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

Dr.Rajasekaran Subramanian 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.

Article Details

How to Cite
SUBRAMANIAN, Dr.Rajasekaran et al. 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. Medical Research Archives, [S.l.], v. 10, n. 10, oct. 2022. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/3145>. Date accessed: 28 nov. 2022. doi: https://doi.org/10.18103/mra.v10i10.3145.
Section
Research Articles

References

1. Richmen DD, Whitley RJ & Hayden FG. Clinical virology. 4th Edition, ASM Press. 2016.
2. F.Shi, J.Wang, J.Shi et al. Review of Artificial Intelligence in imaging data acquisition, segmentation and diagnosis for Covid-19. IEEE reviews in Biomedical Engineering, 2020.
3. Mohammad Ali, Fahed Abdullah, Aleemuddin Naveed, Syed Mahmood Ahmed, Aleem Ahmed Khan, Ashfaq Hasan. Role of circulatory miRNA-21 and associated signaling pathways in the pathogenesis of pulmonary fibrosis among individuals recovered after COVID-19 infection,
Human Gene, Volume 34, 2022, 201093, ISSN 2773-0441. https://doi.org/10.1016/j.humgen.2022.201093.
4. Narges Saeedizadeh, Shervin Minaee et al. Covid TV-U-Net: Segmenting Covid-19 chest CT Images Using Connectivity Imposed U-net. arXiv:2007.12303v3. Aug 2020. Available from: https://arxiv.org/abs/2007.12303v2.
5. Athanasias, Eftychios et al. Deep learning models for Covid-19 infected area segmentation in CT images. MedRxiv, 2020. Available from: https://www.medrxiv.org/content/10.1101/2020.05.08.20094664v2.
6. Radiopaedia. https://radiopaedia.org/. (Accessed May 04, 2020).
7. Arnab Kumar Mishra, Sujit kumar Das et al. Identifying Covid-19 from chest CT images: A Deep Convolutional Neural Networks based approach. Journal of Healthcare Engineering. Hindawi, Volume 2020.
8. Xiaowei Xu, Xiangao Jiang, Chunlian Ma, et al. A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia. https://doi.org/10.1016/j.eng.2020.04.010.
9. Qingsen Yan, Bo.Wang et al. Covid-19 Chest CT image segmentation - A deep convolutional neural network solution. arXiv:2004.10987v2,EESS IV, 2020. Available from: https://arxiv.org/abs/2004.10987.
10. Amine Amyar, Romain Modzelecoski et al. Multi task Deep learning based CT imaging analysis for classification and segmentation. Computers in Biology and Medicine, Elsevier, Volume:126, Nov 2020.
11. Dilibag Singh, Vijay Kumar et al. Classification of covid-19 patients from chest CT images using multi objective differential evolution based convolutional neural networks. Eur J Clin Microbiol Infect Dis, Apr 2020.
12. Deng-Ping Fan, Tao Zhou et al. Inf-Net: Automatic covid-19 lung infection segmentation from CT images. arXiv:2004.14133v4 (EESS.IV), May 2020.
13. Han X, Fan Y, Alwalid O, Li N, Jia X, Yuan M, Li Y, Cao Y, Gu J, Wu H, Shi H. Six-month Follow-up Chest CT Findings after Severe COVID-19 Pneumonia. Radiology. 2021 Apr;299(1):E177-E186. doi: 10.1148/radiol.2021203153. Epub 2021 Jan 26. PMID: 33497317; PMCID: PMC7841877.
14. Covid-19 CT Segmentation dataset. Available from:http://medicalsegmentation.com/covid19/. (Accessed July 04,2022).
15. NiBabel Package. Available from: https://nipy.org/nibabel/.
16. X. Zhang, J. Zou, K. He and J. Sun. Accelerating Very Deep Convolutional Networks for Classification and Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 38, No. 10, pp. 1943-1955, 1 Oct. 2016. doi: 10.1109/TPAMI.2015.2502579.
17. Kaiming He, Xiangyu Zhang et al. Deep residual learning for Image Recognition. arXiv:1512.03385v1[cs.cv], 2015. Available from: https://arxiv.org/abs/1512.03385.
18. Uni-freiburg. U-Net: Convolutional Networks for Biomedical Image Segmentation. Available from: https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/.
19. Olaf Ronneberger, phillipp Fischer et al. U-net: convolutional networks for biomedical image segmentation. MICCAI, Springer, vol:9351:234-241,2015.
20. medRxiv. The Preprint server for Health Sciences. https://www.medrxiv.org/.
21. bioRxiv. The Preprint server for Biology. https://www.biorxiv.org/.
22. Python bindings for MuPDF’s rendering library. https://github.com/pymupdf/PyMuPDF
23. Francone, M., Iafrate, F., Masci, G.M. et al. Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis. European Radiology. Vol 30, 6808–6817 (2020). https://doi.org/10.1007/s00330-020-07033-y
24. Joshua J. Solomon, Brooke Heyman, Jane P. Ko, Rany Condos, and David A. Lynch. CT of Post-Acute Lung Complications of COVID-19. Radiology 2021; 301:2, E383-E395.
25. Havervall S, Rosell A, Phillipson M, et al. Symptoms and Functional Impairment Assessed 8 Months After Mild COVID-19 Among Health Care Workers. JAMA 2021;325(19):2015–2016.
26. Ooi GCKP, Khong PL, Müller NL, et.al. Severe acute respiratory syndrome: temporal lung changes at thin-section CT in 30 patients. Radiology 2004;230(3):836–844.
27. Cherry JD, Krogstad P. SARS: the first pandemic of the 21st century. Pediatr Res 2004;56(1):1–5.
28. Antonio GE, Wong KT, Chu WC, et al. Imaging in severe acute respiratory syndrome (SARS). Clin Radiol 2003;58(11):825–832.
29. Antonio GE, Wong KT, Hui DS, et al. Thin-section CT in patients with severe acute respiratory syndrome following hospital discharge: preliminary experience. Radiology 2003;228(3):810–815.
30.Chan KSZJ, Zheng JP, Mok YW, et al. SARS: prognosis, outcome and sequelae. Respirology 2003;8(Suppl 1):S36–S40.
31. Humphries SM, Swigris JJ, Brown KK, et al. Quantitative high-resolution computed tomography fibrosis score: performance characteristics in idiopathic pulmonary fibrosis. European Respiratory Journal 2018;52(3):1801384.
32.Mathai SK, Humphries S, Kropski JA, et al. MUC5B variant is associated with visually and quantitatively detected preclinical pulmonary fibrosis. Thorax 2019;74(12):1131–1139.
33. Salisbury ML, Hewlett JC, Ding G, Markin CR, Douglas K, Mason W, Guttentag A, Phillips JA 3rd, Cogan JD, Reiss S, Mitchell DB, Wu P, Young LR, Lancaster LH, Loyd JE, Humphries SM, Lynch DA, Kropski JA, Blackwell TS. Development and Progression of Radiologic Abnormalities in Individuals at Risk for Familial Interstitial Lung Disease. Am J Respir Crit Care Med. 2020; May 15;201(10):1230-1239. doi: 10.1164/rccm.201909-1834OC. PMID: 32011901; PMCID: PMC7233345.
34. Humphries SM, Yagihashi K, Huckleberry J, et al. Idiopathic Pulmonary Fibrosis: Data-driven Textural Analysis of Extent of Fibrosis at Baseline and 15-Month Follow-up. Radiology 2017; 285(1):270–278.
35.Raghu G, Wilson KC. COVID-19 interstitial pneumonia: monitoring the clinical course i survivors. Lancet Respir Med 2020; 8(9):839–842.
36. Bnar J. Hama Amin, Fahmi H. Kakamad, Gasha S. Ahmed, Shaho F. Ahmed, Berwn A. Abdulla, Shvan H. mohammed, Tomas M. Mikael, Rawezh Q. Salih, Razhan k. Ali, Abdulwahid M. Salh, Dahat A. Hussein. Post COVID-19 pulmonary fibrosis; a meta-analysis study. Annals of Medicine and Surgery, Volume 77,2022,103590. ISSN 2049-0801. https://doi.org/10.1016/j.amsu.2022.103590.
37. Pan F, Ye T, Sun P, Gui S, Liang B, Li L, Zheng D, Wang J, Hesketh RL, Yang L, Zheng C (2020) Time course of lung changes at chest CT during recovery from coronavirus disease 2019 (COVID-19). Radiology. 295(3):715–721. https://doi.org/10.1148/radiol.2020200370
38. Ali, R.M.M., Ghonimy, M.B.I. Post-COVID-19 pneumonia lung fibrosis: a worrisome sequelae in surviving patients. Egypt J Radiol Nucl Med; Vol 52, 101 (2021). https://doi.org/10.1186/s43055-021-00484-3