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      Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging

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          Abstract

          The scientific community has joined forces to mitigate the scope of the current COVID-19 pandemic. The early identification of the disease, as well as the evaluation of its evolution is a primary task for the timely application of medical protocols. The use of medical images of the chest provides valuable information to specialists. Specifically, chest X-ray images have been the focus of many investigations that apply artificial intelligence techniques for the automatic classification of this disease. The results achieved to date on the subject are promising. However, some results of these investigations contain errors that must be corrected to obtain appropriate models for clinical use. This research discusses some of the problems found in the current scientific literature on the application of artificial intelligence techniques in the automatic classification of COVID-19. It is evident that in most of the reviewed works an incorrect evaluation protocol is applied, which leads to overestimating the results.

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          Most cited references31

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          ImageNet Large Scale Visual Recognition Challenge

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            Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges

            Highlights • Emergence of 2019 novel coronavirus (2019-nCoV) in China has caused a large global outbreak and major public health issue. • At 9 February 2020, data from the WHO has shown >37 000 confirmed cases in 28 countries (>99% of cases detected in China). • 2019-nCoV is spread by human-to-human transmission via droplets or direct contact. • Infection estimated to have an incubation period of 2–14 days and a basic reproduction number of 2.24–3.58. • Controlling infection to prevent spread of the 2019-nCoV is the primary intervention being used.
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              Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases

              Background Chest CT is used for diagnosis of 2019 novel coronavirus disease (COVID-19), as an important complement to the reverse-transcription polymerase chain reaction (RT-PCR) tests. Purpose To investigate the diagnostic value and consistency of chest CT as compared with comparison to RT-PCR assay in COVID-19. Methods From January 6 to February 6, 2020, 1014 patients in Wuhan, China who underwent both chest CT and RT-PCR tests were included. With RT-PCR as reference standard, the performance of chest CT in diagnosing COVID-19 was assessed. Besides, for patients with multiple RT-PCR assays, the dynamic conversion of RT-PCR results (negative to positive, positive to negative, respectively) was analyzed as compared with serial chest CT scans for those with time-interval of 4 days or more. Results Of 1014 patients, 59% (601/1014) had positive RT-PCR results, and 88% (888/1014) had positive chest CT scans. The sensitivity of chest CT in suggesting COVID-19 was 97% (95%CI, 95-98%, 580/601 patients) based on positive RT-PCR results. In patients with negative RT-PCR results, 75% (308/413) had positive chest CT findings; of 308, 48% were considered as highly likely cases, with 33% as probable cases. By analysis of serial RT-PCR assays and CT scans, the mean interval time between the initial negative to positive RT-PCR results was 5.1 ± 1.5 days; the initial positive to subsequent negative RT-PCR result was 6.9 ± 2.3 days). 60% to 93% of cases had initial positive CT consistent with COVID-19 prior (or parallel) to the initial positive RT-PCR results. 42% (24/57) cases showed improvement in follow-up chest CT scans before the RT-PCR results turning negative. Conclusion Chest CT has a high sensitivity for diagnosis of COVID-19. Chest CT may be considered as a primary tool for the current COVID-19 detection in epidemic areas. A translation of this abstract in Farsi is available in the supplement. - ترجمه چکیده این مقاله به فارسی، در ضمیمه موجود است.
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                Author and article information

                Contributors
                josedaniellc@uclv.cu
                rorozco@uclv.cu
                jportal@uclv.cu
                lovelle@infomed.sld.cu
                mperez@uclv.cu
                Journal
                Health Technol (Berl)
                Health Technol (Berl)
                Health and Technology
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                2190-7188
                2190-7196
                5 February 2021
                : 1-14
                Affiliations
                [1 ]GRID grid.411059.8, Centro de Investigaciones de la Informática, Facultad de Matemática, Física y Computación, , Universidad Central “Marta Abreu” de Las Villas, ; Santa Clara, Villa Clara Cuba
                [2 ]GRID grid.411059.8, Departamento de Control Automático, Facultad de Ingeniería Eléctrica, , Universidad Central “Marta Abreu” de Las Villas, ; Villa Clara, Santa Clara, Cuba
                [3 ]Departamento de Imagenología, Hospital Comandante Manuel Fajardo Rivero, Villa Clara, Santa Clara, Cuba
                Author information
                http://orcid.org/0000-0003-2137-0361
                http://orcid.org/0000-0002-6240-1569
                http://orcid.org/0000-0003-1360-4930
                http://orcid.org/0000-0003-3944-3514
                http://orcid.org/0000-0002-3706-9154
                Article
                520
                10.1007/s12553-021-00520-2
                7864619
                33585153
                43ce3dac-0b88-4e37-8c97-82b715453143
                © IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2021

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 27 November 2020
                : 11 January 2021
                Categories
                Original Paper

                covid-19,chest x-rays,artificial intelligence,deep learning

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