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

Suri J. S.;Agarwal S.;Chabert G. L.;Carriero A.;Pasche A.;Danna P. S. C.;Saba L.;Mehmedovic A.;Faa G.;Singh I. M.;Turk M.;Chadha P. S.;Johri A. M.;Khanna N. N.;Mavrogeni S.;Laird J. R.;Pareek G.;Miner M.;Sobel D. W.;Balestrieri A.;Sfikakis P. P.;Tsoulfas G.;Protogerou A. D.;Misra D. P.;Agarwal V.;Kitas G. D.;Teji J. S.;Al-Maini M.;Dhanjil S. K.;Nicolaides A.;Sharma A.;Rathore V.;Fatemi M.;Alizad A.;Krishnan P. R.;Nagy F.;Ruzsa Z.;Fouda M. M.;Naidu S.;Viskovic K.;Kalra M. K.

2022-01-01

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Abstract

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–Whit-ney 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 seg-mented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervaria-bility test.