.gov/pubmed/34943603 http://dx.doi.org/10.3390/diagnostics11122367 |
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author | Suri, Jasjit S. Agarwal, Sushant Carriero, Alessandro Paschè, Alessio Danna, Pietro S. C. Columbu, Marta Saba, Luca Viskovic, Klaudija Mehmedović, Armin Agarwal, Samriddhi Gupta, Lakshya Faa, Gavino Singh, Inder M. Turk, Monika Chadha, Paramjit S. Johri, Amer M. Khanna, Narendra N. Mavrogeni, Sophie Laird, John R. Pareek, Gyan Miner, Martin Sobel, David W. Balestrieri, Antonella Sfikakis, Petros P. Tsoulfas, George Protogerou, Athanasios Misra, Durga Prasanna Agarwal, Vikas Kitas, George D. Teji, Jagjit S. Al-Maini, Mustafa Dhanjil, Surinder K. Nicolaides, Andrew Sharma, Aditya Rathore, Vijay Fatemi, Mostafa Alizad, Azra Krishnan, Pudukode R. Nagy, Ferenc Ruzsa, Zoltan Gupta, Archna Naidu, Subbaram Paraskevas, Kosmas I. Kalra, Mannudeep K. |
author_facet | Suri, Jasjit S. Agarwal, Sushant Carriero, Alessandro Paschè, Alessio Danna, Pietro S. C. Columbu, Marta Saba, Luca Viskovic, Klaudija Mehmedović, Armin Agarwal, Samriddhi Gupta, Lakshya Faa, Gavino Singh, Inder M. Turk, Monika Chadha, Paramjit S. Johri, Amer M. Khanna, Narendra N. Mavrogeni, Sophie Laird, John R. Pareek, Gyan Miner, Martin Sobel, David W. Balestrieri, Antonella Sfikakis, Petros P. Tsoulfas, George Protogerou, Athanasios Misra, Durga Prasanna Agarwal, Vikas Kitas, George D. Teji, Jagjit S. Al-Maini, Mustafa Dhanjil, Surinder K. Nicolaides, Andrew Sharma, Aditya Rathore, Vijay Fatemi, Mostafa Alizad, Azra Krishnan, Pudukode R. Nagy, Ferenc Ruzsa, Zoltan Gupta, Archna Naidu, Subbaram Paraskevas, Kosmas I. Kalra, Mannudeep K. |
author_sort | Suri, Jasjit S. |
collection | PubMed |
description | (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. |
format | Online Article Text |
id | pubmed-8699928 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86999282021-12-24 COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts Suri, Jasjit S. Agarwal, Sushant Carriero, Alessandro Paschè, Alessio Danna, Pietro S. C. Columbu, Marta Saba, Luca Viskovic, Klaudija Mehmedović, Armin Agarwal, Samriddhi Gupta, Lakshya Faa, Gavino Singh, Inder M. Turk, Monika Chadha, Paramjit S. Johri, Amer M. Khanna, Narendra N. Mavrogeni, Sophie Laird, John R. Pareek, Gyan Miner, Martin Sobel, David W. Balestrieri, Antonella Sfikakis, Petros P. Tsoulfas, George Protogerou, Athanasios Misra, Durga Prasanna Agarwal, Vikas Kitas, George D. Teji, Jagjit S. Al-Maini, Mustafa Dhanjil, Surinder K. Nicolaides, Andrew Sharma, Aditya Rathore, Vijay Fatemi, Mostafa Alizad, Azra Krishnan, Pudukode R. Nagy, Ferenc Ruzsa, Zoltan Gupta, Archna Naidu, Subbaram Paraskevas, Kosmas I. Kalra, Mannudeep K. Diagnostics (Basel) Article (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. MDPI 2021-12-15 /pmc/articles/PMC8699928/ /pubmed/34943603 http://dx.doi.org/10.3390/diagnostics11122367 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Suri, Jasjit S. Agarwal, Sushant Carriero, Alessandro Paschè, Alessio Danna, Pietro S. C. Columbu, Marta Saba, Luca Viskovic, Klaudija Mehmedović, Armin Agarwal, Samriddhi Gupta, Lakshya Faa, Gavino Singh, Inder M. Turk, Monika Chadha, Paramjit S. Johri, Amer M. Khanna, Narendra N. Mavrogeni, Sophie Laird, John R. Pareek, Gyan Miner, Martin Sobel, David W. Balestrieri, Antonella Sfikakis, Petros P. Tsoulfas, George Protogerou, Athanasios Misra, Durga Prasanna Agarwal, Vikas Kitas, George D. Teji, Jagjit S. Al-Maini, Mustafa Dhanjil, Surinder K. Nicolaides, Andrew Sharma, Aditya Rathore, Vijay Fatemi, Mostafa Alizad, Azra Krishnan, Pudukode R. Nagy, Ferenc Ruzsa, Zoltan Gupta, Archna Naidu, Subbaram Paraskevas, Kosmas I. Kalra, Mannudeep K. COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts |
title | COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts |
title_full | COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts |
title_fullStr | COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts |
title_full_unstemmed | COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts |
title_short | COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts |
title_sort | covlias 1.0 vs. medseg: artificial intelligence-based comparative study for automated covid-19 computed tomography lung segmentation in italian and croatian cohorts |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8699928/ https://www.ncbi.nlm.nih.gov/pubmed/34943603 http://dx.doi.org/10.3390/diagnostics11122367 |
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