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      Diagnosis of COVID-19 with simultaneous accurate prediction of cardiac abnormalities from chest computed tomographic images

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          Abstract

          COVID-19 has potential consequences on the pulmonary and cardiovascular health of millions of infected people worldwide. Chest computed tomographic (CT) imaging has remained the first line of diagnosis for individuals infected with SARS-CoV-2. However, differentiating COVID-19 from other types of pneumonia and predicting associated cardiovascular complications from the same chest-CT images have remained challenging. In this study, we have first used transfer learning method to distinguish COVID-19 from other pneumonia and healthy cases with 99.2% accuracy. Next, we have developed another CNN-based deep learning approach to automatically predict the risk of cardiovascular disease (CVD) in COVID-19 patients compared to the normal subjects with 97.97% accuracy. Our model was further validated against cardiac CT-based markers including cardiac thoracic ratio (CTR), pulmonary artery to aorta ratio (PA/A), and presence of calcified plaque. Thus, we successfully demonstrate that CT-based deep learning algorithms can be employed as a dual screening diagnostic tool to diagnose COVID-19 and differentiate it from other pneumonia, and also predicts CVD risk associated with COVID-19 infection.

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          Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China

          Chaolin Huang, Yeming Wang, Xingwang Li … (2020)
          Summary Background A recent cluster of pneumonia cases in Wuhan, China, was caused by a novel betacoronavirus, the 2019 novel coronavirus (2019-nCoV). We report the epidemiological, clinical, laboratory, and radiological characteristics and treatment and clinical outcomes of these patients. Methods All patients with suspected 2019-nCoV were admitted to a designated hospital in Wuhan. We prospectively collected and analysed data on patients with laboratory-confirmed 2019-nCoV infection by real-time RT-PCR and next-generation sequencing. Data were obtained with standardised data collection forms shared by WHO and the International Severe Acute Respiratory and Emerging Infection Consortium from electronic medical records. Researchers also directly communicated with patients or their families to ascertain epidemiological and symptom data. Outcomes were also compared between patients who had been admitted to the intensive care unit (ICU) and those who had not. Findings By Jan 2, 2020, 41 admitted hospital patients had been identified as having laboratory-confirmed 2019-nCoV infection. Most of the infected patients were men (30 [73%] of 41); less than half had underlying diseases (13 [32%]), including diabetes (eight [20%]), hypertension (six [15%]), and cardiovascular disease (six [15%]). Median age was 49·0 years (IQR 41·0–58·0). 27 (66%) of 41 patients had been exposed to Huanan seafood market. One family cluster was found. Common symptoms at onset of illness were fever (40 [98%] of 41 patients), cough (31 [76%]), and myalgia or fatigue (18 [44%]); less common symptoms were sputum production (11 [28%] of 39), headache (three [8%] of 38), haemoptysis (two [5%] of 39), and diarrhoea (one [3%] of 38). Dyspnoea developed in 22 (55%) of 40 patients (median time from illness onset to dyspnoea 8·0 days [IQR 5·0–13·0]). 26 (63%) of 41 patients had lymphopenia. All 41 patients had pneumonia with abnormal findings on chest CT. Complications included acute respiratory distress syndrome (12 [29%]), RNAaemia (six [15%]), acute cardiac injury (five [12%]) and secondary infection (four [10%]). 13 (32%) patients were admitted to an ICU and six (15%) died. Compared with non-ICU patients, ICU patients had higher plasma levels of IL2, IL7, IL10, GSCF, IP10, MCP1, MIP1A, and TNFα. Interpretation The 2019-nCoV infection caused clusters of severe respiratory illness similar to severe acute respiratory syndrome coronavirus and was associated with ICU admission and high mortality. Major gaps in our knowledge of the origin, epidemiology, duration of human transmission, and clinical spectrum of disease need fulfilment by future studies. Funding Ministry of Science and Technology, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, and Beijing Municipal Science and Technology Commission.
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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

            Tao Ai, Zhenlu Yang, Hongyan Hou … (2020)
            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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              COVID-19 and the cardiovascular system

              Ying-Ying Zheng, Yi-Tong Ma, Jin-Ying Zhang … (2020)
              Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects host cells through ACE2 receptors, leading to coronavirus disease (COVID-19)-related pneumonia, while also causing acute myocardial injury and chronic damage to the cardiovascular system. Therefore, particular attention should be given to cardiovascular protection during treatment for COVID-19.
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                Author and article information

                Contributors
                Moumita Moitra: Role: Data curationRole: InvestigationRole: MethodologyRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Maha Alafeef: Role: ConceptualizationRole: InvestigationRole: MethodologyRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Arjun Narasimhan: Role: InvestigationRole: Methodology
                Vikram Kakaria: Role: InvestigationRole: Methodology
                Parikshit Moitra: Role: Writing – original draftRole: Writing – review & editing
                Dipanjan Pan:
                ORCID: https://orcid.org/0000-0003-0175-4704
                Role: ConceptualizationRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Mohammad Amin Fraiwan: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS One
                Journal ID (publisher-id): plos
                Title: PLOS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 14 December 2023
                Publication date Collection: 2023
                Volume: 18
                Issue: 12
                Electronic Location Identifier: e0290494
                Affiliations
                [1 ] Center for Blood Oxygen Transport and Hemostasis, Department of Pediatrics, University of Maryland Baltimore School of Medicine, Baltimore, Maryland, United States of America
                [2 ] Department of Chemical, Biochemical and Environmental Engineering, University of Maryland Baltimore County, Baltimore, Maryland, United States of America
                [3 ] Biomedical Engineering Department, Jordan University of Science and Technology, Irbid, Jordan
                [4 ] Department of Nuclear Engineering, The Pennsylvania State University, State College, Pennsylvania, United States of America
                [5 ] Department of Materials Science & Engineering, The Pennsylvania State University, State College, Pennsylvania, United States of America
                [6 ] Huck Institutes of the Life Sciences, State College, Pennsylvania, United States of America
                Jordan University of Science and Technology, JORDAN
                Author notes

                Competing Interests: D.P. is the founder/co-founder of three University-based start‐ups. None of these entities, however, supported this work. All the authors thus declare no competing interests.

                ‡ AN and VK also contributed equally to this work.

                Author information
                https://orcid.org/0000-0003-0175-4704
                Article
                Publisher ID: PONE-D-23-14305
                DOI: 10.1371/journal.pone.0290494
                PMC ID: 10721010
                PubMed ID: 38096254
                SO-VID: a08fded5-a42e-48c3-ab5b-ee712888b61a
                Copyright © © 2023 Moitra et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 10 May 2023
                Date accepted : 9 August 2023
                Page count
                Figures: 6, Tables: 1, Pages: 19
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000090, Congressionally Directed Medical Research Programs;
                Award Recipient :
                ORCID: https://orcid.org/0000-0003-0175-4704
                Funded by: funder-id http://dx.doi.org/10.13039/100017046, School of Medicine, University of Maryland;
                Award Recipient :
                ORCID: https://orcid.org/0000-0003-0175-4704
                Funded by: funder-id http://dx.doi.org/10.13039/100006636, University of Maryland, Baltimore County;
                Award Recipient :
                ORCID: https://orcid.org/0000-0003-0175-4704
                Funded by: funder-id http://dx.doi.org/10.13039/100008321, Pennsylvania State University;
                This research was funded partially by Congressionally Directed Medical Research Program (CDMRP), University of Maryland School of Medicine, University of Maryland Baltimore County and The Pennsylvania State University.
                Categories
                Subject: Research Article
                Subject: Medicine and Health Sciences
                Subject: Medical Conditions
                Subject: Infectious Diseases
                Subject: Viral Diseases
                Subject: Covid 19
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Neuroimaging
                Subject: Computed Axial Tomography
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Neuroimaging
                Subject: Computed Axial Tomography
                Subject: Medicine and Health Sciences
                Subject: Diagnostic Medicine
                Subject: Diagnostic Radiology
                Subject: Tomography
                Subject: Computed Axial Tomography
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Diagnostic Radiology
                Subject: Tomography
                Subject: Computed Axial Tomography
                Subject: Medicine and Health Sciences
                Subject: Radiology and Imaging
                Subject: Diagnostic Radiology
                Subject: Tomography
                Subject: Computed Axial Tomography
                Subject: Medicine and Health Sciences
                Subject: Pulmonology
                Subject: Pneumonia
                Subject: Medicine and Health Sciences
                Subject: Diagnostic Medicine
                Subject: Virus Testing
                Subject: Computer and Information Sciences
                Subject: Artificial Intelligence
                Subject: Machine Learning
                Subject: Medicine and Health Sciences
                Subject: Medical Conditions
                Subject: Cardiovascular Diseases
                Subject: Medicine and Health Sciences
                Subject: Cardiology
                Subject: Cardiovascular Medicine
                Subject: Cardiovascular Diseases
                Subject: Medicine and Health Sciences
                Subject: Diagnostic Medicine
                Subject: Computer and Information Sciences
                Subject: Artificial Intelligence
                Subject: Machine Learning
                Subject: Support Vector Machines
                Custom metadata

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                ScienceOpen disciplines: Uncategorized

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