scholarly journals Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology

2021 ◽  
Author(s):  
Amaka C. Offiah
Keyword(s):  
Artificial Intelligence ◽  
Pediatric Radiology ◽  
Bone Age ◽  
Future Research ◽  
Fracture Detection ◽  
Musculoskeletal Radiology ◽  
Age Assessment ◽  
Adult World ◽  
Bone Age Assessment

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.

scholarly journals Automated Bone Age Assessment: Motivation, Taxonomies, and Challenges

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Marjan Mansourvar ◽  
Maizatul Akmar Ismail ◽  
Tutut Herawan ◽  
Ram Gopal Raj ◽  
Sameem Abdul Kareem ◽  
...  
Keyword(s):  
Bone Age ◽  
Future Research ◽  
Age Assessment ◽  
X Ray ◽  
Left Hand ◽  
Bone Age Assessment ◽  
Manual Methods ◽  
Biological Maturity ◽  
Automated Methods ◽  
Experimental Stage

Bone age assessment (BAA) of unknown people is one of the most important topics in clinical procedure for evaluation of biological maturity of children. BAA is performed usually by comparing an X-ray of left hand wrist with an atlas of known sample bones. Recently, BAA has gained remarkable ground from academia and medicine. Manual methods of BAA are time-consuming and prone to observer variability. This is a motivation for developing automated methods of BAA. However, there is considerable research on the automated assessment, much of which are still in the experimental stage. This survey provides taxonomy of automated BAA approaches and discusses the challenges. Finally, we present suggestions for future research.

Bone age assessment practices in infants and older children among Society for Pediatric Radiology members

2016 ◽  
Vol 46 (9) ◽  
pp. 1269-1274 ◽  
Author(s):  
Micheál A. Breen ◽  
Andy Tsai ◽  
Aymeric Stamm ◽  
Paul K. Kleinman
Keyword(s):  
Pediatric Radiology ◽  
Bone Age ◽  
Assessment Practices ◽  
Older Children ◽  
Age Assessment ◽  
Bone Age Assessment

Validation study of automated bone age assessment in 1285 children and adolescents aged 5 to 16 years

2019 ◽  
Author(s):  
Klara Maratova ◽  
Dana Zemkova ◽  
Jan Lebl ◽  
Ondrej Soucek ◽  
Stepanka Pruhova ◽  
...  
Keyword(s):  
Children And Adolescents ◽  
Validation Study ◽  
Bone Age ◽  
Age Assessment ◽  
Bone Age Assessment

scholarly journals Intelligent Bone Age Assessment: An Automated System to Detect a Bone Growth Problem Using Convolutional Neural Networks with Attention Mechanism

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 765
Author(s):  
Mohd Asyraf Zulkifley ◽  
Nur Ayuni Mohamed ◽  
Siti Raihanah Abdani ◽  
Nor Azwan Mohamed Kamari ◽  
Asraf Mohamed Moubark ◽  
...  
Keyword(s):  
Bone Growth ◽  
Bone Age ◽  
Attention Mechanism ◽  
Assessment System ◽  
Dominant Hand ◽  
Age Assessment ◽  
Image Normalization ◽  
X Ray ◽  
Bone Age Assessment ◽  
Skeletal Bone

Skeletal bone age assessment using X-ray images is a standard clinical procedure to detect any anomaly in bone growth among kids and babies. The assessed bone age indicates the actual level of growth, whereby a large discrepancy between the assessed and chronological age might point to a growth disorder. Hence, skeletal bone age assessment is used to screen the possibility of growth abnormalities, genetic problems, and endocrine disorders. Usually, the manual screening is assessed through X-ray images of the non-dominant hand using the Greulich–Pyle (GP) or Tanner–Whitehouse (TW) approach. The GP uses a standard hand atlas, which will be the reference point to predict the bone age of a patient, while the TW uses a scoring mechanism to assess the bone age using several regions of interest information. However, both approaches are heavily dependent on individual domain knowledge and expertise, which is prone to high bias in inter and intra-observer results. Hence, an automated bone age assessment system, which is referred to as Attention-Xception Network (AXNet) is proposed to automatically predict the bone age accurately. The proposed AXNet consists of two parts, which are image normalization and bone age regression modules. The image normalization module will transform each X-ray image into a standardized form so that the regressor network can be trained using better input images. This module will first extract the hand region from the background, which is then rotated to an upright position using the angle calculated from the four key-points of interest. Then, the masked and rotated hand image will be aligned such that it will be positioned in the middle of the image. Both of the masked and rotated images will be obtained through existing state-of-the-art deep learning methods. The last module will then predict the bone age through the Attention-Xception network that incorporates multiple layers of spatial-attention mechanism to emphasize the important features for more accurate bone age prediction. From the experimental results, the proposed AXNet achieves the lowest mean absolute error and mean squared error of 7.699 months and 108.869 months2, respectively. Therefore, the proposed AXNet has demonstrated its potential for practical clinical use with an error of less than one year to assist the experts or radiologists in evaluating the bone age objectively.

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