Περίληψη:
(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 similar to 4 s and
similar to 10 s, respectively. (4) Conclusions: The performances of
COVLIAS and MedSeg were similar. However, COVLIAS showed improved
computing time over MedSeg.
Συγγραφείς:
Suri, Jasjit S.
Agarwal, Sushant
Carriero, Alessandro and
Pasche, Alessio
Danna, Pietro S. C.
Columbu, Marta
Saba,
Luca
Viskovic, Klaudija
Mehmedovic, Armin
Agarwal, Samriddhi
and Gupta, Lakshya
Faa, Gavino
Singh, Inder M.
Turk, Monika
and Chadha, Paramjit S.
Johri, Amer M.
Khanna, Narendra N. and
Mavrogeni, Sophie
Laird, John R.
Pareek, Gyan
Miner, Martin
and Sobel, David W.
Balestrieri, Antonella
Sfikakis, Petros P.
and Tsoulfas, George
Protogerou, Athanasios
Misra, Durga
Prasanna
Agarwal, Vikas
Kitas, George D.
Teji, Jagjit S. and
Al-Maini, Mustafa
Dhanjil, Surinder K.
Nicolaides, Andrew and
Sharma, Aditya
Rathore, Vijay
Fatemi, Mostafa
Alizad, Azra
and Krishnan, Pudukode R.
Nagy, Ferenc
Ruzsa, Zoltan
Gupta,
Archna
Naidu, Subbaram
Paraskevas, Kosmas I.
Kalra,
Mannudeep K.
