Comparing a deep learning model's diagnostic performance to that of radiologists to detect Covid -19 features on chest radiographs.

Krishnamoorthy, Sabitha; Ramakrishnan, Sudhakar; Colaco, Lanson Brijesh; Dias, Akshay; Gopi, Indu K; Gowda, Gautham A G; Aishwarya, K C; Ramanan, Veena; Chandran, Manju

Krishnamoorthy, Sabitha; Ramakrishnan, Sudhakar; Colaco, Lanson Brijesh; Dias, Akshay; Gopi, Indu K; Gowda, Gautham A G; Aishwarya, K C; Ramanan, Veena; Chandran, Manju.

Afiliação

Krishnamoorthy S; Department of Internal Medicine, Saroja Multispecialty Hospital, Thrissur, Kerala, India.
Ramakrishnan S; Department of Computer Science Alumni, West Virginia University, WV, USA.
Colaco LB; K.V.G Medical College, Sullia, Rajiv Gandhi University of Health Sciences, Bangalore, India.
Dias A; Department of General Medicine, Father Muller Medical College Hospital, Mangalore, Karnataka, India.
Gopi IK; Jubilee Centre of Medical Research, Jubilee Mission Medical College and Research Institute, Thrissur, Kerala, India.
Gowda GAG; Department of Radiodiagnosis, K.V.G Medical College and Hospital, Sullia, Karnataka, India.
Aishwarya KC; Department of Radiodiagnosis, K.V.G Medical College and Hospital, Sullia, Karnataka, India.
Ramanan V; Department of Radiodiagnosis, Travancore Scans, Thiruvananthapuram, Kerala, India.
Chandran M; Osteoporosis and Bone Metabolism Unit, Department of Endocrinology, Division of Internal Medicine, Singapore General Hospital, Singapore.

Indian J Radiol Imaging ; 31(Suppl 1): S53-S60, 2021 Jan.

Article em En | MEDLINE | ID: mdl-33814762

Palavras-chave

Artificial intelligence; COVID 19; CXR; deep learning

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Revista: Indian J Radiol Imaging Ano de publicação: 2021 Tipo de documento: Article

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