Automated determination of bone age in a modern chinese population

A critical comparative study of the performance of three AI-assisted programs for bone age determination

OBJECTIVES

To date, AI-supported programs for bone age (BA) determination for medical use in Europe have almost only been validated separately, according to Greulich and Pyle (G&P). Therefore, the current study aimed to compare the performance of three programs, namely BoneXpert, PANDA, and BoneView, on a single Central European population.

MATERIALS AND METHODS

For this retrospective study, hand radiographs of 306 children aged 1-18 years, stratified by gender and age, were included. A subgroup consisting of the age group accounting for 90% of examinations in clinical practice was formed. The G&P BA was estimated by three human experts-as ground truth-and three AI-supported programs. The mean absolute deviation, the root mean squared error (RMSE), and dropouts by the AI were calculated.

RESULTS

The correlation between all programs and the ground truth was prominent (R2 ≥ 0.98). In the total group, BoneXpert had a lower RMSE than BoneView and PANDA (0.62 vs. 0.65 and 0.75 years) with a dropout rate of 2.3%, 20.3% and 0%, respectively. In the subgroup, there was less difference in RMSE (0.66 vs. 0.68 and 0.65 years, max. 4% dropouts). The standard deviation between the AI readers was lower than that between the human readers (0.54 vs. 0.62 years, p < 0.01).

CONCLUSION

All three AI programs predict BA after G&P in the main age range with similar high reliability. Differences arise at the boundaries of childhood.

KEY POINTS

Question There is a lack of comparative, independent validation for artificial intelligence-based bone age estimation in children. Findings Three commercially available programs estimate bone age after Greulich and Pyle with similarly high reliability in a central European cohort. Clinical relevance The comparative study will help the reader choose a software for bone age estimation approved for the European market depending on the targeted age group and economic considerations.

Bone Age Assessment Using Various Medical Imaging Techniques Enhanced by Artificial Intelligence

Bone age (BA) reflects skeletal maturity and is crucial in clinical and forensic contexts, particularly for growth assessment, adult height prediction, and managing conditions like short stature and precocious puberty, often using X-ray, MRI, CT, or ultrasound imaging. Traditional BA assessment methods, including the Greulich-Pyle and Tanner-Whitehouse techniques, compare morphological changes to reference atlases. Despite their effectiveness, factors like genetics and environment complicate evaluations, emphasizing the need for new methods that account for comprehensive variations in skeletal maturity. The limitations of classical BA assessment methods increase the demand for automated solutions. The first automated tool, HANDX, was introduced in 1989. Researchers now focus on developing reliable artificial intelligence (AI)-driven tools, utilizing machine learning and deep learning techniques to improve accuracy and efficiency in BA evaluations, addressing traditional methods' shortcomings. Recent reviews on BA assessment methods rarely compare AI-based approaches across imaging technologies. This article explores advancements in BA estimation, focusing on machine learning methods and their clinical implications while providing a historical context and highlighting each approach's benefits and limitations.

Bone age assessment: Large-scale comparison of Greulich-Pyle method and Tanner-Whitehouse 3 method for Taiwanese children

BACKGROUND

The Greulich-Pyle (GP) and Tanner-Whitehouse 3 (TW3) methods are two common methods for assessing bone age (BA). The applicability of these methods for populations other than those in the United States and Europe has been questioned. Thus, this study tested the applicability of these methods for Taiwanese children.

METHODS

In total, 1476 radiographs (654 boys, 822 girls) were analyzed. A subset of 200 radiographs was evaluated to determine intrarater and interrater reliability and the time required to yield a BA assessment. BA was determined by two reviewers using the GP method and two of the TW3 methods (the Radial-Ulnar-Short bones [RUS] method and the carpals method [Carpal]). The GP and TW3 methods were directly compared using statistical techniques. A subgroup analysis by age was performed to compare BA and chronological age using a paired t test for each age group.

RESULTS

The average times required to yield an assessment using the GP and TW3-RUS methods were 0.79 ± 0.14 and 3.01 ± 0.84 min (p < 0.001), respectively. Both the intrarater and interrater correlation coefficients were higher for the GP method (0.993, 0.992) than the TW3-RUS (0.985, 0.984) and TW3-Carpal (0.981, 0.973) methods. The correlation coefficient for the GP and TW3-RUS methods was highest in the pubertal stage (0.898 for boys and 0.909 for girls). The mean absolute deviations for the GP and TW3-RUS methods in the pubertal stage were 0.468 years (boys) and 0.496 years (girls). Both the GP and TW3-Carpal methods underestimated BA for boys in the prepubertal stage. Both the GP and TW3-RUS methods overestimated BA for girls in the pubertal and postpubertal stages.

CONCLUSION

The GP and TW3-RUS methods exhibit strong agreement in the pubertal and postpubertal stages for both sexes. With appropriate adjustments based on Taiwanese data, both methods are applicable to our children.

A comparison of bone age assessments using automated and manual methods in children of Indian ethnicity

BACKGROUND

Bone age is useful for pediatric endocrinologists in evaluating various disorders related to growth and puberty. Traditional methods of bone age assessment, namely Greulich and Pyle (GP) and Tanner-Whitehouse (TW), have intra- and interobserver variations. Use of computer-automated methods like BoneXpert might overcome these subjective variations.

OBJECTIVE

The aim of our study was to assess the validity of BoneXpert in comparison to manual GP and TW methods for assessing bone age in children of Asian Indian ethnicity.

MATERIALS AND METHODS

We extracted from a previous study the deidentified left hand radiographs of 920 healthy children aged 2-19 years. We compared bone age as determined by four well-trained manual raters using GP and TW methods with the BoneXpert ratings. We computed accuracy using root mean square error (RMSE) to assess how close the bone age estimated by BoneXpert was to the reference rating.

RESULTS

The standard deviations (SDs) of rating among the four manual raters were 0.52 years, 0.52 years and 0.47 years for GP, TW2 and TW3 methods, respectively. The RMSEs between the automated bone age estimates and the true ratings were 0.39 years, 0.41 years and 0.36 years, respectively, for the same methods. The RMSE values were significantly lower in girls than in boys (0.53, 0.5 and 0.47 vs. 0.39, 0.47 and 0.4) by all the methods; however, no such difference was noted in classification by body mass index. The best agreement between BoneXpert and manual rating was obtained by using 50% weight on carpals (GP50). The carpal bone age was retarded in Indian children, more so in boys.

CONCLUSION

BoneXpert was accurate and performed well in estimating bone age by both GP and TW methods in healthy Asian Indian children; the error was larger in boys. The GP50 establishes "backward compatibility" with manual rating.

Comparison of Bone Agesin Early Puberty: Computerized Greulich-Pyle Based Bone Age vs. Sauvegrain Method

Purpose

To compare the computerized Greulich-Pyle based bone age with elbow bone age.

Materials and Methods

A total of 2126 patients (1525 girls; 601 boys) whose elbow bone age was within the evaluable range by the Sauvegrain method, and who simultaneously underwent hand radiography, were enrolled in the study. The 1st-bone age and VUNO score of the hand were evaluated using VUNOMed-BoneAge software. The correlation between the hand and elbow bone age was analyzed according to the child's gender and the probability of 1st-bone age.

Results

The correlation between VUNO score and elbow bone age (r = 0.898) was higher than the correlation between 1st-bone age and elbow bone age (r = 0.879). Moreover, the VUNO score showed a better correlation with the elbow bone age in patients with a 1st-bone age probability of less than 70%, or in girls. Elbow bone age was more advanced compared to hand bone age, and this difference increased until the middle of puberty and gradually decreased in the latter half.

Conclusion

The computerized Greulich-Pyle based hand bone age showed a significant correlation with the elbow bone age at puberty. However, since the elbow bone age tends to advance faster than the hand bone age, caution is required while judging the bone age during puberty.

Study of Multidimensional and High-Precision Height Model of Youth Based on Multilayer Perceptron

Predicting the adult height of children accurately has great social value for the selection of outstanding athlete as well as early detection of children's growth disorders. Currently, the mainstream method used to predict adult height in China has three problems: its standards are not uniform; it is stale for current Chinese children; its accuracy is not satisfactory. This article uses the data collected by the Chinese Children and Adolescents' Physical Fitness and Growth Health Project in Zhejiang primary and secondary schools. We put forward a new multidimensional and high-precision youth growth curve prediction model, which is based on multilayer perceptron. First, this model uses multidimensional growth data of children as predictors and then utilizes multilayer perceptron to predict the children's adult height. Second, we find the Table of Height Standard Deviation of Chinese Children and fit the data of zero standard deviation to obtain the curve. This curve is regarded as Chinese children's mean growth curve. Third, we use the least-squares method and the mean curve to calculate the individual growth curve. Finally, the individual curve can be used to predict children's state height. Experimental results show that this adult height prediction model's accuracy (between 2 cm) of boys and girls reached 90.20% and 88.89% and the state height prediction accuracy reached 77.46% and 74.93%. Compared with Bayley–Pinneau, the adult height prediction is improved 19.61% for boys and 13.33% for girls. Compared with BoneXpert, the adult height prediction is improved 25.49% for boys and 6.67% for girls. Compared with the method based on the bone age growth map, the adult height prediction is improved 15.69% for boys and 24.45% for girls.

Design of Growth Trend Map of Children and Adolescents Based on Bone Age

Accurate height prediction has important reference significance for the development of children and adolescents and the selection of athletes. The current mainstream height prediction methods include the B-P (Bayley–Pinneau) method and the TW2 (Tanner–Whitehouse) method. A large number of documents show that the B-P method and the TW2 method have relatively large deviations in the lifelong height prediction results of Chinese children and adolescents. Based on the data collected by the Chinese Adolescent Students' Physical Fitness and Growth and Development Health Project in Zhejiang's primary and secondary schools, this paper proposes a graph of height growth trends based on bone age. The height map of age has more reference value. Aiming at the feasibility of the height data in the statistical results, the interpolation prediction method is used to verify the data, and the height growth trend graph is drawn through the method of fitting. Validation results with actual data show that the average error of the lifetime height prediction of the height growth trend map proposed in this paper is 2.1 cm, which is 1.4 cm lower than the 3.5 cm error predicted by the B-P method and 0.4 cm lower than the 2.5 cm error predicted by the TW2 method.

Validation of automated bone age analysis from hand radiographs in a North American pediatric population

BACKGROUND

Radiographic bone age assessment by automated software is precise and instantaneous.

OBJECTIVE

The aim of this study was to evaluate the accuracy of an automated tool for bone age assessment.

MATERIALS AND METHODS

We compared a total of 586 bone age radiographs from 451 patients, which had been assessed by three radiologists from 2013 to 2018, with bone age analysis by BoneXpert, using the Greulich and Pyle method. We made bone age comparisons in different patient groups based on gender, diagnosis and race, and in a subset with repeated bone age studies. We calculated Spearman correlation (r) and accuracy (root mean square error, or R²).

RESULTS

Bone age analyses by automated and manual assessments showed a strong correlation (r=0.98; R²=0.96; P<0.0001), with the mean bone age difference of 0.12±0.76 years. Bone age comparisons by the two methods remained strongly correlated (P<0.0001) when stratified by gender, common endocrine conditions including growth disorders and early/precocious puberty, and race. In the longitudinal analysis, we also found a strong correlation between the automated software and manual bone age over time (r=0.7852; R²=0.63; P<0.01).

CONCLUSION

Automated bone age assessment was found to be reliable and accurate in a large cohort of pediatric patients in a clinical practice setting in North America.

Artificial Intelligence-Assisted Bone Age Assessment to Improve the Accuracy and Consistency of Physicians With Different Levels of Experience

BACKGROUND

The accuracy and consistency of bone age assessments (BAA) using standard methods can vary with physicians' level of experience.

METHODS

To assess the impact of information from an artificial intelligence (AI) deep learning convolutional neural network (CNN) model on BAA, specialists with different levels of experience (junior, mid-level, and senior) assessed radiographs from 316 children aged 4-18 years that had been randomly divided into two equal sets-group A and group B. Bone age (BA) was assessed independently by each specialist without additional information (group A) and with information from the model (group B). With the mean assessment of four experts as the reference standard, mean absolute error (MAE), and intraclass correlation coefficient (ICC) were calculated to evaluate accuracy and consistency. Individual assessments of 13 bones (radius, ulna, and short bones) were also compared between group A and group B with the rank-sum test.

RESULTS

The accuracies of senior, mid-level, and junior physicians were significantly better (all P < 0.001) with AI assistance (MAEs 0.325, 0.344, and 0.370, respectively) than without AI assistance (MAEs 0.403, 0.469, and 0.755, respectively). Moreover, for senior, mid-level, and junior physicians, consistency was significantly higher (all P < 0.001) with AI assistance (ICCs 0.996, 0.996, and 0.992, respectively) than without AI assistance (ICCs 0.987, 0.989, and 0.941, respectively). For all levels of experience, accuracy with AI assistance was significantly better than accuracy without AI assistance for assessments of the first and fifth proximal phalanges.

CONCLUSIONS

Information from an AI model improves both the accuracy and the consistency of bone age assessments for physicians of all levels of experience. The first and fifth proximal phalanges are difficult to assess, and they should be paid more attention.

Traditional and New Methods of Bone Age Assessment-An Overview

Bone age is one of biological indicators of maturity used in clinical practice and it is a very important parameter of a child’s assessment, especially in paediatric endocrinology. The most widely used method of bone age assessment is by performing a hand and wrist radiograph and its analysis with Greulich-Pyle or Tanner-Whitehouse atlases, although it has been about 60 years since they were published. Due to the progress in the area of Computer-Aided Diagnosis and application of artificial intelligence in medicine, lately, numerous programs for automatic bone age assessment have been created. Most of them have been verified in clinical studies in comparison to traditional methods, showing good precision while eliminating inter- and intra-rater variability and significantly reducing the time of assessment. Additionally, there are available methods for assessment of bone age which avoid X-ray exposure, using modalities such as ultrasound or magnetic resonance imaging.

Age estimation in the living: A scoping review of population data for skeletal and dental methods

Age estimation of living individuals has become a crucial part of the forensic practice, especially due to the global increase in cross-border migration. The low rate of birth registration in many countries, hence of identification documents of migrants, especially in Africa and Asia, highlights the importance of reliable methods for age estimation of living individuals. Despite the fact that a number of skeletal and dental methods for age estimation have been developed, their main limitation is that they are based on specific reference samples and there is still no consensus among researchers on whether these methods can be applied to all populations. Though this issue remains still unsolved, population information at a glance could be useful for forensic practitioners dealing with such issues. This study aims at presenting a scoping review and mapping of the current situation concerning population data for skeletal (hand-wrist and clavicle) and dental methods (teeth eruption and third molar formation) for age estimation in the living. Two hundred studies on the rate of skeletal maturation and four hundred thirty-nine on the rate of dental maturation were found, covering the period from 1952 and 2020 for a total of ninety-eight countries. For most of the western and central African countries there are currently no data on the rate of skeletal and dental maturation. The same applies to the countries of the Middle East, as well as the eastern European countries, especially as regard the skeletal development.

Cortical Bone Mass is Low in Boys with Klinefelter Syndrome and Improves with Oxandrolone

Context

Klinefelter syndrome (KS) is the most common sex aneuploidy in men. Affected males have hypogonadism, and, as a result, face an increased risk for osteoporosis and fractures. Androgen therapy is standard in adolescents and adults with KS but has not been used earlier in childhood.

Objective

To determine the effects of androgen treatment on bone mass in children with KS.

Methods

Randomized, double-blind, placebo-controlled clinical trial of oxandrolone (OX; 0.06 mg/kg daily; n = 38) versus placebo (PL; n = 40) for 2 years in boys with KS (ages 4-12 years). Changes in bone mass were examined by digital x-ray radiogrammetry, which determines the Bone Health Index (BHI) and standard deviation score (SDS).

Results

BHI SDS was similar between groups at baseline (–0.46 ± 1.1 vs –0.34 ± 1.0 OX vs PL, P > .05) and higher in the OX group at 2 years (–0.1 ± 1.3 vs –0.53 ± 0.9, OX vs PL, P < .01). At baseline, BHI SDS values of all subjects were not normally distributed with 25.7% of subjects plotted below –1 SDS (P < .001), suggesting a deficit in bone mass. In total, 13.5% of subjects had sustained a fracture and their BHI SDS was lower than those with no fractures (–1.6 ± 1.3 vs –0.3 ± 1.0, P = .004).

Conclusion

Bone mass using BHI SDS is reduced in some children with KS and improves with OX. Since these individuals are at risk for osteoporosis, age-appropriate androgen replacement and future studies on bone health in children with KS should be further explored.

Skeletal Maturation in the Current Pediatric Mexican Population

OBJECTIVE

The most commonly used methods for bone age (BA) reading were described in the Caucasian population decades ago. However, there are secular trends in skeletal maturation and different BA patterns between ethnic groups. Automated BA reading makes updating references easier and more precise than human reading. The objective of the present study was to present automated BA reference curves according to chronological age and gender in the Mexican population and compare the maturation tempo with that of other populations.

METHODS

The study included 923 healthy participants aged 5 to 18 years between 2017 and 2018. A hand radio-graph was analyzed using BoneXpert software to obtain the automated BA reading according to Greulich and Pyle (G&P) and Tanner-Whitehouse 2 (TW2) references. We constructed reference curves using the average difference between the BA and chronological age according to sex and age.

RESULTS

The G&P and TW2 automated reference curves showed that Mexican boys exhibit delays in BA during middle childhood by 0.5 to 0.7 (95% confidence interval &lsqb;CI], -0.9 to -0.2) years; however, they demonstrate an advanced BA of up to 1.1 (95% CI, 0.8 to 1.4) years at the end of puberty. Mexican girls exhibited a delay in BA by 0.3 to 0.6 (95% CI, -0.9 to -0.1) years before puberty and an advanced BA of up to 0.9 (95% CI, 0.7 to 1.2) years at the end of puberty.

CONCLUSION

Mexican children aged <10 years exhibited a delay in skeletal maturity, followed by an advanced BA by approximately 1 year at the end of puberty. This may affect the estimation of growth potential in this population.

Automatic assessment of bone age in Taiwanese children: A comparison of the Greulich and Pyle method and the Tanner and Whitehouse 3 method

Proper bone age assessment is crucial for the clinical diagnosis and evaluation of treatment responses. We investigated the applicability of Greulich and Pyle (GP), and Tanner and Whitehouse 3 (TW3) methods for children in modern Taiwan, using computer-aided diagnosis. Hand and wrist radiographs were obtained from 611 children (3-17 years) who came to our emergency department due to trauma. Ages 0 to 2 years old were excluded because of a limited number of cases. Skeletal maturation was assessed using the BoneXpert (version 2.5.4.1 automated software), which determines GP and TW3 bone age. The two scoring systems were evaluated for comparing the chronological ages in each subgroup. In boys, mean GP bone age vs mean chronological ages were delayed for ages 3 to 11 and advanced for age 12 to 17. In girls, mean GP bone age vs mean chronological ages was delayed for ages 4 to 8 and 17, and advanced for ages 3 and 9 to 17. In boys, the mean TW3 bone ages vs mean chronological ages were delayed for ages 5 to 10 except age 8, and advanced for ages 3 to 4, 8, and 11 to 15. In girls, the mean TW3 bone ages vs mean chronological ages were delayed for ages 4 to 12, and advanced for ages 3 and 13 to 14. By using the BoneXpert automatic software, we established bone age reference standards for children in Taiwan. Clinical application of GP and TW3 scoring methods can be adjusted according to our results to better assess bone age.

Artificial intelligence in bone age assessment: accuracy and efficiency of a novel fully automated algorithm compared to the Greulich-Pyle method

Background

Bone age (BA) assessment performed by artificial intelligence (AI) is of growing interest due to improved accuracy, precision and time efficiency in daily routine. The aim of this study was to investigate the accuracy and efficiency of a novel AI software version for automated BA assessment in comparison to the Greulich-Pyle method.

Methods

Radiographs of 514 patients were analysed in this retrospective study. Total BA was assessed independently by three blinded radiologists applying the GP method and by the AI software. Overall and gender-specific BA assessment results, as well as reading times of both approaches, were compared, while the reference BA was defined by two blinded experienced paediatric radiologists in consensus by application of the Greulich-Pyle method.

Results

Mean absolute deviation (MAD) and root mean square deviation (RSMD) were significantly lower between AI-derived BA and reference BA (MAD 0.34 years, RSMD 0.38 years) than between reader-calculated BA and reference BA (MAD 0.79 years, RSMD 0.89 years; p < 0.001). The correlation between AI-derived BA and reference BA (r = 0.99) was significantly higher than between reader-calculated BA and reference BA (r = 0.90; p < 0.001). No statistical difference was found in reader agreement and correlation analyses regarding gender (p = 0.241). Mean reading times were reduced by 87% using the AI system.

Conclusions

A novel AI software enabled highly accurate automated BA assessment. It may improve efficiency in clinical routine by reducing reading times without compromising the accuracy compared with the Greulich-Pyle method.

Bone age determination in eutrophic, overweight and obese Brazilian children and adolescents: a comparison between computerized BoneXpert and Greulich-Pyle methods

BACKGROUND

Bone age determination is usually employed to evaluate growth disorders and their treatment. The Greulich-Pyle method is the simplest and most frequently used type of evaluation, but it presents huge interobserver variability. The BoneXpert is a computer-automated method developed to avoid significant bone age variability among distinct observers.

OBJECTIVE

To compare the BoneXpert and Greulich-Pyle methods of bone age determination in eutrophic children and adolescents, as well as in overweight and obese pediatric patients.

MATERIALS AND METHODS

The sample comprised 515 participants, 253 boys (159 eutrophic, 53 overweight and 41 obese) and 262 girls (146 eutrophic, 76 overweight and 40 obese). Left hand and wrist radiographs were acquired for bone age determination using both methods.

RESULTS

There was a positive correlation between chronological age and Greulich-Pyle, chronological age and BoneXpert, and Greulich-Pyle and BoneXpert. There was a significant increase (P<0.05) in bone age in both the Greulich-Pyle and BoneXpert methods in obese boys when compared to eutrophic or overweight boys of the same age. In girls, there was an increase in bone age in both obese and overweight individuals when compared to eutrophic girls (P<0.05). The Greulich-Pyle bone age was advanced in comparison to that of BoneXpert in all groups, except in obese boys, in which bone age was similarly advanced in both methods.

CONCLUSION

The BoneXpert computer-automated bone age determination method showed a significant positive correlation with chronological age and Greulich-Pyle. Furthermore, the impact of being overweight or obese on bone age could be identified by both methods.

Evaluation of a Computer-Aided Diagnosis System for Automated Bone Age Assessment in Comparison to the Greulich-Pyle Atlas Method: A Multireader Study

OBJECTIVE

The aim of this study was to investigate a novel version of a computer-aided diagnosis (CAD) system developed for automated bone age (BA) assessment in comparison to the Greulich and Pyle method, regarding its accuracy and the influence of carpal bones on BA assessment.

METHODS

Total BA, BA of the left distal radius, and BA of carpal bones in 305 patients were determined independently by 3 blinded radiologists and assessed by the CAD system. Pearson product-moment correlation, Bland-Altman plot, root-mean-square deviation, and further agreement analyses were computed.

RESULTS

Mean total BA and BA of the distal radius showed high correlation between both approaches (r = 0.985 and r = 0.963). There was significantly higher correlation between values of total BA and BA of the distal radius (r = 0.969) compared with values of total BA and BA of carpal bones (r = 0.923). The assessment of carpal bones showed significantly lower interreader agreement compared with measurements of the distal radius (κ = 0.79 vs κ = 0.98).

CONCLUSION

A novel version of a CAD system enables highly accurate automated BA assessment. The assessment of carpal bones revealed lower precision and interreader agreement. Therefore, methods determining BA without analyzing carpal bones may be more precise and accurate.

Automatic Determination of the Greulich-Pyle Bone Age as an Alternative Approach for Chinese Children with Discordant Bone Age

BACKGROUND

Automated bone age (BA) rating using BoneXpert is being adopted worldwide. This study investigated whether manual matching of hand radiographs could be replaced by BoneXpert for BA ratings of Chinese children with delayed or advanced BA.

METHODS

482 left-hand radiographs from 482 children (aged 2-16 years) with discordant BA were evaluated by BoneXpert and manually by 4 radiology residents using the Greulich and Pyle atlas. Radiographs whose BoneXpert BA deviated by >1 year from manual assessment were rerated by 2 attending radiologists in a blinded manner.

RESULTS

Among all 482 radiographs, 46 (9.5%) radiographs were rerated and no radiographs were rejected. Differences between BoneXpert and manual rating of 28 (5.8%) cases were >1 year. The manual BAs of the 28 radiographs were all >10 years and greater than the BoneXpert BAs. The root mean square deviation between the residents and BoneXpert was 0.56 for these children (95% CI 0.53-0.61).

CONCLUSION

BoneXpert agreed with manual BA rating in 94.2% of the images. Therefore, BoneXpert could be used as an alternative for the radiology residents to make an initial BA estimation. Modification of BoneXpert should provide greater accuracy for the estimation of BA in children aged >10 years with discordant BA.

Automated determination of bone age from hand X-rays at the end of puberty and its applicability for age estimation

The BoneXpert method for automated determination of bone age from hand X-rays was introduced in 2009, covering the Greulich-Pyle bone age ranges up to 17 years for boys and 15 years for girls. This paper presents an extension of the method up to bone age 19 years for boys and 18 years for girls. The extension was developed based on images from the First Zurich Longitudinal Study of 231 healthy children born in 1954-1956 and followed with annual X-rays of both hands until adulthood. The method was validated on two cross-sectional studies of healthy children from Rotterdam and Los Angeles. We found root mean square deviations from manual rating of 0.69 and 0.45 years in these two studies for boys in the bone age range 17-19 years. For girls, the deviations were 0.75 and 0.59 years, respectively, in the bone age range 15-18 years. It is shown how the automated bone age method can be applied to infer the age probability distribution for healthy Caucasian European males. Considering a population with age 15.0-21.0 years, the method can be used to decide whether the subject is above 18 years with a false positive rate (children classified as adults) of 10 % (95% confidence interval = 7-13%) and a false negative rate of 30 % (adults classified as children). To apply this method in other ethnicities will require a study of the average of "bone age - age" at the end of puberty, i.e. how much this population is shifted relative to the Greulich-Pyle standard.

Maturation Disparity between Hand-Wrist Bones in a Chinese Sample of Normal Children: An Analysis Based on Automatic BoneXpert and Manual Greulich and Pyle Atlas Assessment

OBJECTIVE

To assess the maturation disparity of hand-wrist bones using the BoneXpert system and Greulich and Pyle (GP) atlas in a sample of normal children from China.

MATERIALS AND METHODS

Our study included 229 boys and 168 girls aged 2-14 years. The bones in the hand and wrist were divided into five groups: distal radius and ulna, metacarpals, proximal phalanges, middle phalanges and distal phalanges. Bone age (BA) was assessed separately using the automatic BoneXpert and GP atlas by two raters. Differences in the BA between the most advanced and retarded individual bones and bone groups were analyzed.

RESULTS

In 75.8% of children assessed with the BoneXpert and 59.4% of children assessed with the GP atlas, the BA difference between the most advanced and most retarded individual bones exceeded 2.0 years. The BA mean differences between the most advanced and most retarded individual bones were 2.58 and 2.25 years for the BoneXpert and GP atlas methods, respectively. Furthermore, for both methods, the middle phalanges were the most advanced group. The most retarded group was metacarpals for BoneXpert, while metacarpals and the distal radius and ulna were the most retarded groups according to the GP atlas. Overall, the BAs of the proximal and distal phalanges were closer to the chronological ages than those of the other bone groups.

CONCLUSION

Obvious and regular maturation disparities are common in normal children. Overall, the BAs of the proximal and distal phalanges are more useful for BA estimation than those of the other bone groups.

A Piece of the Puzzle: The Bone Health Index of the BoneXpert Software Reflects Cortical Bone Mineral Density in Pediatric and Adolescent Patients

INTRODUCTION

Suspected osteopathology in chronically ill children often necessitates the assessment of bone mineral density. The most frequently used methods are dual-energy X-ray-absorption (DXA) and peripheral quantitative computed tomography (pQCT). The BoneXpert software provides an automated radiogrammatic method to assess skeletal age from digitalized X-rays of the left hand. Furthermore, the program calculates the Bone Health Index (BHI), a measure of cortical thickness and mineralization, which is obtained from indices of three metacarpal bones. In our study, we analyzed the manner in which BHI information provided by BoneXpert compares with DXA or pQCT measurements in youths.

STUDY DESIGN

The BHI was retrospectively obtained using digitalized X-rays of the left hand and compared with the results of 203 corresponding DXA readings (Lunar Prodigy, GE Healthcare) of the lumbar vertebrae and femur as well as 117 pQCT readings (XCT 900, Stratec) of the distal radius.

RESULTS

The BHI values showed a strong positive correlation with the DXA readings at each and all lumbar vertebrae (L1 -L4: r = 0.73; P < 0.0001). The age-adjusted Z-score of L1 -L4 and the height-adjusted score showed a positive correlation with the BHI-SDS (standard deviation score, r = 0.23; P < 0.002 and r = 0.27; P < 0.001, respectively). Total bone mineral density, as assessed via pQCT, also positively correlated with the BHI (r = 0.39; P < 0.0001), but the trabecular values displayed only a weak correlation.

CONCLUSIONS

The BHI obtained using BoneXpert can be a useful parameter in the assessment of bone health in children in most cases. This technique provides observer-independent information on cortical thickness and mineralization based on X-ray imaging of the hands.

Bone age: assessment methods and clinical applications

The main bone age assessment methods are the Greulich-Pyle and Tanner-Whitehouse 2 methods, both of which involve left hand and wrist radiographs. Several other bone age assessment methods have been developed, including ultrasonographic, computerized, and magnetic resonance (MR) imaging methods. The ultrasonographic method appears unreliable in children with delayed and advanced bone age. MR imaging is noninvasive; however, bone age assessment using MR imaging is relatively new, and further examinations are needed. An automated method for determining bone age, named BoneXpert, has been validated for Caucasian children with growth disorders and children of various ethnic groups. Sex hormones are necessary for bone growth and maturation in children with a bone age corresponding to normal pubertal age, and estrogen is essential for growth plate closure. Bone age is an effective indicator for diagnosing and treating various diseases. A new method for adult height prediction based on bone age has been developed using BoneXpert, in addition to the commonly used Bayley-Pinneau and Tanner-Whitehouse mark II methods. Furthermore, bone age may become a predictor for the timing of peak height velocity and menarche.