Τετάρτη 27 Νοεμβρίου 2019

Radiology: Artificial Intelligence,
November 2019
Volume 1, Issue 6

Meet Dr. Kahn at RSNA on Monday, December 2 at 2:00 PM CST in the Publications Booth (booth 1119)!

Editorials
Charles E. Kahn, Jr
Special Feature
 
Bradley J. Erickson
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Original Research
 
Deep learning allows for fast, robust, and fully automated quantification of epicardial adipose tissue from non–contrast material–enhanced calcium-scoring CT.  
 
Frederic Commandeur, Markus Goeller...Damini Dey
 
A deep learning–based reconstruction method can quantitatively and qualitatively improve the quality of abdominal CT images for the evaluation of hypovascular hepatic metastases.  
 
Yuko Nakamura, Toru Higaki...Kazuo Awai
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Convolutional neural networks can reduce image noise and artifacts to improve low-dose pediatric abdominal CT images reconstructed with filtered back projection.  
 
Robert D. MacDougall, Yanbo Zhang...Hengyong Yu
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Deep learning–based localization is a feasible strategy for planning cardiac MRI planes.  
 
Kevin Blansit, Tara Retson...Albert Hsiao
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Ensembles created using models submitted to an open competition convincingly outperformed single-model prediction for bone age assessment.  
 
Ian Pan, Hans Henrik Thodberg...David B. Larson
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Technical Development
 
A graphical user interface promoted involvement of local-expert subspecialty radiologists in ongoing image data curation, including case-by-case annotation, potentially concurrent with artificial intelligence–enhanced routine image review in support of ongoing model learning.  
 
Mutlu Demirer, Sema Candemir...Barbaros S. Erdal
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