{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:36:28Z","timestamp":1767339388184},"reference-count":16,"publisher":"Georg Thieme Verlag KG","issue":"04","license":[{"start":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T00:00:00Z","timestamp":1641772800000},"content-version":"vor","delay-in-days":101,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Indian J Radiol Imaging"],"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>\n          Background\u2003Evaluation of suspected coronavirus disease-2019 (COVID-19) patient is a diagnostic dilemma as it commonly presents like influenza in early stages. Studies and guidelines have emerged both for and against the use of imaging as a frontline tool to investigate such patients. Reverse transcriptase-polymerase chain reaction (RT-PCR) is suggested as the backbone of diagnosis. We designed and tested a diagnostic algorithm using artificial intelligence (AI) to determine the role of imaging in the evaluation of patients with acute flu-like presentation.<\/jats:p><jats:p>\n          Materials and Methods\u2003Overall, 3,235 consecutive patients with flu-like presentation were evaluated over a period of 240 days. All patients underwent plain radiographs of chest with computer-aided detection for COVID-19 (CAD4COVID) AI analysis. Based on the threshold scores, they were divided into two groups: group A (score\u2009&lt;\u200950) and group B (score\u2009&gt;\u200950). Group A patients were discharged and put on routine symptomatic treatment and follow-up with RT-PCR, while group B patients underwent high-resolution computed tomography (HRCT) followed by COVID-19 AI analysis and RT-PCR test. These were then triaged into COVID-19 and non-COVID-19 subgroups based on COVID-19 similarity scores by AI, and lung severity scores were also determined.<\/jats:p><jats:p>\n          Results\u2003Group A had 2,209 (68.3%) patients with CAD4COVID score of &lt;50 while 1,026 (31.7%) patients comprised group B. Also, 825 (25.5%) patients were COVID-19 positive with COVID-19 similarity threshold of &gt;0.85 on AI. RT-PCR was positive in 415 and false-negative in 115 patients while 12 patients died before the test could be done. The sensitivity and specificity of CAD4COVID AI analysis on plain radiographs for detection of any lung abnormality combined with HRCT AI analysis was 97.9% and 99% using the above algorithm.<\/jats:p><jats:p>\n          Conclusion\u2003Combined use of chest radiographs and plain HRCT with AI-based analysis is useful and an accurate frontline tool to triage patients with acute flu-like symptoms in non-COVID-19 health care facilities.<\/jats:p>","DOI":"10.1055\/s-0041-1741103","type":"journal-article","created":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T23:58:42Z","timestamp":1641859122000},"page":"901-909","source":"Crossref","is-referenced-by-count":4,"title":["Use of Artificial Intelligence to Triage Patients with Flu-Like Symptoms Using Imaging in Non-COVID-19 Hospitals during COVID-19 Pandemic: An Ongoing 8-Month Experience"],"prefix":"10.1055","volume":"31","author":[{"given":"Atul","family":"Kapoor","sequence":"additional","affiliation":[{"name":"Department of Radiology, Advanced Diagnostics and Institute of Imaging, Amritsar, Punjab, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aprajita","family":"Kapoor","sequence":"additional","affiliation":[{"name":"Department of Radiology, Advanced Diagnostics and Institute of Imaging, Amritsar, Punjab, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Goldaa","family":"Mahajan","sequence":"additional","affiliation":[{"name":"Department of Radiology, Advanced Diagnostics and Institute of Imaging, Amritsar, Punjab, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"194","published-online":{"date-parts":[[2022,1,10]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/S0140-6736(20)30211-7","article-title":"Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study","volume":"395","author":"N Chen","year":"2020","journal-title":"Lancet"},{"issue":"13","key":"ref2","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1056\/NEJMoa2001316","article-title":"Early transmission dynamics in Wuhan, China of Novel corona virus-infected pneumonia","volume":"382","author":"Q Li","year":"2020","journal-title":"N Engl J Med"},{"issue":"10","key":"ref3","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1056\/NEJMoa2001191","article-title":"First case of 2019 novel coronavirus in the United States","volume":"382","author":"M L Holshue","year":"2020","journal-title":"N Engl J Med"},{"issue":"11","key":"ref4","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1001\/jama.2020.1585","article-title":"Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China","volume":"323","author":"D Wang","year":"2020","journal-title":"JAMA"},{"issue":"02","key":"ref5","doi-asserted-by":"crossref","first-page":"E32","DOI":"10.1148\/radiol.2020200642","article-title":"Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases","volume":"296","author":"T Ai","year":"2020","journal-title":"Radiology"},{"issue":"02","key":"ref6","doi-asserted-by":"crossref","first-page":"E115","DOI":"10.1148\/radiol.2020200432","article-title":"Sensitivity of chest CT for COVID-19: comparison to RT-PCR","volume":"296","author":"Y Fang","year":"2020","journal-title":"Radiology"},{"issue":"06","key":"ref7","doi-asserted-by":"crossref","first-page":"3306","DOI":"10.1007\/s00330-020-06731-x","article-title":"Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): a study of 63 patients in Wuhan, China","volume":"30","author":"Y Pan","year":"2020","journal-title":"Eur Radiol"},{"issue":"06","key":"ref8","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1097\/RLI.0000000000000674","article-title":"Clinical and high-resolution CT features of the COVID-19 infection: comparison of the initial and follow-up changes","volume":"55","author":"Y Xiong","year":"2020","journal-title":"Invest Radiol"},{"key":"ref9","first-page":"E001","article-title":"Radiological diagnosis of new coronavirus infected pneumonitis: expert recommendation from the Chinese Society of Radiology","volume":"54","author":"Chinese Society of Radiology","year":"2020","journal-title":"Zhonghua Fang She Xue Za Zhi"},{"issue":"18","key":"ref12","doi-asserted-by":"crossref","first-page":"1708","DOI":"10.1056\/NEJMoa2002032","article-title":"Clinical characteristics of coronavirus disease 2019 in China","volume":"382","author":"W J Guan","year":"2020","journal-title":"N Engl J Med"},{"issue":"08","key":"ref13","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial intelligence in radiology","volume":"18","author":"A Hosny","year":"2018","journal-title":"Nat Rev Cancer"},{"issue":"04","key":"ref14","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.dsx.2020.04.012","article-title":"Artificial intelligence (AI) applications for COVID-19 pandemic","volume":"14","author":"R Vaishya","year":"2020","journal-title":"Diabetes Metab Syndr"},{"issue":"01","key":"ref15","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1148\/radiol.2020200230","article-title":"CT imaging features of 2019 novel coronavirus (2019-nCoV)","volume":"295","author":"M Chung","year":"2020","journal-title":"Radiology"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"108774","DOI":"10.1016\/j.ejrad.2019.108774","article-title":"Artificial intelligence applications for thoracic imaging","volume":"123","author":"G Chassagnon","year":"2020","journal-title":"Eur J Radiol"},{"issue":"02","key":"ref17","doi-asserted-by":"crossref","first-page":"e200047","DOI":"10.1148\/ryct.2020200047","article-title":"Chest CT severity score: an imaging tool for assessing severe COVID19","volume":"2","author":"R Yang","year":"2020","journal-title":"Radiol Cardiothorac Imaging"},{"issue":"05","key":"ref18","doi-asserted-by":"crossref","first-page":"e0124122","DOI":"10.1371\/journal.pone.0124122","article-title":"Dynamics of influenza seasonality at sub-regional levels in India and implications for vaccination timing","volume":"10","author":"M S Chadha","year":"2015","journal-title":"PLoS One"}],"container-title":["Indian Journal of Radiology and 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