Automatic determination of Greulich and Pyle bone age in healthy Dutch children

Acceleration of skeletal maturation in Central Europe over the last two decades: insights from two cohorts of healthy children

BACKGROUND

Deviations between the determination of bone age (BA) according to Greulich and Pyle (G&P) and chronological age (CA) are common in Caucasians. Assessing these discrepancies in a population over time requires analysis of large samples and low intra-observer variability in BA estimation, both can be achieved with artificial intelligence-based software. The latest software-based reference curve contrasting the BA determined by G&P to the CA of Central European children dates back over two decades.

OBJECTIVE

To examine whether the reference curve from a historical cohort from the Netherlands (Rotterdam cohort) between BA determined by G&P and CA still applies to a current Central European cohort and derive a current reference curve.

MATERIALS AND METHODS

This retrospective single-center study included 1,653 children and adolescents (aged 3-17 years) who had received a radiograph of the hand following trauma. The G&P BA estimated using artificial intelligence-based software was contrasted with the CA, and the deviations were compared with the Rotterdam cohort.

RESULTS

Among the participants, the mean absolute error between BA and CA was 0.92 years for girls and 0.97 years for boys. For the ages of 8 years (boys) and 11 years (girls) and upward, the mean deviation was significantly greater in the current cohort than in the Rotterdam cohort. The reference curves of both cohorts also differed significantly from each other (P < 0.001 for both boys and girls).

CONCLUSION

The BA of the current Central European population and that of the curve from the Rotterdam cohort from over two decades ago differ. Whether this effect can be attributed to accelerated bone maturation needs further evaluation.

Automated bone age assessment in a German pediatric cohort: agreement between an artificial intelligence software and the manual Greulich and Pyle method

OBJECTIVES

This study aimed to evaluate the performance of artificial intelligence (AI) software in bone age (BA) assessment, according to the Greulich and Pyle (G&P) method in a German pediatric cohort.

MATERIALS AND METHODS

Hand radiographs of 306 pediatric patients aged 1-18 years (153 boys, 153 girls, 18 patients per year of life)-including a subgroup of patients in the age group for which the software is declared (243 patients)-were analyzed retrospectively. Two pediatric radiologists and one endocrinologist made independent blinded BA reads. Subsequently, AI software estimated BA from the same images. Both agreements, accuracy, and interchangeability between AI and expert readers were assessed.

RESULTS

The mean difference between the average of three expert readers and AI software was 0.39 months with a mean absolute difference (MAD) of 6.8 months (1.73 months for the mean difference and 6.0 months for MAD in the intended use subgroup). Performance in boys was slightly worse than in girls (MAD 6.3 months vs. 5.6 months). Regression analyses showed constant bias (slope of 1.01 with a 95% CI 0.99-1.02). The estimated equivalence index for interchangeability was - 14.3 (95% CI -27.6 to - 1.1).

CONCLUSION

In terms of BA assessment, the new AI software was interchangeable with expert readers using the G&P method.

CLINICAL RELEVANCE STATEMENT

The use of AI software enables every physician to provide expert reader quality in bone age assessment.

KEY POINTS

• A novel artificial intelligence-based software for bone age estimation has not yet been clinically validated. • Artificial intelligence showed a good agreement and high accuracy with expert radiologists performing bone age assessment. • Artificial intelligence showed to be interchangeable with expert readers.

High performance for bone age estimation with an artificial intelligence solution

PURPOSE

The purpose of this study was to compare the performance of an artificial intelligence (AI) solution to that of a senior general radiologist for bone age assessment.

MATERIAL AND METHODS

Anteroposterior hand radiographs of eight boys and eight girls from each age interval between five and 17 year-old from four different radiology departments were retrospectively collected. Two board-certified pediatric radiologists with knowledge of the sex and chronological age of the patients independently estimated the Greulich and Pyle bone age to determine the standard of reference. A senior general radiologist not specialized in pediatric radiology (further referred to as "the reader") then determined the bone age with knowledge of the sex and chronological age. The results of the reader were then compared to those of the AI solution using mean absolute error (MAE) in age estimation.

RESULTS

The study dataset included a total of 206 patients (102 boys of mean chronological age of 10.9 ± 3.7 [SD] years, 104 girls of mean chronological age of 11 ± 3.7 [SD] years). For both sexes, the AI algorithm showed a significantly lower MAE than the reader (P < 0.007). In boys, the MAE was 0.488 years (95% confidence interval [CI]: 0.28-0.44; r² = 0.978) for the AI algorithm and 0.771 years (95% CI: 0.64-0.90; r² = 0.94) for the reader. In girls, the MAE was 0.494 years (95% CI: 0.41-0.56; r² = 0.973) for the AI algorithm and 0.673 years (95% CI: 0.54-0.81; r² = 0.934) for the reader.

CONCLUSION

The AI solution better estimates the Greulich and Pyle bone age than a general radiologist does.

A comprehensive validation study of the latest version of BoneXpert on a large cohort of Caucasian children and adolescents

INTRODUCTION

Automated bone age assessment has recently become increasingly popular. The aim of this study was to assess the agreement between automated and manual evaluation of bone age using the method according to Tanner-Whitehouse (TW3) and Greulich-Pyle (GP).

METHODS

We evaluated 1285 bone age scans from 1202 children (657 scans from 612 boys) by using both manual and automated (TW3 as well as GP) bone age assessment. BoneXpert software versions 2.4.5.1. (BX2) and 3.2.1. (BX3) (Visiana, Holte, Denmark) were compared with manual evaluation using root mean squared error (RMSE) analysis.

RESULTS

RMSE for BX2 was 0.57 and 0.55 years in boys and 0.72 and 0.59 years in girls, respectively for TW3 and GP. For BX3, RMSE was 0.51 and 0.68 years in boys and 0.49 and 0.52 years in girls, respectively for TW3 and GP. Sex- and age-specific analysis for BX2 identified the largest differences between manual and automated TW3 evaluation in girls between 6-7, 12-13, 13-14 and 14-15 years, with RMSE 0.88, 0.81, 0.92 and 0.84 years, respectively. The BX3 version showed better agreement with manual TW3 evaluation (RMSE 0.64, 0.45, 0.46 and 0.57).

CONCLUSION

The latest version of the BoneXpert software provides improved and clinically sufficient agreement with manual bone age evaluation in children of both sexes compared to the previous version and may be used for routine bone age evaluation in non-selected cases in pediatric endocrinology care.

Automated detection of acute appendicular skeletal fractures in pediatric patients using deep learning

OBJECTIVE

We aimed to perform an external validation of an existing commercial AI software program (BoneView™) for the detection of acute appendicular fractures in pediatric patients.

MATERIALS AND METHODS

In our retrospective study, anonymized radiographic exams of extremities, with or without fractures, from pediatric patients (aged 2-21) were included. Three hundred exams (150 with fractures and 150 without fractures) were included, comprising 60 exams per body part (hand/wrist, elbow/upper arm, shoulder/clavicle, foot/ankle, leg/knee). The Ground Truth was defined by experienced radiologists. A deep learning algorithm interpreted the radiographs for fracture detection, and its diagnostic performance was compared against the Ground Truth, and receiver operating characteristic analysis was done. Statistical analyses included sensitivity per patient (the proportion of patients for whom all fractures were identified) and sensitivity per fracture (the proportion of fractures identified by the AI among all fractures), specificity per patient, and false-positive rate per patient.

RESULTS

There were 167 boys and 133 girls with a mean age of 10.8 years. For all fractures, sensitivity per patient (average [95% confidence interval]) was 91.3% [85.6, 95.3], specificity per patient was 90.0% [84.0,94.3], sensitivity per fracture was 92.5% [87.0, 96.2], and false-positive rate per patient in patients who had no fracture was 0.11. The patient-wise area under the curve was 0.93 for all fractures. AI diagnostic performance was consistently high across all anatomical locations and different types of fractures except for avulsion fractures (sensitivity per fracture 72.7% [39.0, 94.0]).

CONCLUSION

The BoneView™ deep learning algorithm provides high overall diagnostic performance for appendicular fracture detection in pediatric patients.

Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology

Artificial 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.

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.

Primary Amenorrhea with Apparently Absent Uterus: A Report of Three Cases

Background: The apparent absence of a uterus upon imaging women with primary amenorrhea appears to lead to a high risk of misdiagnosis, which will lead to significant mental distress in patients. Case: Three young females with primary amenorrhea were referred with a diagnosis of Mayer–Rokitansky–Kuster–Hauser syndrome based on radiological findings of an apparently absent uterus. In two patients, the absence of the uterus could be confirmed, but with various diagnoses. The other patient had a normal but unstimulated uterus due to her hypoestrogenic state. Summary and Conclusion: The presented cases illustrate the broad differential diagnoses and the specific pitfalls of primary amenorrhea with an apparently absent uterus upon imaging. A well-established diagnosis was only possible through a thorough correlation of imaging findings with clinical history, biochemical findings and physical examination.

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.

Accuracy and self-validation of automated bone age determination

The BoneXpert method for automated determination of bone age from hand X-rays was introduced in 2009 and is currently running in over 200 hospitals. The aim of this work is to present version 3 of the method and validate its accuracy and self-validation mechanism that automatically rejects an image if it is at risk of being analysed incorrectly. The training set included 14,036 images from the 2017 Radiological Society of North America (RSNA) Bone Age Challenge, 1642 images of normal Dutch and Californian children, and 8250 images from Tübingen from patients with Short Stature, Congenital Adrenal Hyperplasia and Precocious Puberty. The study resulted in a cross-validated root mean square (RMS) error in the Tübingen images of 0.62 y, compared to 0.72 y in the previous version. The RMS error on the RSNA test set of 200 images was 0.45 y relative to the average of six manual ratings. The self-validation mechanism rejected 0.4% of the RSNA images. 121 outliers among the self-validated images of the Tübingen study were rerated, resulting in 6 cases where BoneXpert deviated more than 1.5 years from the average of the three re-ratings, compared to 72 such cases for the original manual ratings. The accuracy of BoneXpert is clearly better than the accuracy of a single manual rating. The self-validation mechanism rejected very few images, typically with abnormal anatomy, and among the accepted images, there were 12 times fewer severe bone age errors than in manual ratings, suggesting that BoneXpert could be safer than manual rating.

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.

[Artificial intelligence in image evaluation and diagnosis]

Die Radiologie ist von stetem Wandel geprägt und definiert sich über den technologischen Fortschritt. Künstliche Intelligenz (KI) wird die praktische Tätigkeit in der Kinder- und Jugendradiologie künftig in allen Belangen verändern. Bildakquisition, Befunderkennung und -segmentierung sowie die Erkennung von Gewebeeigenschaften und deren Kombination mit Big Data werden die Haupteinsatzgebiete in der Radiologie sein. Höhere Effektivität, Beschleunigung von Untersuchung und Befundung sowie Kosteneinsparung sind mit der Anwendung von KI verbundene Erwartungshaltungen. Ein verbessertes Patientenmanagement, Arbeitserleichterungen für medizinisch-technische Radiologieassistenten und Kinder- und Jugendradiologen sowie schnellere Untersuchungs- und Befundzeiten markieren die Meilensteine der KI-Entwicklung in der Radiologie. Von der Terminkommunikation und Gerätesteuerung bis zu Therapieempfehlung und -monitoring wird der Alltag durch Elemente der KI verändert. Kinder- und Jugendradiologen müssen daher grundlegend über KI informiert sein und mit Datenwissenschaftlern bei der Etablierung und Anwendung von KI-Elementen zusammenarbeiten.

Evaluation of Bone Age in Children: A Mini-Review

Bone age represents a common index utilized in pediatric radiology and endocrinology departments worldwide for the definition of skeletal maturity for medical and non-medical purpose. It is defined by the age expressed in years that corresponds to the level of maturation of bones. Although several bones have been studied to better define bone age, the hand and wrist X-rays are the most used images. In fact, the images obtained by hand and wrist X-ray reflect the maturity of different types of bones of the skeletal segment evaluated. This information, associated to the characterization of the shape and changes of bone components configuration, represent an important factor of the biological maturation process of a subject. Bone age may be affected by several factors, including gender, nutrition, as well as metabolic, genetic, and social factors and either acute and chronic pathologies especially hormone alteration. As well several differences can be characterized according to the numerous standardized methods developed over the past decades. Therefore, the complete characterization of the main methods and procedure available and particularly of all their advantages and disadvantages need to be known in order to properly utilized this information for all its medical and non-medical main fields of application.

Are Automated and Visual Greulich and Pyle-Based Methods Applicable to Caucasian European Children With a Moroccan Ethnic Origin When Assessing Bone Age?

Introduction To test the accuracy of the visual and automated bone age assessment base on the Greulich and Pyle (GP) method in healthy Caucasian European children with a Moroccan ethnic origin. Material and methods Moroccan Caucasian (MC) children were retrospectively and consecutively enrolled along with age- and sex-matched control group (CG) of European Caucasian (EC) children enrolled from the general population. The two groups included 423 children aged from 2 to 15 years with a normal left-hand radiograph performed to rule out a trauma between March 2008 and December 2017. One radiologist, blinded to the BoneXpert^® (Visiana, Holte, Denmark) estimates, visually reviewed the radiographs using the GP atlas. The BoneXpert^® automatically analysed all 423 radiographs. The intraclass correlation coefficient (ICC), linear regression and Bland-Altman plots were performed to describe the agreement between each method and the chronological age (CA) and the agreement between the two methods. Results Visual bone age assessment was related to the CA in both girls (MC ICC 0.97; EC ICC 0.97) and boys (MC ICC 0.95; EC ICC 0.96). Automated bone age assessment was related to the CA in both girls (MC ICC 0.97; EC ICC 0.96) and boys (MC ICC 0.88; EC ICC 0.96). Bland-Altman plots showed an excellent agreement between the two methods in both sexes and ethnicities before puberty especially in Moroccan boys. Conclusion Visual and automatic bone age assessment based on the GP method, previously validated in the general population of Caucasian European children, can be confidently used in healthy Caucasian European children with a Moroccan ethnic origin.

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.

Impaired Bone Health in Children With Biliary Atresia

OBJECTIVES

The aim of the study was to examine the frequency of rickets and bone fractures and to assess areal bone mineral density (aBMD) in childhood among patients with biliary atresia (BA).

METHODS

We gathered data on all patients diagnosed with BA in Finland that survived to ≥1 year of age between 1 January 2000 to 30 June 2018. Data on gestational age, birth weight, postsurgical medications, and history of rickets and bone fractures were collected retrospectively. Serum levels of 25-hydroxyvitamin D [25(OH)D] postportoenterostomy (PE) were collected. Plain radiographs and dual energy X-ray absorptiometry (DXA) measurements of study subjects were reviewed.

RESULTS

Out of 49 patients, 7 (14%) were diagnosed with rickets during infancy. Clearance of jaundice [odds ratio 0.055, 95% confidence interval [CI] 0.00266-0.393; P < 0.01] was a protective factor against rickets. Sufficient 25(OH)D levels were reached 3 months post-PE. Eleven (22%) patients suffered at least one bone fracture (range 1-9) during childhood and adolescence. In DXA measurements, median lumbar spine aBMD anthropometrically adjusted z-scores were as follows: in native liver survivors 0.8 (interquartile range [IQR] -1.9 to 1.4) at 5 and -0.3 (IQR -1.3 to 0.8) at 10 years and for liver transplanted patients 0.4 (IQR -0.2 to 1.1) at 5 and 0.6 (IQR -0.1 to 1.3) at 10 years.

CONCLUSIONS

BA patients have an increased risk for rickets and bone fractures compared with the normal population. Most BA patients have aBMD within normal range between 5 and 10 years of age irrespective of liver transplantation status.

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.

Towards a Rational and Efficient Diagnostic Approach in Children Referred for Tall Stature and/or Accelerated Growth to the General Paediatrician

Tall stature and/or accelerated growth (TS/AG) in a child can be the result of a primary or secondary growth disorder, but more frequently no cause can be found (idiopathic TS). The conditions with the most important therapeutic implications are Klinefelter syndrome, Marfan syndrome and secondary growth disorders such as precocious puberty, hyperthyroidism and growth hormone excess. We propose a diagnostic flow chart offering a systematic approach to evaluate children referred for TS/AG to the general paediatrician. Based on the incidence, prevalence and clinical features of medical conditions associated with TS/AG, we identified relevant clues for primary and secondary growth disorders that may be obtained from the medical history, physical evaluation, growth analysis and additional laboratory and genetic testing. In addition to obtaining a diagnosis, a further goal is to predict adult height based on growth pattern, pubertal development and skeletal maturation. We speculate that an improved diagnostic approach in addition to expanding use of genetic testing may increase the diagnostic yield and lower the age at diagnosis of children with a pathologic cause of TS/AG.

Bone Health Index and bone turnover in pediatric patients with type 1 diabetes mellitus and poor metabolic control

BACKGROUND

There is a need for a non-invasive, affordable, and reliable method for bone health screening in pediatric patients at risk.

OBJECTIVE

To assess Bone Health Index (BHI) in pediatric patients with type 1 diabetes (T1D) and its relation to bone metabolism, age at onset, duration, control, and insulin dose.

SUBJECTS AND METHODS

Left-hand radiographs were obtained from 65 patients with T1D, mean age 11.23 ± 3.89 years, mean disease duration 5.23 ± 3.76 years and mean glycosylated hemoglobin (HbA1c)-83 mmol/mol (9.7%). Blood and 24 hours urine samples were collected for bone and mineral metabolism assessment. BoneXpert was used to determine BHI, Bone Health Index standard deviation score (BHI SDS), and bone age.

RESULTS

Mean BHI SDS was -1.15 ± 1.19 (n = 54). In 20.37% (n = 11) BHI SDS was < -2SD with mean value -2.82 ± 0. 69, P < .001. These patients had lower levels of beta cross laps (0.77  ± 0.33 ng/mL vs 1.17 ± 0.47 ng/mL), osteocalcin (47.20 ± 14.07 ng/mL vs 75.91 ± 32.08 ng/mL), serum magnesium (0.79 ± 0.05 mmol/L vs 0.83 ± 0.06 mmol/L) and phosphorus (1.48 ± 0.29 mmol/L vs 1.71 ± 0.28 mmol/L) but higher ionized calcium (1.29 ± 0.04 mmol/L vs 1.26 ± 0.05 mmol/L), P < .05, compared to patients with BHI SDS in the normal range. We found a positive correlation between BHI SDS and age at manifestation (r = 0.307, P = 0.024) and a negative one with disease duration (r = -0.284, P = .038). No correlations were found with HbA1c, insulin dose, height, weight, BMI.

CONCLUSIONS

To the best of our knowledge, this is the first study to assess bone health in pediatric patients with T1D using BHI. We found significantly decreased cortical bone density and bone turnover in 20.37%. Earlier age at onset and diabetes duration may have a negative impact on cortical bone density in patients with poor control. Longitudinal studies are needed to follow changes or to assess future interventions.

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.

Imaging in Short Stature and Bone Age Estimation

Short stature in children is a diagnostic challenge to the physician. Bone age assessment can be done using various methods. The causes of short stature are variable; often leading to a series of investigations. The endocrine conditions have typical imaging features. This chapter provides a short overview of the methods of bone age estimation, and imaging findings and algorithmic approach towards a child with short stature.

Well-founded practice or personal preference: a comparison of established techniques for measuring ulnar variance in healthy children and adolescents

Objectives

Ulnar variance is a clinical measure used to determine the relative difference in length between the radius and ulna. We aimed to examine consistency in ulnar variance measurements and normative data in children and adolescents using the perpendicular and the Hafner methods.

Methods

Two raters measured ulnar variance on hand radiographs of 350 healthy children. Participants’ mean calendar and skeletal ages were 12.3 ± 3.6 and 12.0 ± 3.7 years, 52% were female. Raters used the perpendicular method, an adapted version of the perpendicular method (in which the distal radial articular surface is defined as a sclerotic rim) and the Hafner method, being the distance between the most proximal points of the ulnar and radial metaphyses (PRPR) and the distance between the most distal points of both (DIDI). Intraclass correlation coefficients (ICCs) for intermethod consistency and inter- and intrarater agreement were calculated using a two-way ANOVA model. Variability and limits of agreement were determined using the Bland-Altman method.

Results

The interrater ICC was 0.75 (95% CI, 0.61–0.84) for the adapted perpendicular method, 0.88 (95% CI, 0.80–0.93) for PRPR, and 0.94 (95% CI, 0.90–0.97) for DIDI. The intermethod consistency ICC was 0.60 (95% CI, 0.48–0.70) for perpendicular versus PRPR and 0.60 (95% CI, 0.49–0.70) for perpendicular versus DIDI. The intrarater ICC was 0.88 (95% CI, 0.70–0.95) for perpendicular, 0.90 (95% CI, 0.83–0.94) for PRPR, and 0.81 (95% CI, 0.69–0.89) for DIDI. The perpendicular method was not useable in 38 cases (skeletal age ≤ 9 years) and the Hafner method in 79 cases (skeletal age ≥ 12 years).

Conclusions

The perpendicular and Hafner methods show moderate intermethod consistency. The Hafner method is preferred for children with skeletal ages < 14 years, with good to excellent inter- and intrarater agreement. The adapted perpendicular method is recommended for patients with skeletal ages ≥ 14 years.

Key Points

• The perpendicular method for measuring ulnar variance requires extended instructions to ensure good interrater agreement in pediatric and adolescent patients.

• The Hafner method is recommended for ulnar variance measurement in children with unfused growth plates and up to a skeletal age of 13 years, and the perpendicular method is recommended for children with fused growth plates and from skeletal age 14 and older.

• The mean ulnar variance measured in this study for each skeletal age group (range, 5–18 years) is provided, to serve as a reference for future ulnar variance measurements using both methods in clinical practice.

Electronic supplementary material

The online version of this article (10.1007/s00330-019-06354-x) contains supplementary material, which is available to authorized users.

Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis

BACKGROUND

The assessment of bone age and skeletal maturity and its comparison to chronological age is an important task in the medical environment for the diagnosis of pediatric endocrinology, orthodontics and orthopedic disorders, and legal environment in what concerns if an individual is a minor or not when there is a lack of documents. Being a time-consuming activity that can be prone to inter- and intra-rater variability, the use of methods which can automate it, like Machine Learning techniques, is of value.

OBJECTIVE

The goal of this paper is to present the state of the art evidence, trends and gaps in the research related to bone age assessment studies that make use of Machine Learning techniques.

METHOD

A systematic literature review was carried out, starting with the writing of the protocol, followed by searches on three databases: Pubmed, Scopus and Web of Science to identify the relevant evidence related to bone age assessment using Machine Learning techniques. One round of backward snowballing was performed to find additional studies. A quality assessment was performed on the selected studies to check for bias and low quality studies, which were removed. Data was extracted from the included studies to build summary tables. Lastly, a meta-analysis was performed on the performances of the selected studies.

RESULTS

26 studies constituted the final set of included studies. Most of them proposed automatic systems for bone age assessment and investigated methods for bone age assessment based on hand and wrist radiographs. The samples used in the studies were mostly comprehensive or bordered the age of 18, and the data origin was in most of cases from United States and West Europe. Few studies explored ethnic differences.

CONCLUSIONS

There is a clear focus of the research on bone age assessment methods based on radiographs whilst other types of medical imaging without radiation exposure (e.g. magnetic resonance imaging) are not much explored in the literature. Also, socioeconomic and other aspects that could influence in bone age were not addressed in the literature. Finally, studies that make use of more than one region of interest for bone age assessment are scarce.

SOFT syndrome in a patient from Chile

SOFT syndrome (MIM614813) is an extremely rare primordial dwarfism caused by biallelic mutations in the POC1A gene. It is characterized by prenatal short stature, onychodysplasia, facial dysmorphism, hypotrichosis, and variable skeletal abnormalities including hypoplastic pelvis and sacrum, small hands, and cone-shaped epiphyses, as well as delayed bone age. To the best of our knowledge, only eight POC1A mutations have been reported in humans to date. We report a 7-year-old Chilean girl with SOFT syndrome arising from a novel POC1A mutation c. 649C>T, p.Arg217Trp. Although her clinical features were largely compatible with SOFT syndrome, hand X-ray examinations at 3.5 and 6 years unexpectedly showed normal bone age. Automated bone age determination was performed using image analysis software, BoneXpert. This case highlights the importance of the accumulation of patients with POC1A mutations to further elucidate the detailed clinical features of SOFT syndrome.

Assessment of Reliability of Greulich and Pyle (GP) Method for Determination of Age of Children at Debre Markos Referral Hospital, East Gojjam Zone

Background

Greulich and Pyle standards are the most widely used age estimation standards all over the world. The applicability of the Greulich and Pyle standards to populations which differ from their reference population is often questioned. This study aimed to assess the reliability of Greulich and Pyle (GP) method for determination of age of children at Debre Markos Referral Hospital, East Gojjam Zone, Ethiopia.

Subjects and Methods

Hospital based cross sectional study design was applied to children who came to Debre Markos Referral Hospital from May to October 2015 and fulfilled the inclusion criteria of the study. The data was analyzed using SPSS version 20 and medcalc version 15 softwares. Significance was set at α = 0.05.

Results

A total of 108 radiographs were analyzed. Chronological age in most of the children was under estimated. The mean under-estimation was 11.8 months in the female sample and 8.7 months in the male sample. Greulich and Pyle method became inapplicable for the sample at 16 years for females and 16.5 years for males and later. Delay in skeletal maturation was observed in both sexes, but the females in the sample matured earlier than the males.

Conclusion

The findings of this study suggest against the applicability of GP atlas which were not directly applicable to an East Gojjam Zone population. Large scale studies should be planned and nationwide guideline, and atlas which can easily be used throughout the country should be developed.

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.

Systematic assessment of the growth plates of the wrist in young gymnasts: development and validation of the Amsterdam MRI assessment of the Physis (AMPHYS) protocol

Objectives

To develop and validate a protocol for MRI assessment of the distal radial and ulnar periphyseal area in gymnasts and non-gymnasts.

Methods

Twenty-four gymnasts with wrist pain, 18 asymptomatic gymnasts and 24 non-gymnastic controls (33 girls) underwent MRI of the wrist on a 3T scanner. Sequences included coronal proton density-weighted images with and without fat saturation, and three-dimensional water-selective cartilage scan and T2 Dixon series. Skeletal age was determined using hand radiographs. Three experienced musculoskeletal radiologists established a checklist of possible (peri)physeal abnormalities based on literature and clinical experience. Five other musculoskeletal radiologists and residents evaluated 30 MRI scans (10 from each group) using this checklist and reliability was determined using the intraclass correlation coefficient (ICC) and Fleiss' kappa. A final evaluation protocol was established containing only items with fair to excellent reliability.

Results

Twenty-seven items were assessed for reliability. Intra-rater and inter-rater agreement was good to excellent (respective ICCs 0.60-0.91 and 0.60-0.78) for four epiphyseal bone marrow oedema-related items, physeal signal intensity, metaphyseal junction and depth of metaphyseal intrusions. For physeal thickness, thickness compared with proximal physis of first metacarpal, metaphyseal intrusions, physeal connection of intrusions and metaphyseal bone marrow signal intensity, intra-rater agreement was fair to excellent (ICC/kappa 0.55-0.85) and inter-rater agreement was fair (ICC/kappa 0.41-0.59). Twelve items were included in the final protocol.

Conclusion

The Amsterdam MRI assessment of the Physis protocol facilitates patient-friendly and reliable assessment of the (peri)physeal area in the radius and ulna.

Bone Age: A Handy Tool for Pediatric Providers

Pediatricians have relied on methods for determining skeletal maturation for >75 years. Bone age continues to be a valuable tool in assessing children's health. New technology for bone age determination includes computer-automated readings and assessments obtained from alternative imaging modalities. In addition, new nonclinical bone age applications are evolving, particularly pertaining to immigration and children's rights to asylum. Given the significant implications when bone ages are used in high-stake decisions, it is necessary to recognize recently described limitations in predicting accurate age in various ethnicities and diseases. Current methods of assessing skeletal maturation are derived from primarily white populations. In modern studies, researchers have explored the accuracy of bone age across various ethnicities in the United States. Researchers suggest there is evidence that indicates the bone ages obtained from current methods are less generalizable to children of other ethnicities, particularly children with African and certain Asian backgrounds. Many of the contemporary methods of bone age determination may be calibrated to individual populations and hold promise to perform better in a wider range of ethnicities, but more data are needed.

Joint cartilage thickness and automated determination of bone age and bone health in juvenile idiopathic arthritis

BACKGROUND

BoneXpert is an automated method to calculate bone maturation and bone health index (BHI) in children with juvenile idiopathic arthritis (JIA). Cartilage thickness can also be seen as an indicator for bone health and arthritis damage. The objective of this study was to evaluate the relation between cartilage thickness, bone maturation and bone health in patients with JIA.

METHODS

Patients with JIA diagnosed according ILAR criteria included in a previous ultrasonography (US) study were eligible if hand radiographs were taken at the same time as the US examination. Of the 95 patients 67 met the inclusion criteria.

RESULTS

Decreased cartilage thickness was seen in 27% of the examined joints. Decreased BHI was seen in half of the JIA patient, and delayed bone maturation was seen in 33% of patients. A combination of decreased BHI and bone age was seen in 1 out of 5 JIA patients. Decreased cartilage thickness in the knee, wrist and MCP joint was negatively correlated with delayed bone maturation but not with bone health index.

CONCLUSION

Delayed bone maturation and decreased BHI were not related to a thinner cartilage, but a thicker cartilage. No relation with JADAS 10 was found. The rheumatologist should remain aware of delayed bone maturation and BHI in JIA patients with cartilage changes, even in the biologic era.

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.

Metacarpal bone loss in patients with rheumatoid arthritis estimated by a new Digital X-ray Radiogrammetry method - initial results

BACKGROUND

The Digital X-ray Radiogrammetry (DXR) method measures the cortical bone thickness in the shafts of the metacarpals and has demonstrated its relevance in the assessment of hand bone loss caused by rheumatoid arthritis (RA). The aim of this study was to validate a novel approach of the DXR method in comparison with the original version considering patients with RA.

METHOD

The study includes 49 patients with verified RA. The new version is an extension of the BoneXpert method commonly used in pediatrics which has these characteristics: (1) It introduces a new technique to analyze the images which automatically validates the results for most images, and (2) it defines the measurement region relative to the ends of the metacarpals. The BoneXpert method measures the Metacarpal Index (MCI) at the metacarpal bone (II to IV). Additionally, the MCI is quantified by the DXR X-posure System.

RESULTS

The new version correctly analyzed all 49 images, and 45 were automatically validated. The standard deviation between the MCI results of the two versions was 2.9% of the mean MCI. The average Larsen score was 2.6 with a standard deviation of 1.3. The correlation of MCI to Larsen score was -0.81 in both versions, and there was no significant difference in their ability to detect erosions.

CONCLUSION

The new DXR version (BoneXpert) validated 92% of the cases automatically, while the same good correlation to RA severity could be presented compared to the old version.

Psoriatic arthritis is associated with bone loss of the metacarpals

Background

BoneXpert (BX) is a newly developed medical device based on digital X-ray radiogrammetry to measure human cortical bone thickness. The aim of this study was to quantify cortical bone loss of the metacarpals in patients with psoriatic arthritis (PsA) and compare these findings with other radiological scoring methods.

Methods

The study includes 104 patients with verified PsA. The BX method was used to measure the Metacarpal Index (MCI) at the metacarpal bones (II–IV). Additionally, the T-score of the MCI (T-score_MCI) was calculated. Radiographic severity was determined by the Psoriatic Arthritis Ratingen Score (Proliferation Score and Destruction Score) as published by Wassenberg et al. and the Psoriatic Arthritis modified van der Heijde Sharp Score (Joint Space Narrowing Score and Erosion Score).

Results

For the total PsA study cohort, the T-score_MCI was significantly reduced by −1.289 ± 1.313 SD. The MCI negatively correlated with the Proliferation Score (r = −0.732; p < 0.001) and the Destruction Score (r = −0.771; p < 0.001) of the Psoriatic Arthritis Ratingen Score. Lower coefficients of correlations were observed for the Psoriatic Arthritis modified van der Heijde Sharp Score. In this context, a severity-dependent and PsA-related periarticular demineralisation as measured by the MCI was quantified. The strongest reduction of −30.8 % (p < 0.01) was observed for the MCI in the Destruction Score.

Conclusions

The BX MCI score showed periarticular demineralisation and severity-dependent bone loss in patients with PsA. The measurements of the BX technique were able to sensitively differentiate between the different stages of disease manifestation affecting bone integrity and thereby seem to achieve the potential to be a surrogate marker of radiographic progression in PsA.

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.

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.

Validation of adult height prediction based on automated bone age determination in the Paris Longitudinal Study of healthy children

BACKGROUND

An adult height prediction model based on automated determination of bone age was developed and validated in two studies from Zurich, Switzerland. Varied living conditions and genetic backgrounds might make the model less accurate.

OBJECTIVE

To validate the adult height prediction model on children from another geographical location.

MATERIALS AND METHODS

We included 51 boys and 58 girls from the Paris Longitudinal Study of children born 1953 to 1958. Radiographs were obtained once or twice a year in these children from birth to age 18. Bone age was determined using the BoneXpert method. Radiographs in children with bone age greater than 6 years were considered, in total 1,124 images.

RESULTS

The root mean square deviation between the predicted and the observed adult height was 2.8 cm for boys in the bone age range 6-15 years and 3.1 cm for girls in the bone age range 6-13 years. The bias (the average signed difference) was zero, except for girls below bone age 12, where the predictions were 0.8 cm too low.

CONCLUSION

The accuracy of the BoneXpert method in terms of root mean square error was as predicted by the model, i.e. in line with what was observed in the Zurich studies.

Automated radiogrammetry is a feasible method for measuring bone quality and bone maturation in severely disabled children

BACKGROUND

Children with severe neurological impairment and intellectual disability are prone to low bone quality and fractures.

OBJECTIVE

We studied the feasibility of automated radiogrammetry in assessing bone quality in this specific group of children. We measured outcome of bone quality and, because these children tend to have altered skeletal maturation, we also studied bone age.

MATERIALS AND METHODS

We used hand radiographs obtained in 95 children (mean age 11.4 years) presenting at outpatient paediatric clinics. We used BoneXpert software to determine bone quality, expressed as paediatric bone index and bone age.

RESULTS

Regarding feasibility, we successfully obtained a paediatric bone index in 60 children (63.2%). The results on bone quality showed a mean paediatric bone index standard deviation score of -1.85, significantly lower than that of healthy peers (P < 0.0001). Almost 50% of the children had severely diminished bone quality. In 64% of the children bone age diverged more than 1 year from chronological age. This mostly concerned delayed bone maturation.

CONCLUSION

Automated radiogrammetry is feasible for evaluating bone quality in children who have disabilities but not severe contractures. Bone quality in these children is severely diminished. Because bone maturation frequently deviated from chronological age, we recommend comparison to bone-age-related reference values.

Automated Greulich-Pyle bone age determination in children with chronic kidney disease

BACKGROUND

Growth restriction and retarded bone age are common findings in children with chronic kidney disease (CKD). We compared the automated BoneXpert™ method with the manual assessment of an X-ray of the non-dominant hand.

METHODS

In this retrospective multicenter study, 359 patients with CKD stages 2-5, aged 2-14.5 (girls) or 2.5-17 years (boys) were included. Bone age was determined manually by three experts (according to Greulich and Pyle). Automated determination of bone age was performed using the image analysis software BoneXpert™.

RESULTS

There was a strong correlation between the automatic and the manual method (r = 0.983, p < 0.001). The automatic method tended to generate higher bone age values (0.64 ± 0.73 years) in the younger patients (4-5 years) and to underestimate retardation or acceleration of bone age. The so-called "bone health index" (BHI) was reduced in comparison to the reference population. Bone health index standard deviation score (BHI-SDS) was not related to the stage of CKD, but weakly negatively correlated with plasma PTH concentrations (r = 0.12, p = 0.019).

CONCLUSIONS

BoneXpert™ allows an objective, time-saving, and in general valid bone age assessment in children with CKD. Possible underestimation of retarded or accelerated bone age should be taken into account. Validation of the BHI needs further study.

Hand X-ray in pediatric endocrinology: Skeletal age assessment and beyond

Skeletal age assessment (SAA) is a clinical procedure which is used in determining the SA of children and adolescents. Bone development is influenced by a number of factors, including nutrition, hormonal secretions, and genetics. There are several factors to be borne in mind when using methods of assessing skeletal maturity. These include: Variability among methods, degree of variability in the estimation of skeletal maturation, sources of low accuracy, and dispersion of the values of skeletal maturation. Currently, the main clinical methods for SAA are the Greulich and Pyle (GP) and Tanner and Whitehouse (TW) methods. The GP method has the advantage of being quick and easy to use. A well-trained radiologist takes few minutes to determine the bone age (BA) from a single hand radiograph. The method of TW, however, seems to be more reliable than the GP method. In recent years, the increasing speed in computer sciences and reduction of their cost has given the opportunity to create and use computerized BA estimation system. Despite the fact that the number of automated systems for BAA have increased, most are still within the experimental phase. The use of automated BA determination system, cleared for clinical use in Europe (BoneXpert), has been validated for various ethnicities and children with endocrine disorders. Ultrasound imaging has some limitations that include operator dependence, lower intra-rater and inter-rater reliability of assessment and difficulties with standardization of documentation and imaging transfer. Magnetic resonance imaging (MRI) is noninvasive alternative tool for SA assessment in children. However, few studies have been reported on this topic, and further research is needed to evaluate the reliability and validity of MRI BAAs. In conclusion, at present radiographic methods for the assessment of BA remain the gold standards. Whatever method one adopts, it is essential to minimize the causes of imprecision by taking care to consider the quality of the X-ray. Moreover, it is imperative to assume a correct hand positioning because poor positioning can change the appearance of some bones. It is also preferable to employ scoring methods to these techniques and percentiles rather than BA in years and months. In addition, the possible differences in maturation among different population should be kept in mind.

Automated determination of bone age and bone mineral density in patients with juvenile idiopathic arthritis: a feasibility study

Introduction

Chronic inflammation combined with glucocorticoid treatment and immobilization puts juvenile idiopathic arthritis (JIA) patients at risk of impaired growth and reduced bone mineral density (BMD). Conventional methods for evaluating bone age and BMD are time-consuming or come with additional costs and radiation exposure. In addition, an automated measurement of bone age and BMD is likely to be more consistent than visual evaluation. In this study, we aimed to evaluate the feasibility of an automated method for determination of bone age and (cortical) bone mineral density (cBMD) in severely affected JIA patients. A secondary objective was to describe bone age and cBMD in this specific JIA population eligible for biologic treatment.

Methods

In total, 69 patients with standard hand radiographs at the start of etanercept treatment and of calendar age within the reliability ranges (2.5 to 17 years for boys and 2 to 15 years for girls) were extracted from the Dutch Arthritis and Biologicals in Children register. Radiographs were analyzed using the BoneXpert method, thus automatically determining bone age and cBMD expressed as bone health index (BHI). Agreement between measurements of the left- and right-hand radiographs and a repeated measurement of the left hand were assessed with the intraclass correlation coefficient (ICC). Regression analysis was used to identify variables associated with Z-scores of bone age and BHI.

Results

The BoneXpert method was reliable in the evaluation of radiographs of 67 patients (radiographs of 2 patients were rejected because of poor image quality). Agreement between left- and right-hand radiographs (ICC = 0.838 to 0.996) and repeated measurements (ICC = 0.999 to 1.000) was good. Mean Z-scores of bone age (−0.36, P = 0.051) and BHI (−0.85, P < 0.001) were lower compared to the healthy population. Glucocorticoid use was associated with delayed bone age (0.79 standard deviation (SD), P = 0.028), and male gender was associated with a lower Z-score of BHI (0.65 SD, P = 0.021).

Conclusions

BoneXpert is an easy-to-use method for assessing bone age and cBMD in patients with JIA, provided that radiographs are of reasonable quality and patients’ bone age lies within the age ranges of the program. The population investigated had delayed bone maturation and lower cBMD than healthy children.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-014-0424-1) contains supplementary material, which is available to authorized users.

Validation of automatic bone age determination in children with congenital adrenal hyperplasia

BACKGROUND

Determination of bone age is routinely used for following up substitution therapy in congenital adrenal hyperplasia (CAH) but today is a procedure with significant subjectivity.

OBJECTIVE

The aim was to test the performance of automatic bone age rating by the BoneXpert software package in all radiographs of children with CAH seen at our clinic from 1975 to 2006.

MATERIALS AND METHODS

Eight hundred and ninety-two left-hand radiographs from 100 children aged 0 to 17 years were presented to a human rater and BoneXpert for bone age rating. Images where ratings differed by more than 1.5 years were each rerated by four human raters.

RESULTS

Rerating was necessary in 20 images and the rerating result was closer to the BoneXpert result than to the original manual rating in 18/20 (90 %). Bone age rating precision based on the smoothness of longitudinal curves comprising a total of 327 data triplets spanning less than 1.7 years showed BoneXpert to be more precise (P<0.001).

CONCLUSION

BoneXpert performs reliable bone age ratings in children with CAH.

Bone age assessment: automated techniques coming of age?

Bone age determination from hand radiographs is one of the oldest radiographic procedures. The first atlas was published by Poland in 1898, and to date the Greulich Pyle atlas, although it dates from 1959, is still the most commonly used method. Bone age rating is time-consuming, suffers from an unsatisfactorily high rater variability, and therefore already 25 years ago it was proposed to replace the manual rating by an automated, computerized method, a field nowadays referred to as computer-aided diagnosis (CAD). The pursuit of this goal reached a first stage of accomplishment in 1992-1996 with the presentation of several systems. However, they had limited clinical value, and efforts in CAD research were increasingly focused on lesion detection for cancer screening. It was only in 2008 that a fully-automated bone age method was presented, which appears to be clinically acceptable. In this paper we consider the requirements that should be met by an automated bone age method and review the state of the art. Integration in PACS and saving time are important factors for radiologists. But it is the validation of the methods which poses the greatest challenge, because there is no gold standard for bone age rating, and the direct comparison to manual rating is therefore not sufficient for demonstrating that manual rating can be replaced by automated rating. One needs additional studies assessing the precision of a method and its accuracy when used for adult height prediction, which serves as an objective.