Bone Age Assessment

2013 ◽  
Vol 2 (2) ◽  
pp. 1-10
Author(s):  
S. Kavya ◽  
Pavithra Pugalendi ◽  
Rose Martina P. A. ◽  
N. Sriraam ◽  
K. S. Babu ◽  
...  
Keyword(s):  
Skeletal Development ◽  
Back Propagation ◽  
Bone Age ◽  
Back Propagation Neural Network ◽  
Typical Feature ◽  
Age Assessment ◽  
Left Hand ◽  
Automated Method ◽  
Bone Age Assessment ◽  
The Given

Bone age assessment defined as the measure of skeletal development is most often used in pediatrics and forensics to estimate the true age of a person. It is usually done by comparing the left hand X-ray of a person with the hand radiographs in the standard atlas or based on local regions of interests (ROI) that include epiphyseal regions of the phalanges (14 ROI’s).Both these assessments were labour intensive, prone to discrepancies and can only be used to estimate the age till 18. Hence there is a need to develop automated method to assess the bone age by exploiting the appropriate features. This paper attempts to identify a procedure in recognizing the respective bone that belongs to male or female with its corresponding age. The automated procedure comprises of segmentation of metacarpals using area based statistics followed by typical feature extraction. Nine features are extracted for the experimental study. A back propagation neural network is then applied to classify whether the given sample refers to male or female bone. It is observed from the simulation results that the proposed procedure is found to be less computation burden and the results are found to be comparable with the existing work reported in the literature.

scholarly journals Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks

2017 ◽  
Author(s):  
Vladimir Iglovikov ◽  
Alexander Rakhlin ◽  
Alexandr A. Kalinin ◽  
Alexey Shvets
Keyword(s):  
Skeletal Development ◽  
Bone Age ◽  
Deep Convolutional Neural Networks ◽  
Age Assessment ◽  
Left Hand ◽  
Radiological Society ◽  
Hand Bones ◽  
Bone Age Assessment ◽  
Using Data ◽  
Common Clinical Practice

AbstractSkeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development. In this paper, we describe a fully automated deep learning approach to the problem of bone age assessment using data from the 2017 Pediatric Bone Age Challenge organized by the Radiological Society of North America. The dataset for this competition consists of 12,600 radiological images. Each radiograph in this dataset is an image of a left hand labeled with bone age and sex of a patient. Our approach utilizes several deep neural network architectures trained end-to-end. We use images of whole hands as well as specific parts of a hand for both training and prediction. This approach allows us to measure the importance of specific hand bones for automated bone age analysis. We further evaluate the performance of the suggested method in the context of skeletal development stages. Our approach outperforms other common methods for bone age assessment.

Creation and Validation of a Shorthand Magnetic Resonance Imaging Bone Age Assessment Tool of the Knee as an Alternative Skeletal Maturity Assessment

2021 ◽  
pp. 036354652110329
Author(s):  
Cary S. Politzer ◽  
James D. Bomar ◽  
Hakan C. Pehlivan ◽  
Pradyumna Gurusamy ◽  
Eric W. Edmonds ◽  
...  
Keyword(s):  
Assessment Tool ◽  
Skeletal Maturity ◽  
Bone Age ◽  
Mri Scan ◽  
Perfect Agreement ◽  
Age Assessment ◽  
Left Hand ◽  
Knee Mri ◽  
Bone Age Assessment ◽  
Hand Radiograph

Background: In managing pediatric knee conditions, an accurate bone age assessment is often critical for diagnostic, prognostic, and treatment purposes. Historically, the Greulich and Pyle atlas (hand atlas) has been the gold standard bone age assessment tool. In 2013, a shorthand bone age assessment tool based on this atlas (hand shorthand) was devised as a simpler and more efficient alternative. Recently, a knee magnetic resonance imaging (MRI) bone age atlas (MRI atlas) was created to circumvent the need for a left-hand radiograph. Purpose: To create a shorthand version of the knee MRI atlas. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: A shorthand bone age assessment method was created utilizing the previously published MRI atlas, which utilizes several criteria that are visualized across a series of images. The MRI shorthand draws on characteristic criteria for each age that are best observed on a single MRI scan. For validation, we performed a retrospective assessment of skeletally immature patients. One reader performed the bone age assessment using the MRI atlas and the MRI shorthand on 200 patients. Then, 4 readers performed the bone age assessment with the hand atlas, hand shorthand, MRI atlas, and MRI shorthand on a subset of 22 patients in a blinded fashion. All 22 patients had a knee MRI scan and a left-hand radiograph within 4 weeks of each other. Interobserver and intraobserver reliability, as well as variability among observers, were evaluated. Results: A total of 200 patients with a mean age of 13.5 years (range, 9.08-17.98 years) were included in this study. Also, 22 patients with a mean age of 13.3 years (range, 9.0-15.6 years) had a knee MRI scan and a left-hand radiograph within 4 weeks. The intraobserver and interobserver reliability of all 4 assessment tools were acceptable (intraclass correlation coefficient [ICC] ≥ 0.8; P < .001). When comparing the MRI shorthand with the MRI atlas, there was excellent agreement (ICC = 0.989), whereas the hand shorthand compared with the hand atlas had good agreement (ICC = 0.765). The MRI shorthand also had perfect agreement in 50% of readings among all 4 readers, and 95% of readings had agreement within 1 year, whereas the hand shorthand had perfect agreement in 32% of readings and 77% agreement within 1 year. Conclusion: The MRI shorthand is a simple and efficient means of assessing the skeletal maturity of adolescent patients with a knee MRI scan. This bone age assessment technique had interobserver and intraobserver reliability equivalent to or better than the standard method of utilizing a left-hand radiograph.

A Localization and Extraction Method Research for Phalange ROI and Carpal ROI from Left Hand Radiography

2011 ◽  
Vol 340 ◽  
pp. 259-265
Author(s):  
Long Ke Ran ◽  
Ling He ◽  
Zhong Chen
Keyword(s):  
Prediction Method ◽  
Bone Age ◽  
Third Order ◽  
Shape Information ◽  
Age Assessment ◽  
Left Hand ◽  
Key Points ◽  
Order Polynomial ◽  
Bone Age Assessment ◽  
Hand Radiographs

In the research of Automatic bone age assessment,the most efficient location and successful extraction of regions of interest(ROI) from hand radiographs is one of the most difficult and important key technologies. Based on using shape information for phalanges and carpals, a background prediction method is propoesd , which uses a two-dimensional third order polynomial linear regression to fit background. And we also localize the key points of carpal and phalange ROI by usingK-cosine algorithm, finally we extract the carpal and phalange ROI successfully and properly. Through experiments, the proposed method resulted in over 93% correct extraction from more than 60 left hand radiograph data. The proposed method is robust to gray value variation of background and the position and orientation of the hand, so it can be used directly for automatic bone age assessment in the following study.

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.

scholarly journals Creation and Validation of a Shorthand Knee MRI Bone Age Assessment Tool as an Alternative for Skeletal Maturity Assessment (203)

2021 ◽  
Vol 9 (10_suppl5) ◽  
pp. 2325967121S0031
Author(s):  
Cary Politzer ◽  
James Bomar ◽  
Hakan Pehlivan ◽  
Pradyumna Gurusamy ◽  
Eric Edmonds ◽  
...  
Keyword(s):  
Assessment Tool ◽  
Skeletal Maturity ◽  
Bone Age ◽  
Perfect Agreement ◽  
Age Assessment ◽  
Left Hand ◽  
Observer Reliability ◽  
Knee Mri ◽  
Bone Age Assessment ◽  
Hand Radiograph

Objectives: In managing pediatric knee conditions, an accurate bone age assessment is often critical for diagnostic, prognostic, and treatment purposes. Historically, the Greulich and Pyle Atlas (hand atlas) has been the gold standard bone age assessment tool. In 2013, a shorthand bone age assessment tool based on this atlas (hand shorthand) was established as a simpler and more efficient alternative. Recently, a knee MRI bone age atlas (MRI atlas) was created potentially to circumvent the need for a left hand radiograph. Our objective is to create a shorthand version of the magnetic resonance imaging atlas. Methods: A shorthand bone age method (Figure 1) was created utilizing the previously published MRI atlas, which utilizes several criteria that are visualized across a series of images. The MRI shorthand draws on the most characteristic criteria for each age that is best observed on a single MR image. For validation, we performed a retrospective assessment of skeletally immature patients that had a knee MRI and left hand radiograph within four weeks. Four readers who were familiar with the hand atlas, hand shorthand, MRI atlas, and MRI shorthand read each of the images in a blinded fashion. Inter- and intra-observer reliability was evaluated using intraclass correlation coefficient (ICC), variability among observers was evaluated using percent agreement. Results: 26 patients with a mean age of 13.6 years (range 9.0-16.9) met the inclusion criteria. The intra- and inter-observer reliability of all four assessment tools was excellent (ICC ≥ 0.8, p<0.001) (Table 1). When comparing the MRI shorthand to the MRI atlas, there was excellent agreement (ICC = 0.974), whereas the hand shorthand compared to the hand atlas had good agreement (ICC = 0.765). The MRI shorthand also had perfect agreement in 58% of reads among all four readers and 96% of reads had agreement within 1 year, whereas the hand shorthand had perfect agreement in 32% of reads and 77% agreement within 1 year (Table 2). Conclusions: The MRI shorthand is a simple and efficient means of assessing skeletal maturity of adolescent patients with a knee MRI. This bone age assessment technique has inter-observer and intra-observer reliability equivalent or better than the standard means utilizing a left hand radiograph.

scholarly journals A semi-automated method for bone age assessment using cervical vertebral maturation

2012 ◽  
Vol 82 (4) ◽  
pp. 658-662 ◽  
Author(s):  
Roberto S. Baptista ◽  
Camila L. Quaglio ◽  
Laila M. E. H. Mourad ◽  
Anderson D. Hummel ◽  
Cesar Augusto C. Caetano ◽  
...  
Keyword(s):  
Bone Age ◽  
Age Assessment ◽  
Automated Method ◽  
Bone Age Assessment ◽  
Cervical Vertebral Maturation

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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