Naslov
COVLIAS 1.0Lesion vs. MedSeg: An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans

Autori
Suri, Jasjit S. ; Agarwal, Sushant ; Chabert, Gian Luca ; Carriero, Alessandro ; Paschè, Alessio ; Danna, Pietro S. C. ; Saba, Luca ; Mehmedović, Armin ; 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 D. ; 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 ; Fouda, Mostafa M. ; Naidu, Subbaram ; Višković, Klaudija ; Kalra, Manudeep K.

Izvornik
Diagnostics (2075-4418) 12 (2022), 5; 34, 34

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
COVID-19 ; computed tomography ; COVID lesions ; ground-glass opacities ; segmentation ; hybrid deep learning

Sažetak
Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models—namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet—were trained by an expert radiologist. The training scheme adopted a fivefold cross- validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests—namely, the Mann– Whitney test, paired t-test, and Wilcoxon test— demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s. Conclusions: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test.

Izvorni jezik
Engleski

Znanstvena područja
Kliničke medicinske znanosti

POVEZANOST RADA