TY - JOUR
T1 - COVLIAS 1.0 vs. MedSeg
T2 - Artificial intelligence-based comparative study for automated COVID-19 computed tomography lung segmentation in Italian and Croatian cohorts
AU - Suri, Jasjit S.
AU - Agarwal, Sushant
AU - Carriero, Alessandro
AU - Paschè, Alessio
AU - Danna, Pietro S.C.
AU - Columbu, Marta
AU - Saba, Luca
AU - Viskovic, Klaudija
AU - Mehmedović, Armin
AU - Agarwal, Samriddhi
AU - Gupta, Lakshya
AU - Faa, Gavino
AU - Singh, Inder M.
AU - Turk, Monika
AU - Chadha, Paramjit S.
AU - Johri, Amer M.
AU - Khanna, Narendra N.
AU - Mavrogeni, Sophie
AU - Laird, John R.
AU - Pareek, Gyan
AU - Miner, Martin
AU - Sobel, David W.
AU - Balestrieri, Antonella
AU - Sfikakis, Petros P.
AU - Tsoulfas, George
AU - Protogerou, Athanasios
AU - Misra, Durga Prasanna
AU - Agarwal, Vikas
AU - Kitas, George D.
AU - Teji, Jagjit S.
AU - Al-Maini, Mustafa
AU - Dhanjil, Surinder K.
AU - Nicolaides, Andrew
AU - Sharma, Aditya
AU - Rathore, Vijay
AU - Fatemi, Mostafa
AU - Alizad, Azra
AU - Krishnan, Pudukode R.
AU - Nagy, Ferenc
AU - Ruzsa, Zoltan
AU - Gupta, Archna
AU - Naidu, Subbaram
AU - Paraskevas, Kosmas I.
AU - Kalra, Mannudeep K.
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/12
Y1 - 2021/12
N2 - (1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiau-tomated 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™, 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.
AB - (1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiau-tomated 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™, 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.
KW - AI
KW - Benchmark.4
KW - COVID-19
KW - COVLIAS
KW - CT
KW - DL
KW - HDL
KW - Lung segmentation
KW - MedSeg
KW - Validation
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U2 - 10.3390/diagnostics11122367
DO - 10.3390/diagnostics11122367
M3 - Article
AN - SCOPUS:85121566843
SN - 2075-4418
VL - 11
JO - Diagnostics
JF - Diagnostics
IS - 12
M1 - 2367
ER -