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Artificial intelligence enabled non-invasive T-ray imaging technique for early detection of coronavirus infected patients
A new artificial intelligence (AI) supported T-Ray imaging system designed and implemented for non-invasive and non-ionizing screening for coronavirus-affected patients. The new system has the potential to replace the standard conventional X-Ray based imaging modality of virus detection. This resear...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Authors. Published by Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9296229/ https://www.ncbi.nlm.nih.gov/pubmed/35873921 http://dx.doi.org/10.1016/j.imu.2022.101025 |
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author | Biswas, Swarnava Adhikari, Saikat Chawla, Riddhi Maiti, Niladri Bhatia, Dinesh Phukan, Pranjal Mukherjee, Moumita |
author_facet | Biswas, Swarnava Adhikari, Saikat Chawla, Riddhi Maiti, Niladri Bhatia, Dinesh Phukan, Pranjal Mukherjee, Moumita |
author_sort | Biswas, Swarnava |
collection | PubMed |
description | A new artificial intelligence (AI) supported T-Ray imaging system designed and implemented for non-invasive and non-ionizing screening for coronavirus-affected patients. The new system has the potential to replace the standard conventional X-Ray based imaging modality of virus detection. This research article reports the development of solid state room temperature terahertz source for thermograph study. Exposure time and radiation energy are optimized through several real-time experiments. During its incubation period, Coronavirus stays within the cell of the upper respiratory tract and its presence often causes an increased level of blood supply to the virus-affected cells/inter-cellular region that results in a localized increase of water content in those cells & tissues in comparison to its neighbouring normal cells. Under THz-radiation exposure, the incident energy gets absorbed more in virus-affected cells/inter-cellular region and gets heated; thus, the sharp temperature gradient is observed in the corresponding thermograph study. Additionally, structural changes in virus-affected zones make a significant contribution in getting better contrast in thermographs. Considering the effectiveness of the Artificial Intelligence (AI) analysis tool in various medical diagnoses, the authors have employed an explainable AI-assisted methodology to correctly identify and mark the affected pulmonary region for the developed imaging technique and thus validate the model. This AI-enabled non-ionizing THz-thermography method is expected to address the voids in early COVID diagnosis, at the onset of infection. |
format | Online Article Text |
id | pubmed-9296229 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Authors. Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92962292022-07-20 Artificial intelligence enabled non-invasive T-ray imaging technique for early detection of coronavirus infected patients Biswas, Swarnava Adhikari, Saikat Chawla, Riddhi Maiti, Niladri Bhatia, Dinesh Phukan, Pranjal Mukherjee, Moumita Inform Med Unlocked Article A new artificial intelligence (AI) supported T-Ray imaging system designed and implemented for non-invasive and non-ionizing screening for coronavirus-affected patients. The new system has the potential to replace the standard conventional X-Ray based imaging modality of virus detection. This research article reports the development of solid state room temperature terahertz source for thermograph study. Exposure time and radiation energy are optimized through several real-time experiments. During its incubation period, Coronavirus stays within the cell of the upper respiratory tract and its presence often causes an increased level of blood supply to the virus-affected cells/inter-cellular region that results in a localized increase of water content in those cells & tissues in comparison to its neighbouring normal cells. Under THz-radiation exposure, the incident energy gets absorbed more in virus-affected cells/inter-cellular region and gets heated; thus, the sharp temperature gradient is observed in the corresponding thermograph study. Additionally, structural changes in virus-affected zones make a significant contribution in getting better contrast in thermographs. Considering the effectiveness of the Artificial Intelligence (AI) analysis tool in various medical diagnoses, the authors have employed an explainable AI-assisted methodology to correctly identify and mark the affected pulmonary region for the developed imaging technique and thus validate the model. This AI-enabled non-ionizing THz-thermography method is expected to address the voids in early COVID diagnosis, at the onset of infection. The Authors. Published by Elsevier Ltd. 2022 2022-07-20 /pmc/articles/PMC9296229/ /pubmed/35873921 http://dx.doi.org/10.1016/j.imu.2022.101025 Text en © 2022 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Biswas, Swarnava Adhikari, Saikat Chawla, Riddhi Maiti, Niladri Bhatia, Dinesh Phukan, Pranjal Mukherjee, Moumita Artificial intelligence enabled non-invasive T-ray imaging technique for early detection of coronavirus infected patients |
title | Artificial intelligence enabled non-invasive T-ray imaging technique for early detection of coronavirus infected patients |
title_full | Artificial intelligence enabled non-invasive T-ray imaging technique for early detection of coronavirus infected patients |
title_fullStr | Artificial intelligence enabled non-invasive T-ray imaging technique for early detection of coronavirus infected patients |
title_full_unstemmed | Artificial intelligence enabled non-invasive T-ray imaging technique for early detection of coronavirus infected patients |
title_short | Artificial intelligence enabled non-invasive T-ray imaging technique for early detection of coronavirus infected patients |
title_sort | artificial intelligence enabled non-invasive t-ray imaging technique for early detection of coronavirus infected patients |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9296229/ https://www.ncbi.nlm.nih.gov/pubmed/35873921 http://dx.doi.org/10.1016/j.imu.2022.101025 |
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