Modernization of bone age assessment: comparing the accuracy and reliability of an artificial intelligence algorithm and shorthand bone age to Greulich and Pyle

2020 ◽  
Vol 49 (9) ◽  
pp. 1449-1457
Author(s):  
Mina Gerges ◽  
Hayley Eng ◽  
Harpreet Chhina ◽  
Anthony Cooper
Author(s):  
Amaka C. Offiah

AbstractArtificial intelligence (AI) is playing an ever-increasing role in radiology (more so in the adult world than in pediatrics), to the extent that there are unfounded fears it will completely take over the role of the radiologist. In relation to musculoskeletal applications of AI in pediatric radiology, we are far from the time when AI will replace radiologists; even for the commonest application (bone age assessment), AI is more often employed in an AI-assist mode rather than an AI-replace or AI-extend mode. AI for bone age assessment has been in clinical use for more than a decade and is the area in which most research has been conducted. Most other potential indications in children (such as appendicular and vertebral fracture detection) remain largely in the research domain. This article reviews the areas in which AI is most prominent in relation to the pediatric musculoskeletal system, briefly summarizing the current literature and highlighting areas for future research. Pediatric radiologists are encouraged to participate as members of the research teams conducting pediatric radiology artificial intelligence research.


2020 ◽  
Vol 2 (4) ◽  
pp. e190198
Author(s):  
Ian Pan ◽  
Grayson L. Baird ◽  
Simukayi Mutasa ◽  
Derek Merck ◽  
Carrie Ruzal-Shapiro ◽  
...  

2007 ◽  
Author(s):  
Aifeng Zhang ◽  
Sinchai Tsao ◽  
James W. Sayre ◽  
Arkadiusz Gertych ◽  
Brent J. Liu ◽  
...  

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