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Yoichi Sato et al.
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Yu-Cheng Tung et al.
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Nils Hendrix et al.
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Eyal Klang et al.
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Shelly Soffer et al.
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Chi-Tung Cheng et al.
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M. Diya Sabbagh et al.
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Brian Gibney et al.
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The utility of cross-sectional imaging in the management of suspected scaphoid fractures
Asanka R. Wijetunga et al.
JOURNAL OF MEDICAL RADIATION SCIENCES (2019)
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Morgan P. McBee et al.
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Matthew D. F. McInnes et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2018)
Deep learning and medical imaging
Eyal Klang
JOURNAL OF THORACIC DISEASE (2018)
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Mark Christopher et al.
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Geert Litjens et al.
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Comparison of MRI, CT and bone scintigraphy for suspected scaphoid fractures
A. D. de Zwart et al.
European Journal of Trauma and Emergency Surgery (2016)
Occult fractures of the proximal femur: imaging diagnosis and management of 82 cases in a regional trauma center
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Wrist fractures: sensitivity of radiography, prevalence, and patterns in MDCT
Ali Balci et al.
EMERGENCY RADIOLOGY (2015)
QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies
Penny F. Whiting et al.
ANNALS OF INTERNAL MEDICINE (2011)
'Clinical scaphoid fracture': is it time to abolish this phrase?
S. Shetty et al.
ANNALS OF THE ROYAL COLLEGE OF SURGEONS OF ENGLAND (2011)
Current methods of diagnosis and treatment of scaphoid fractures
Steven J. Rhemrev et al.
INTERNATIONAL JOURNAL OF EMERGENCY MEDICINE (2011)
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement
David Moher et al.
ANNALS OF INTERNAL MEDICINE (2009)
MDCT and radiography of wrist fractures: Radiographic sensitivity and fracture patterns
Rodney D. Welling et al.
AMERICAN JOURNAL OF ROENTGENOLOGY (2008)
Scaphoid fractures and nonunions: diagnosis and treatment
Scott P. Steinmann et al.
JOURNAL OF ORTHOPAEDIC SCIENCE (2006)
Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews
JB Reitsma et al.
JOURNAL OF CLINICAL EPIDEMIOLOGY (2005)
Diagnosis of occult scaphoid fractures and other wrist injuries -: Are repeated clinical examinations and plain radiographs still state of the art?
C Gäbler et al.
LANGENBECKS ARCHIVES OF SURGERY (2001)