Details

Autor(en) / Beteiligte

• 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
• Viskovic, Klaudija
• Kalra, Manudeep K

Titel

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

Ist Teil von

• Diagnostics (Basel), 2022-05, Vol.12 (5)

Ort / Verlag

Switzerland

Erscheinungsjahr

2022

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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.