CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images

Abstract Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR)...

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Autores principales:	Tahereh Javaheri, Morteza Homayounfar, Zohreh Amoozgar, Reza Reiazi, Fatemeh Homayounieh, Engy Abbas, Azadeh Laali, Amir Reza Radmard, Mohammad Hadi Gharib, Seyed Ali Javad Mousavi, Omid Ghaemi, Rosa Babaei, Hadi Karimi Mobin, Mehdi Hosseinzadeh, Rana Jahanban-Esfahlan, Khaled Seidi, Mannudeep K. Kalra, Guanglan Zhang, L. T. Chitkushev, Benjamin Haibe-Kains, Reza Malekzadeh, Reza Rawassizadeh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/96e868fd935b46638ccad432036de83b
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spelling	oai:doaj.org-article:96e868fd935b46638ccad432036de83b2021-12-02T10:54:07ZCovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images10.1038/s41746-021-00399-32398-6352https://doaj.org/article/96e868fd935b46638ccad432036de83b2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00399-3https://doaj.org/toc/2398-6352Abstract Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method; however, its accuracy in detection is only ~70–75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80–98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95% compared to radiologists (70%). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership.Tahereh JavaheriMorteza HomayounfarZohreh AmoozgarReza ReiaziFatemeh HomayouniehEngy AbbasAzadeh LaaliAmir Reza RadmardMohammad Hadi GharibSeyed Ali Javad MousaviOmid GhaemiRosa BabaeiHadi Karimi MobinMehdi HosseinzadehRana Jahanban-EsfahlanKhaled SeidiMannudeep K. KalraGuanglan ZhangL. T. ChitkushevBenjamin Haibe-KainsReza MalekzadehReza RawassizadehNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Tahereh Javaheri Morteza Homayounfar Zohreh Amoozgar Reza Reiazi Fatemeh Homayounieh Engy Abbas Azadeh Laali Amir Reza Radmard Mohammad Hadi Gharib Seyed Ali Javad Mousavi Omid Ghaemi Rosa Babaei Hadi Karimi Mobin Mehdi Hosseinzadeh Rana Jahanban-Esfahlan Khaled Seidi Mannudeep K. Kalra Guanglan Zhang L. T. Chitkushev Benjamin Haibe-Kains Reza Malekzadeh Reza Rawassizadeh CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images
description	Abstract Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method; however, its accuracy in detection is only ~70–75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80–98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95% compared to radiologists (70%). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership.
format	article
author	Tahereh Javaheri Morteza Homayounfar Zohreh Amoozgar Reza Reiazi Fatemeh Homayounieh Engy Abbas Azadeh Laali Amir Reza Radmard Mohammad Hadi Gharib Seyed Ali Javad Mousavi Omid Ghaemi Rosa Babaei Hadi Karimi Mobin Mehdi Hosseinzadeh Rana Jahanban-Esfahlan Khaled Seidi Mannudeep K. Kalra Guanglan Zhang L. T. Chitkushev Benjamin Haibe-Kains Reza Malekzadeh Reza Rawassizadeh
author_facet	Tahereh Javaheri Morteza Homayounfar Zohreh Amoozgar Reza Reiazi Fatemeh Homayounieh Engy Abbas Azadeh Laali Amir Reza Radmard Mohammad Hadi Gharib Seyed Ali Javad Mousavi Omid Ghaemi Rosa Babaei Hadi Karimi Mobin Mehdi Hosseinzadeh Rana Jahanban-Esfahlan Khaled Seidi Mannudeep K. Kalra Guanglan Zhang L. T. Chitkushev Benjamin Haibe-Kains Reza Malekzadeh Reza Rawassizadeh
author_sort	Tahereh Javaheri
title	CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images
title_short	CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images
title_full	CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images
title_fullStr	CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images
title_full_unstemmed	CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images
title_sort	covidctnet: an open-source deep learning approach to diagnose covid-19 using small cohort of ct images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/96e868fd935b46638ccad432036de83b
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CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images

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