Automatic bone age assessment for young children from newborn to 7-year-old using carpal bones

Zhang, Aifeng; Gertych, Arkadiusz; Liu, Brent J.

doi:10.1016/j.compmedimag.2007.02.008

Cited by 111 publications

(55 citation statements)

References 13 publications

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“…All subjects (age range, 1 day to 18 years) were recruited from public schools in Los Angeles County, California, starting in the late 1990s (9)(10)(11)(12)(13)(14)(15).…”

Section: Subject Recruitmentmentioning

confidence: 99%

Racial Differences in Growth Patterns of Children Assessed on the Basis of Bone Age

Zhang

Sayre²,

Vachon³

et al. 2009

Radiology

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149

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“…All subjects (age range, 1 day to 18 years) were recruited from public schools in Los Angeles County, California, starting in the late 1990s (9)(10)(11)(12)(13)(14)(15).…”

Section: Subject Recruitmentmentioning

confidence: 99%

Racial Differences in Growth Patterns of Children Assessed on the Basis of Bone Age

Zhang

Sayre²,

Vachon³

et al. 2009

Radiology

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149

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“…The underlying reason for this is the overlapping of carpal bones that begin at the age of 7 years in male children, and at the age of 5 years in female children. After the age of 7 years, using the phalanges to analyze the development stage will yield more reliable results [9]. Fig.…”

Section: Computer-assisted Bone Age Assessment Systemsmentioning

confidence: 99%

Methods Used in Computer-Assisted Bone Age Assessment of Children

Aydoğdu¹,

Başçiftçi²

2014

JACN

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Abstract-For many years, various studies have been conducted to determine methods to identify the age of living persons. Nowadays, the method most commonly used for the identification of age is bone age assessment. Bone age assessment is a method that is very frequently used in forensic cases and by child development specialists. However, the decision process in these assessments depends on the observations of a specialist; hence, the assessment results may vary from one specialist to another. The purpose of automatic assessment with computers is to render the decision process more objective, and to consequently allow more consistent results to be obtained. Studies in this area have drawn considerable attention to automatic assessment methods, especially following the developments in the area of image processing. In the current study, the Greulich-Pyle (GP) and the Tanner-Whitehouse (TW) methods used in computer-assisted bone age assessment were presented, and information was also provided regarding the automation of these methods.Index Terms-Bone age assessment, Greulich-Pyle method, Tanner-Whitehouse method, TW3. I. INTRODUCTIONIn medicine, the identification of bone age is used for the diagnosis, follow-up and treatment of diseases and endocrine disorders. It is also important in judiciary cases for establishing whether individuals are adults and have legal capacity. For example, Turkish laws and regulations divide individuals into different age groups from both a legal and penal perspective. Depending of the nature of the crime, a sentence or penalty may vary according the age group of the perpetrator. It is especially important to determine whether the individual in question is at least 7, 12, 15, or 18 years of age [1], [2].In growing individuals, the bone age is the most commonly used criterion of biological growth and age. Bone age assessments are based on studying the stages of growth associated with skeletal development through the evaluation of hand and wrist radiographies. The level of skeletal maturity can, essentially, be determined based on two characteristics: The level of growth in areas undergoing ossification, and the level of calcium accumulation in those areas. From infancy to adulthood, these two characteristics follow a certain and specific pattern and timeline [3].Studies performed from the beginning of the 20 th century to the present have made use of left hand and wrist radiographies Manuscript received June 15, 2013; revised October 16, 2013 for assessment purposes. There are certain advantages associated with using only the left hand instead of both hands. In particular, using one hand reduces both the cost of procedures and the person's exposure to radiation by half. The fact that the left hand has lower chances of experiencing accidents and injury due to the higher prevalence of right-handedness in most societies, and the fact that researchers performing the initial studies on bone age assessment preferred using the left hand are the leading reasons why the left hand is employed in ...

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“…Bone age determination was conducted by using artificial neural networks, and the performance of different learning algorithms was compared. Another study performed by Zhang et al [12] used the size, eccentricity, and triangularity of the hamate and capitate carpal bones as the features of the fuzzy classification system. They performed the study on 205 young children aged 0-7 years.…”

Section: Literature Reviewmentioning

confidence: 99%

Bone age determination in young children (newborn to 6 years old) using support vector machines

Güraksın¹,

Uğuz²,

Baykan³

2016

Turk J Elec Eng & Comp Sci

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Abstract:Bone age is assessed through a radiological analysis of the left-hand wrist and is then compared to chronological age. A conflict between these two values indicates an abnormality in the development process of the skeleton. This study, conducted on children aged between 0 and 6 years, proposes a computer-based diagnostic system to eliminate the disadvantages of the methods used in bone age determination. For this purpose, primarily an image processing procedure was applied to the X-ray images of the left-hand wrist of children from different ethnic groups aged between 0 and 6 years. A total of 9 features, corresponding to the carpal bones and distal epiphysis of the radius bone with some physiological attributes of the children, were obtained. Then, by using gain ratio, the best 6 features were used for the classification process. Next, the bone age determination process was performed with the obtained features with the help of the support vector machine (SVM), naive Bayes, k-nearest neighborhood, and C4.5 algorithms. Finally, the features used in the determination process and their effects on the accuracies were examined. The results of the designed system showed that SVM method has a better achievement rate than the other methods at a rate of 72.82%. Additionally, in this study, a new feature corresponding to the distance between the centers of gravity of the carpal bones was used for the classification process, and the analysis of the related feature showed that there was a statistically significant difference at P <0.05 between this feature and bones in children aged between 0 and 6 years.

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Automatic bone age assessment for young children from newborn to 7-year-old using carpal bones

Cited by 111 publications

References 13 publications

Racial Differences in Growth Patterns of Children Assessed on the Basis of Bone Age

Racial Differences in Growth Patterns of Children Assessed on the Basis of Bone Age

Methods Used in Computer-Assisted Bone Age Assessment of Children

Bone age determination in young children (newborn to 6 years old) using support vector machines

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