Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study

Chest x-rays are widely used in clinical practice; however, interpretation can be hindered by human error and a lack of experienced thoracic radiologists. Deep learning has the potential to improve the accuracy of chest x-ray interpretation. We therefore aimed to assess the accuracy of radiologists...

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Veröffentlicht in:	The Lancet (British edition) 2021-08, Vol.3 (8), p.e496-e506
Hauptverfasser:	Seah, Jarrel C Y, Tang, Cyril H M, Buchlak, Quinlan D, Holt, Xavier G, Wardman, Jeffrey B, Aimoldin, Anuar, Esmaili, Nazanin, Ahmad, Hassan, Pham, Hung, Lambert, John F, Hachey, Ben, Hogg, Stephen J F, Johnston, Benjamin P, Bennett, Christine, Oakden-Rayner, Luke, Brotchie, Peter, Jones, Catherine M
Format:	Artikel
Sprache:	eng
Schlagworte:	Adolescent Adult Aged Aged, 80 and over Area Under Curve Artificial Intelligence Deep Learning Female Humans Infections - diagnosis Infections - diagnostic imaging Male Mass Screening - methods Middle Aged Models, Biological Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Radiologists Retrospective Studies ROC Curve Thoracic Injuries - diagnosis Thoracic Injuries - diagnostic imaging Thoracic Neoplasms - diagnosis Thoracic Neoplasms - diagnostic imaging X-Rays Young Adult
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