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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

Authors :
Jasjit S. Suri
Sushant Agarwal
Alessandro Carriero
Alessio Paschè
Pietro S. C. Danna
Marta Columbu
Luca Saba
Klaudija Viskovic
Armin Mehmedović
Samriddhi Agarwal
Lakshya Gupta
Gavino Faa
Inder M. Singh
Monika Turk
Paramjit S. Chadha
Amer M. Johri
Narendra N. Khanna
Sophie Mavrogeni
John R. Laird
Gyan Pareek
Martin Miner
David W. Sobel
Antonella Balestrieri
Petros P. Sfikakis
George Tsoulfas
Athanasios Protogerou
Durga Prasanna Misra
Vikas Agarwal
George D. Kitas
Jagjit S. Teji
Mustafa Al-Maini
Surinder K. Dhanjil
Andrew Nicolaides
Aditya Sharma
Vijay Rathore
Mostafa Fatemi
Azra Alizad
Pudukode R. Krishnan
Ferenc Nagy
Zoltan Ruzsa
Archna Gupta
Subbaram Naidu
Kosmas I. Paraskevas
Mannudeep K. Kalra
Source :
Diagnostics, Vol 11, Iss 12, p 2367 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

(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 AtheroPointTM, 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

Details

Language :
English
ISSN :
20754418
Volume :
11
Issue :
12
Database :
Directory of Open Access Journals
Journal :
Diagnostics
Publication Type :
Academic Journal
Accession number :
edsdoj.2e838515d7944bbcbd64e268a0465992
Document Type :
article
Full Text :
https://doi.org/10.3390/diagnostics11122367