Javaheri, T. and Homayounfar, M. and Amoozgar, Z. and Reiazi, R. and Homayounieh, F. and Abbas, E. and Laali, A. and Radmard, A.R. and Gharib, M.H. and Mousavi, S.A.J. and Ghaemi, O. and Babaei, R. and Mobin, H.K. and Hosseinzadeh, M. and Jahanban-Esfahlan, R. and Seidi, K. and Kalra, M.K. and Zhang, G. and Chitkushev, L.T. and Haibe-Kains, B. and Malekzadeh, R. and Rawassizadeh, R. (2021) CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images. npj Digital Medicine, 4 (1).
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CovidCTNet an open-source deep learning approach to diagnose covid-19 using small cohort of CT images.pdf Download (2MB) | Preview |
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. © 2021, The Author(s).
| Item Type: | Article |
|---|---|
| Additional Information: | cited By 0 |
| Uncontrolled Keywords: | Article; clinical outcome; cohort analysis; community acquired pneumonia; computer assisted tomography; controlled study; convolutional neural network; coronavirus disease 2019; deep learning; diagnostic accuracy; diagnostic error; diagnostic radiologist; diagnostic test accuracy study; differential diagnosis; human; image reconstruction; priority journal; receiver operating characteristic; sensitivity and specificity; thorax radiography |
| Subjects: | WC Communicable Diseases |
| Depositing User: | eprints admin |
| Date Deposited: | 24 Aug 2021 05:13 |
| Last Modified: | 24 Aug 2021 05:13 |
| URI: | http://eprints.iums.ac.ir/id/eprint/38939 |
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