Quantitative Progression analysis of Post-Acute Sequelae of COVID-19, Pulmonary Fibrosis (PASC-PF) and Artificial Intelligence driven CT scoring of Lung involvement in Covid-19 infection using HRCT-Chest images CNN based automatic scoring of Post-Acute Sequelae of COVID-19 (PASC) Lung Involvement in Covid-19 infection on HRCT-images
Main Article Content
http://orcid.org/0000-0002-6572-3934
Dr.R.Devika Rubi
Mogilisetti Dedeepya
Sravan Kumar V M Gorugantu
Abstract
Covid-19 is a contagious respiratory disease caused by SARS-COV-2 coronavirus, continue to spread across the world since Jan'2020. Covid-19 can be diagnosed by Nucleic Acid Testing, Antigens Test and Serology Tests or by Digitized Medical Imaging tools like X-ray and High-Resolution Computed Tomography (HRCT)-Chest images. Advanced computational technologies like Artificial Intelligence (AI) - Deep Learning models can assist the radiologist in quantitative, accurate, infection severity and consistent diagnosis, by training the computational system to learn the features of Covid-19 by feeding practitioner's annotated CT images. Quantitative HRCT assessment of severity of COVID-19 infection was widely appreciated by medical fraternity, various government administrations across the world during the early phase of infection. During digitized HRCT-Chest images analysis, the typical features of COVID-19 like bilateral peripheral Ground Glass Opacities (GGO) and/or consolidation, predominantly involving the lower lobes of lung are analyzed. Bilateral peripheral rounded patchy ground glass opacities have been analyzed. HRCT images analysis helps to identify pre-dominant patterns of lung abnormalities like unilateral, multifocal and peripherally GGOs. Most patients make a complete recovery, few of them continue to experience sequalae long after they recover from the acute infection. The most common sequalae symptoms reported were lost of taste or smell, fatigue, and shortness of breath. This constellation of sequalae symptoms, is called Post-Acute Sequelae of COVID-19 (PASC). A subset of patients recovering from infection continue to have persistent respiratory symptoms and chest imaging abnormalities. Our AI-Deep Learning Model is calculating the extent of lung involvement (% of involvement) which is common in the acute phase of infection, was recognized early in the pandemic. While the GGOs and consolidations slowly improved after Covid-19 recovery, fibrosis was seen considerable percentage of patients on follow-up scans after discharge. Results arriving out from our AI model is very much helpful to focus on the post-acute lung disease in COVID-19. Our Deep Learning model analytics on quantitative HRCT metrics of fibrotic lung disease quickly facilitate a role in the evaluation of post covid symptoms, lung fibrotic changes or pulmonary fibrosis.
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