[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128029-en":3,"doc-seo-128029-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128029,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Added value of chest CT in a machine learning-based prediction model to rule out COVID-19 before inpatient admission","Purpose: Hospitals need timely identification of COVID-19 before inpatient admission. This study evaluates whether an artificial intelligence approach using a clinical network can improve pandemic preparedness. Method: Supervised machine learning built diagnostic models from 4,437 participants across 12 German centers (2017–2020), using chest CT, clinical examination and laboratory tests, or all features, with RT-PCR as reference. Results: Adding chest CT significantly increased sensitivity, specificity, NPV, and AUC while reducing false negatives. Conclusions: Chest CT features strengthen rule-out effectiveness prior to ward admission.","European Journal of Radiology 163 (2023) 110827  \nContents lists available at ScienceDirect  \nEuropean Journal of Radiology  \njournal [homepage:](homepage: www.elsevier.com/locate/ejrad)[ www.elsevier.com/locate/ejrad](homepage: www.elsevier.com/locate/ejrad)  \n| Added value of chest CT in a machine learning-based prediction model to   rule out COVID-19 before inpatient admission: A retrospective university\u003Cbr>network study\u003Cbr>Martin Kr¨amer, Maja Ingwersen, Ulf Teichgr¨aber, Felix Güttler *, RACOON consortium1 Department of Radiology, Friedrich Schiller University, Jena University Hospital, Jena, Germany |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Artificial intelligence Computed tomography COVID-19\u003Cbr>Machine learning\u003Cbr>Predictive value of tests |  | Purpose: During the coronavirus disease 2019 (COVID-19) pandemic, hospitals still face the challenge of timely identification of infected individuals before inpatient admission. An artificial intelligence approach based on an established clinical network may improve prospective pandemic preparedness.\u003Cbr>Method: Supervised machine learning was used to construct diagnostic models to predict COVID-19. A pooled database was retrospectively generated from 4437 participant data that were collected between January 2017 and October 2020 at 12 German centers that belong to the radiological cooperative network of the COVID-19 (RACOON) consortium. A total of 692 (15.6 %) participants were COVID-19 positive according to the reference of the reverse transcription-polymerase chain reaction test. The diagnostic models included chest CT features (model R), clinical examination and laboratory test features (model CL), or all three feature categories (model RCL). Performance outcomes included accuracy, sensitivity, specificity, negative and positive predictive value, and area under the receiver operating curve (AUC).\u003Cbr>Results: Performance of predictive models improved significantly by adding chest CT features to clinical evaluation and laboratory test features. Without (model CL) and with inclusion of chest CT (model RCL), sensitivity was 0.82 and 0.89 (p \u003C 0.0001), specificity was 0.84 and 0.89 (p \u003C 0.0001), negative predictive value was 0.96 and 0.97 (p \u003C 0.0001), AUC was 0.92 and 0.95 (p \u003C 0.0001), and proportion of false negative classifications was 2.6 % and 1.7 %(p \u003C 0.0001), respectively.\u003Cbr>Conclusions: Addition of chest CT features to machine learning-based predictive models improves the effectiveness in ruling out COVID-19 before inpatient admission to regular wards. |\n\nAbbreviations: AUC, area under receiver operating characteristics curve; COVID-19, coronavirus disease 2019; Model CL, model included clinical examination and laboratory test features; Model R, model included chest CT features; Model RCL, model included chest CT-, clinical examination-, and laboratory test features; NPV, negative predictive value; PPV, positive predictive value; RACOON, radiological cooperative network; RT-PCR, reverse transcription-polymerase chain reaction; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.  \n* Corresponding author at: Friedrich-Schiller-University, Jena University Hospital, Department of Radiology, Am Klinikum 1, 07747 Jena, Germany.  \n[E-mail addresses:](E-mail addresses: martin.kraemer@med.uni-jena.de)[ martin.kraemer@med.uni-jena.de](E-mail addresses: martin.kraemer@med.uni-jena.de) (M. Kr¨amer), maja.ingwersen@gmx.de (M. Ingwersen), ulf.teichgraeber@med.uni-jena.de (U. Teichgr¨aber), [felix.guettler@med.uni-jena.de](felix.guettler@med.uni-jena.de) (F. Güttler).  \n1 RACOON consortium: Alexander Gussew, Alexander K¨onig, Alexey Surov, Andreas Bucher, Andreas Mahnken, Arno Bücker, Bernd Hamm, Birte Valentin, Christian Stroszczynski, Christiane Kuhl, Christoph Düber, Christopher Kloth, Daniel Kütting, David Maintz, Elmar Kotter, Evelyn Bohrer, Fabian Bamberg, Felix Güttler, Felix Meinel, Florian Schwarz, Frank Wacker, Frederik","cbCaiaN4n5mwwErJ","https://ap.wps.com/l/cbCaiaN4n5mwwErJ","pdf",1975511,1,7,"English","en",105,"# Abstract\n## Purpose\n## Method\n## Results\n## Conclusions\n# Introduction","[{\"question\":\"What problem does this study address during the COVID-19 pandemic?\",\"answer\":\"Hospitals face the challenge of identifying infected individuals quickly before inpatient admission to limit in-hospital spread.\"},{\"question\":\"How were the machine learning diagnostic models built and validated?\",\"answer\":\"Supervised machine learning constructed models using retrospectively pooled participant data, with RT-PCR used as the reference standard.\"},{\"question\":\"What is the impact of adding chest CT features to the predictive model?\",\"answer\":\"Including chest CT features significantly improved sensitivity, specificity, negative predictive value, and AUC, and reduced the proportion of false negatives.\"}]","Added value of chest CT in a machine learning-based prediction model to rule out COVID-19 before inpatient admission | 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