Naslov
Multicenter Study on COVID-19 Lung Computed Tomography Segmentation with varying Glass Ground Opacities using Unseen Deep Learning Artificial Intelligence Paradigms: COVLIAS 1.0 Validation

Autori
Suri, Jasjit S. ; Agarwal, Sushant ; Saba, Luca ; Chabert, Gian Luca ; Carriero, Alessandro ; Paschè, Alessio ; Danna, Pietro ; Mehmedović, Armin ; Faa, Gavino ; Jujaray, Tanay ; Singh, Inder M. ; Khanna, Narendra N. ; Laird, John R. ; Sfikakis, Petros P. ; Agarwal, Vikas ; Teji, Jagjit S. ; R Yadav, Rajanikant ; Nagy, Ferenc ; Kincses, Zsigmond Tamás ; Ruzsa, Zoltan ; Višković, Klaudija ; Kalra, Mannudeep K.

Izvornik
Journal of Medical Systems (1573-689X) 46 (2022), 10; 29, 29

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

Ključne riječi
And AI ; COVID-19 ; Glass ground opacities ; Hounsfield units ; Hybrid deep learning ; Lung CT ; Segmentation ; Solo deep learning

Sažetak
Variations in COVID-19 lesions such as glass ground opacities (GGO), consolidations, and crazy paving can compromise the ability of solo-deep learning (SDL) or hybrid-deep learning (HDL) artificial intelligence (AI) models in predicting automated COVID-19 lung segmentation in Computed Tomography (CT) from unseen data leading to poor clinical manifestations. As the first study of its kind, "COVLIAS 1.0-Unseen" proves two hypotheses, (i) contrast adjustment is vital for AI, and (ii) HDL is superior to SDL. In a multicenter study, 10, 000 CT slices were collected from 72 Italian (ITA) patients with low-GGO, and 80 Croatian (CRO) patients with high-GGO. Hounsfield Units (HU) were automatically adjusted to train the AI models and predict from test data, leading to four combinations-two Unseen sets: (i) train-CRO:test- ITA, (ii) train-ITA:test-CRO, and two Seen sets: (iii) train-CRO:test-CRO, (iv) train-ITA:test-ITA. COVILAS used three SDL models: PSPNet, SegNet, UNet and six HDL models: VGG-PSPNet, VGG-SegNet, VGG-UNet, ResNet-PSPNet, ResNet-SegNet, and ResNet-UNet. Two trained, blinded senior radiologists conducted ground truth annotations. Five types of performance metrics were used to validate COVLIAS 1.0-Unseen which was further benchmarked against MedSeg, an open-source web- based system. After HU adjustment for DS and JI, HDL (Unseen AI) > SDL (Unseen AI) by 4% and 5%, respectively. For CC, HDL (Unseen AI) > SDL (Unseen AI) by 6%. The COVLIAS-MedSeg difference was < 5%, meeting regulatory guidelines.Unseen AI was successfully demonstrated using automated HU adjustment. HDL was found to be superior to SDL.

Izvorni jezik
Engleski

Znanstvena područja
Kliničke medicinske znanosti

POVEZANOST RADA