Evaluation of the models generated from clinical features and deep learning-based segmentations: Can thoracic CT on admission help us to predict hospitalized COVID-19 patients who will require intensive care?

Gülbay, Mutlu; Bastug, Aliye; Özkan, Erdem; Öztürk, Büsra Yüce; Mendi, Bökebatur Ahmet Rasit; Bodur, Hürrem

Gülbay, Mutlu; Bastug, Aliye; Özkan, Erdem; Öztürk, Büsra Yüce; Mendi, Bökebatur Ahmet Rasit; Bodur, Hürrem.

Afiliación

Gülbay M; Department of Radiology, Ankara City Hospital, Üniversiteler Mahallesi 1604. Cadde No: 9, 06800, Çankaya, Ankara, Turkey. drgulbay@gmail.com.
Bastug A; Department of Infectious Diseases and Clinical Microbiology, University of Health Sciences Turkey, Gülhane Faculty of Medicine, Ankara City Hospital, Ankara, Turkey.
Özkan E; Department of Radiology, Ankara City Hospital, Üniversiteler Mahallesi 1604. Cadde No: 9, 06800, Çankaya, Ankara, Turkey.
Öztürk BY; Department of Clinical Microbiology and Infectious Diseases, Ankara City Hospital, Ankara, Turkey.
Mendi BAR; Department of Radiology, Ankara City Hospital, Üniversiteler Mahallesi 1604. Cadde No: 9, 06800, Çankaya, Ankara, Turkey.
Bodur H; Department of Infectious Diseases and Clinical Microbiology, University of Health Sciences Turkey, Gülhane Faculty of Medicine, Ankara City Hospital, Ankara, Turkey.

BMC Med Imaging ; 22(1): 110, 2022 06 07.

Article en En | MEDLINE | ID: mdl-35672719

Asunto(s)

COVID-19; Aprendizaje Profundo; Inteligencia Artificial; Teorema de Bayes; COVID-19/diagnóstico por imagen; Cuidados Críticos; Humanos; Estudios Retrospectivos; SARS-CoV-2; Tomografía Computarizada por Rayos X/métodos

Palabras clave

Artificial intelligence; COVID-19; Computed tomography; Deep learning; Logistic regression models; Machine learning; Radiomics

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo / COVID-19 Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: BMC Med Imaging Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2022 Tipo del documento: Article País de afiliación: Turquía Pais de publicación: Reino Unido

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