The BoneXpert method for automated determination of skeletal maturity

Mimicking the radiologists' workflow: Estimating pediatric hand bone age with stacked deep neural networks

Pediatric endocrinologists regularly order radiographs of the left hand to estimate the degree of bone maturation in order to assess their patients for advanced or delayed growth, physical development, and to monitor consecutive therapeutic measures. The reading of such images is a labor-intensive task that requires a lot of experience and is normally performed by highly trained experts like pediatric radiologists. In this paper we build an automated system for pediatric bone age estimation that mimics and accelerates the workflow of the radiologist without breaking it. The complete system is based on two neural network based models: on the one hand a detector network, which identifies the ossification areas, on the other hand gender and region specific regression networks, which estimate the bone age from the detected areas. With a small annotated dataset an ossification area detection network can be trained, which is stable enough to work as part of a multi-stage approach. Furthermore, our system achieves competitive results on the RSNA Pediatric Bone Age Challenge test set with an average error of 4.56 months. In contrast to other approaches, especially purely encoder-based architectures, our two-stage approach provides self-explanatory results. By detecting and evaluating the individual ossification areas, thus simulating the workflow of the Tanner-Whitehouse procedure, the results are interpretable for a radiologist.

Diagnostic performance of convolutional neural network-based Tanner-Whitehouse 3 bone age assessment system

Background

Bone age can reflect the true growth and development status of a child; thus, it plays a critical role in evaluating growth and endocrine disorders. This study established and validated an optimized Tanner-Whitehouse 3 artificial intelligence (TW3-AI) bone age assessment (BAA) system based on a convolutional neural network (CNN).

Methods

A data set of 9,059 clinical radiographs of the left hand was obtained from the picture archives and communication systems (PACS) between January 2012 and December 2016. Among these, 8,005/9,059 (88%) samples were treated as the training set for model implementation, 804/9,059 (9%) samples as the validation set for parameters optimization, and the remaining 250/9,059 (3%) samples were used to verify the accuracy and reliability of the model compared to that of 4 experienced endocrinologists and 2 experienced radiologists. The overall variation of TW3-metacarpophalangeal, radius, ulna and short bones (RUS) and TW3-Carpal bone score, as well as each bone (13 RUS + 7 Carpal) between reviewers and the AI, were compared by Bland-Altman (BA) chart and Kappa test, respectively. Furthermore, the time consumption between the model and reviewers was also compared.

Results

The performance of TW3-AI model was highly consistent with the reviewers' overall estimation, and the root mean square (RMS) was 0.50 years. The accuracy of the BAA of the TW3-AI model was better than the estimate of the reviewers. Further analysis revealed that human interpretations of the male capitate, hamate, the first distal and fifth middle phalanx and female capitate, the trapezoid, and the third and fifth middle phalanx, were most inconsistent. The average image processing time was 1.5±0.2 s in the TW3-AI model, which was significantly shorter than manual interpretation.

Conclusions

The diagnostic performance of CNN-based TW3 BAA was accurate and timesaving, and possesses better stability compared to diagnostics made by experienced experts.

Assessing the Bone Age of Children in an Automatic Manner Newborn to 18 Years Range

Bone age assessment (BAA) is a radiological process to identify the growth disorders in children. Although this is a frequent task for radiologists, it is cumbersome. The objective of this study is to assess the bone age of children from newborn to 18 years old in an automatic manner through computer vision methods including histogram of oriented gradients (HOG), local binary pattern (LBP), and scale invariant feature transform (SIFT). Here, 442 left-hand radiographs are applied from the University of Southern California (USC) hand atlas. In this experiment, for the first time, HOG-LBP-dense SIFT features with background subtraction are applied to assess the bone age of the subject group. For this purpose, features are extracted from the carpal and epiphyseal regions of interest (ROIs). The SVM and 5-fold cross-validation are used for classification. The accuracy of female radiographs is 73.88% and of the male is 68.63%. The mean absolute error is 0.5 years for both genders' radiographs. The accuracy a within 1-year range is 95.32% for female and 96.51% for male radiographs. The accuracy within a 2-year range is 100% and 99.41% for female and male radiographs, respectively. The Cohen's kappa statistical test reveals that this proposed approach, Cohen's kappa coefficients are 0.71 for female and 0.66 for male radiographs, p value < 0.05, is in substantial agreement with the bone age assessed by experienced radiologists within the USC dataset. This approach is robust and easy to implement, thus, qualified for computer-aided diagnosis (CAD). The reduced processing time and number of ROIs facilitate BAA.

Bone age determination using only the index finger: a novel approach using a convolutional neural network compared with human radiologists

BACKGROUND

Recently developed convolutional neural network (CNN) models determine bone age more accurately than radiologists.

OBJECTIVE

The purpose of this study was to determine whether a CNN and radiologists can accurately predict bone age from radiographs using only the index finger rather than the whole hand.

MATERIALS AND METHODS

We used a public anonymized dataset provided by the Radiological Society of North America (RSNA) pediatric bone age challenge. The dataset contains 12,611 hand radiographs for training and 200 radiographs for testing. The index finger was cropped from these images to create a second dataset. Separate CNN models were trained using the whole-hand radiographs and the cropped second-digit dataset using the consensus ground truth provided by the RSNA bone age challenge. Bone age determination using both models was compared with ground truth as provided by the RSNA dataset. Separately, three pediatric radiologists determined bone age from the whole-hand and index-finger radiographs, and the consensus was compared to the ground truth and CNN-model-determined bone ages.

RESULTS

The mean absolute difference between the ground truth and CNN bone age for whole-hand and index-finger was similar (4.7 months vs. 5.1 months, P=0.14), and both values were significantly smaller than that for radiologist bone age determination from the single-finger radiographs (8.0 months, P<0.0001).

CONCLUSION

CNN-model-determined bone ages from index-finger radiographs are similar to whole-hand bone age interpreted by radiologists in the dataset, as well as a model trained on the whole-hand radiograph. In addition, the index-finger model performed better than the ground truth compared to subspecialty trained pediatric radiologists also using only the index finger to determine bone age. The radiologist interpreting bone age can use the second digit as a reliable starting point in their search pattern.

Towards fully automated third molar development staging in panoramic radiographs

Staging third molar development is commonly used for age assessment in sub-adults. Current staging techniques are, at most, semi-automated and rely on manual interactions prone to operator variability. The aim of this study was to fully automate the staging process by employing the full potential of deep learning, using convolutional neural networks (CNNs) in every step of the procedure. The dataset used to train the CNNs consisted of 400 panoramic radiographs (OPGs), with 20 OPGs per developmental stage per sex, staged in consensus between three observers. The concepts of transfer learning, using pre-trained CNNs, and data augmentation were used to mitigate the issues when dealing with a limited dataset. In this work, a three-step procedure was proposed and the results were validated using fivefold cross-validation. First, a CNN localized the geometrical center of the lower left third molar, around which a square region of interest (ROI) was extracted. Second, another CNN segmented the third molar within the ROI. Third, a final CNN used both the ROI and the segmentation to classify the third molar into its developmental stage. The geometrical center of the third molar was found with an average Euclidean distance of 63 pixels. Third molars were segmented with an average Dice score of 93%. Finally, the developmental stages were classified with an accuracy of 54%, a mean absolute error of 0.69 stages, and a linear weighted Cohen's kappa coefficient of 0.79. The entire automated workflow on average took 2.72 s to compute, which is substantially faster than manual staging starting from the OPG. Taking into account the limited dataset size, this pilot study shows that the proposed fully automated approach shows promising results compared with manual staging.

Fully Automated Bone Age Assessment on Large-Scale Hand X-Ray Dataset

Bone age assessment (BAA) is an essential topic in the clinical practice of evaluating the biological maturity of children. Because the manual method is time-consuming and prone to observer variability, it is attractive to develop computer-aided and automated methods for BAA. In this paper, we present a fully automatic BAA method. To eliminate noise in a raw X-ray image, we start with using U-Net to precisely segment hand mask image from a raw X-ray image. Even though U-Net can perform the segmentation with high precision, it needs a bigger annotated dataset. To alleviate the annotation burden, we propose to use deep active learning (AL) to select unlabeled data samples with sufficient information intentionally. These samples are given to Oracle for annotation. After that, they are then used for subsequential training. In the beginning, only 300 data are manually annotated and then the improved U-Net within the AL framework can robustly segment all the 12611 images in RSNA dataset. The AL segmentation model achieved a Dice score at 0.95 in the annotated testing set. To optimize the learning process, we employ six off-the-shell deep Convolutional Neural Networks (CNNs) with pretrained weights on ImageNet. We use them to extract features of preprocessed hand images with a transfer learning technique. In the end, a variety of ensemble regression algorithms are applied to perform BAA. Besides, we choose a specific CNN to extract features and explain why we select that CNN. Experimental results show that the proposed approach achieved discrepancy between manual and predicted bone age of about 6.96 and 7.35 months for male and female cohorts, respectively, on the RSNA dataset. These accuracies are comparable to state-of-the-art performance.

High frequency of pathogenic ACAN variants including an intragenic deletion in selected individuals with short stature

CONTEXT

Defining the underlying etiology of idiopathic short stature (ISS) improves the overall management of an individual.

OBJECTIVE

To assess the frequency of pathogenic ACAN variants in selected individuals.

DESIGN

The single-center cohort study was conducted at a tertiary university children's hospital. From 51 unrelated patients with ISS, the 16 probands aged between 3 and 18 years (12 females) with advanced bone age and/or autosomal dominant inheritance pattern of short stature were selected for the study. Fifteen family members of ACAN-positive probands were included. Exome sequencing was performed in all probands, and additional copy number variation (CNV) detection was applied in selected probands with a distinct ACAN-associated phenotype.

RESULTS

Systematic phenotyping of the study cohort yielded 37.5% (6/16) ACAN-positive probands, with all novel pathogenic variants, including a 6.082 kb large intragenic deletion, detected by array comparative genomic hybridization (array CGH) and exome data analysis. All variants were co-segregated with short stature phenotype, except in one family member with the intragenic deletion who had an unexpected growth pattern within the normal range (-0.5 SDS). One patient presented with otosclerosis, a sign not previously associated with aggrecanopathy.

CONCLUSIONS

ACAN pathogenic variants presented a common cause of familial ISS. The selection criteria used in our study were suggested for a personalized approach to genetic testing of the ACAN gene in clinical practice. Our results expanded the number of pathogenic ACAN variants, including the first intragenic deletion, and suggested CNV evaluation in patients with typical clinical features of aggrecanopathy as reasonable. Intra-familial phenotypic variability in growth patterns should be considered.

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.

The distal radial physis: Exploring normal anatomy on MRI enables interpretation of stress related changes in young gymnasts

Rationale: Explore the MRI-appearance of the healthy distal radial physis and the distribution of stress-related changes in physeal thickness in young gymnasts to aid in the understanding of the pathophysiological process of stress-related physeal injury. Methods: Symptomatic gymnasts with clinically suspected overuse injury of the distal radial physis and age and gender-matched asymptomatic gymnasts and healthy non-gymnasts underwent an MRI-scan of the wrist. A cartilage-specific sequence was used to obtain three-dimensional reconstructions of the distal radial physis. Heat maps and line charts of these reconstructions visualised distribution of physeal thickness per study group and were used to explore differences between study groups. Symptomatic gymnasts displaying the most profound physeal widening (n = 10) were analysed separately. Results: Twenty-seven symptomatic - (skeletal age 12.9 ± 1.5 years), 16 asymptomatic - (skeletal age 12.8 ± 1.9 years) and 23 non-gymnasts (skeletal age 13.6 ± 1.9 years) were included for analysis. Physes of healthy non-gymnasts had a thin centre and increased in thickness towards the borders. Gymnasts demonstrated an increase in thickness of the entire physeal surface. In symptomatic gymnasts increase in physeal thickness was most prominent at the volar side when compared to asymptomatic gymnasts and non-gymnasts. Conclusion: The healthy distal radial physis is characterised by a thin centre surrounded by thicker borders. Stress applied to the wrist during gymnastics causes an overall increase in physeal thickness. Profound thickness increase is present at the volar side of the physis mainly in symptomatic gymnasts. These results can help unravel the pathophysiological mechanism of stress-related physeal injury in gymnasts and aid early injury identification.

Transition analysis applied to third molar development in a Danish population

INTRODUCTION

Age assessment based on dental development is often requested in order to assess whether an individual is older or younger than 18 years of age. There are several statistical approaches to estimate age based upon third molar development. The aim of this study was to apply the principles of transition analysis (TA) to a Danish reference material and to evaluate whether it was indicated to include a model that allows for logistic non-linearity as opposed to applying a model only allowing for logistic linearity. For this we chose to use the generalized additive model (gam) and the generalized linear model (glm), respectively.

MATERIAL AND METHOD

A cross-sectional sample comprising 1302 panoramic radiographs of Danish subjects in the chronological age range of 13-25 years was included. All present third molars had been scored according to the 10-stage method of Gleiser and Hunt. Each transition from one stage to the subsequent stage was analyzed according to the statistical approach of TA and fitted with both the generalized linear model (glm) and the generalized additive model (gam). In order to assess whether gam or glm was more parsimonious for each transition individually, the Akaikon information criterion (AIC) was applied.

RESULTS

The results emphasized the importance of applying a statistical model that sufficiently captures the spread of the age estimate. The AIC values showed that some transitions were sufficiently described by glm whereas for others the gam curves fitted significantly better.

CONCLUSION

We recommend that for an age assessment tool based on TA, both a fitting allowing for non-linearity and one allowing only for linearity should be included.

It's a thin line: development and validation of Dixon MRI-based semi-quantitative assessment of stress-related bone marrow edema in the wrists of young gymnasts and non-gymnasts

Purpose

To assess reliability and clinical utility of evaluating stress-related metaphyseal water distribution using a semi-quantitative Dixon MRI-based method for early diagnosis of physeal stress injuries in adolescent gymnasts.

Methods

Twenty-four gymnasts with clinically suspected overuse injury of the distal radial physis, 18 asymptomatic gymnasts, and 24 non-gymnast controls aged 12 ± 1.5 years prospectively underwent hand radiographs and 3T MRI of the wrist including coronal T1-weighted and T2-weighted Dixon sequences. Two raters measured metaphyseal water signal fraction in 13 radial and ulnar regions of interest (ROI). Inter- and intrarater reliability, interslice (between 3 middle radial slices), and inter-ROI (between 3 ROIs on same level) reliability were assessed using intraclass correlation coefficients (ICC). Water signal fractions and their within-person ratios in distal versus most proximal ROIs were compared between groups using one-way analysis of variance.

Results

Inter- and intrarater ICCs were 0.79–0.99 and 0.94–1.0 for T1-weighted, and 0.88–1.0 and 0.88–1.0 for T2-weighted Dixon. Interslice and inter-ROI ICCs were 0.55–0.94 and 0.95–0.97 for T1-weighted, and 0.70–0.96 and 0.96–0.97 for T2-weighted Dixon. Metaphyseal water signal fraction in symptomatic gymnasts was higher in six distal ROIs compared with asymptomatic gymnasts and in nine ROIs compared with non-gymnasts (p < 0.05). Metaphyseal water score (ratio of distal versus most proximal ROIs) was 1.61 in symptomatic gymnasts and 1.35 in asymptomatic gymnasts on T2-weighted Dixon (p < 0.05).

Conclusion

Semi-quantitative Dixon MRI-based water signal fraction assessment has good to excellent reproducibility and shows increased metaphyseal water scores in symptomatic gymnasts compared with asymptomatic gymnastic peers.

Key Points

• The proposed Dixon MRI-based semi-quantitative method for assessment of metaphyseal bone marrow water content is reliable, with off-the-shelf availability and short scan times.

• The metaphyseal water score allows comparisons between gymnasts using a within-person reference area for unaffected metaphyseal bone.

• As metaphyseal water score was increased in symptomatic gymnasts compared with asymptomatic gymnasts, this semi-quantitative method can potentially be used as an indicator of bone marrow edema in the early diagnosis of gymnastic physeal stress injury.

Electronic supplementary material

The online version of this article (10.1007/s00330-019-06446-8) contains supplementary material, which is available to authorized users.

Statistical Shape Modeling of Skeletal Anatomy for Sex Discrimination: Their Training Size, Sexual Dimorphism, and Asymmetry

Purpose: Statistical shape modeling provides a powerful tool for describing and analyzing human anatomy. By linearly combining the variance of the shape of a population of a given anatomical entity, statistical shape models (SSMs) identify its main modes of variation and may approximate the total variance of that population to a selected threshold, while reducing its dimensionality. Even though SSMs have been used for over two decades, they lack in characterization of their goodness of prediction, in particular when defining whether these models are actually representative for a given population.

Methods: The current paper presents, to the authors' knowledge, the most extent lower limb anatomy shape model considering the pelvis, femur, patella, tibia, fibula, talus, and calcaneum to date. The present study includes the segmented training shapes (n = 542) obtained from 271 lower limb CT scans. The different models were evaluated in terms of accuracy, compactness, generalizability as well as specificity.

Results: The size of training samples needed in each model so that it can be considered population covering was estimated to approximate around 200 samples, based on the generalizability properties of the different models. Simultaneously differences in gender and patterns in left-right asymmetry were identified and characterized. Size was found to be the most pronounced sexual discriminator whereas intra-individual variations in asymmetry were most pronounced at the insertion site of muscles.

Conclusion: For models aimed at population covering descriptive studies, the number of training samples required should amount a sizeable 200 samples. The geometric morphometric method for sex discrimination scored excellent, however, it did not largely outperformed traditional methods based on discrete measures.

Potential Value of Conventional Ultrasound in Estimation of Bone Age in Patients from Birth to Near Adulthood

We aimed to assess the relationship between standard bone age (BA) stratification obtained using plain radiography and that obtained using ultrasound determination of ossification ratio (OR) as a novelty in patients from birth to near adulthood. The ratio of diameters of the ossification center and epiphysis was calculated to evaluate the OR of bones. The ORs of the bones assigned to different weight coefficient were then summed as the skeletal maturity score (SMS). Pearson's correlation r between SMSs derived from ORs and BAs was 0.97 in girls (p < 0.001) and 0.97 in boys (p < 0.001), respectively. There are significant positive correlations between SMSs measured by conventional ultrasound imaging and BAs obtained by radiography of the hand and wrist in patients from birth to near adulthood. The scoring system may potentially provide a quantitative modality to estimate BA by conventional ultrasound.

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.

Effect of Lower Third Molar Segmentations on Automated Tooth Development Staging using a Convolutional Neural Network

Staging third molar development is commonly used for age estimation in subadults. Automated developmental stage allocation to the mandibular left third molar in panoramic radiographs has been examined in a pilot study. This method used an AlexNet Deep Convolutional Neural Network (CNN) approach to stage lower left third molars, which had been selected by manually drawn bounding boxes around them. This method (bounding box AlexNet = BA) still contained parts of surrounding structures which may have affected the automated stage allocation performance. We hypothesize that segmenting only the third molar could further improve the automated stage allocation performance. Therefore, the current study aimed to determine and validate the effect of lower third molar segmentations on automated tooth development staging. Retrospectively, 400 panoramic radiographs were collected, processed and segmented in three ways: bounding box (BB), rough (RS), and full (FS) tooth segmentation. A DenseNet201 CNN was used for automated stage allocation. Automated staging results were compared with reference stages - allocated by human observers - overall and per stage. FS rendered the best results with a stage allocation accuracy of 0.61, a mean absolute difference of 0.53 stages and a Cohen's linear κ of 0.84. Misallocated stages were mostly neighboring stages, and DenseNet201 rendered better results than AlexNet by increasing the percentage of correctly allocated stages by 3% (BA compared to BB). FS increased the percentage of correctly allocated stages by 7% compared to BB. In conclusion, full tooth segmentation and a DenseNet CNN optimize automated dental stage allocation for age estimation.

Applicability of two commonly used bone age assessment methods to twenty-first century UK children

Objectives

To assess the effect of secular change on skeletal maturation and thus on the applicability of the Greulich and Pyle (G&P) and Tanner and Whitehouse (TW3) methods.

Methods

BoneXpert was used to assess bone age from 392 hand trauma radiographs (206 males, 257 left). The paired sample t test was performed to assess the difference between mean bone age (BA) and mean chronological age (CA). ANOVA was used to assess the differences between groups based on socioeconomic status (taken from the Index of Multiple Deprivation).

Results

CA ranged from 2 to 15 years for females and 2.5 to 15 years for males. Numbers of children living in low, average and high socioeconomic areas were 216 (55%), 74 (19%) and 102 (26%) respectively. We found no statistically significant difference between BA and CA when using G&P. However, using TW3, CA was underestimated in females beyond the age of 3 years, with significant differences between BA and CA (− 0.43 years, SD 1.05, p = < 0.001) but not in males (0.01 years, SD 0.97, p = 0.76). Of the difference in females, 17.8% was accounted for by socioeconomic status.

Conclusion

No significant difference exists between BoneXpert-derived BA and CA when using the G&P atlas in our study population. There was a statistically significant underestimation of BoneXpert-derived BA compared with CA in females when using TW3, particularly in those from low and average socioeconomic backgrounds. Secular change has not led to significant advancement in skeletal maturation within our study population.

Key Points

• The Greulich and Pyle method can be applied to the present-day United Kingdom (UK) population.

• The Tanner and Whitehouse (TW3) method consistently underestimates the age of twenty-first century UK females by an average of 5 months.

• Secular change has not advanced skeletal maturity of present-day UK children compared with those of the mid-twentieth century.

Electronic supplementary material

The online version of this article (10.1007/s00330-019-06300-x) contains supplementary material, which is available to authorized users.

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.

Automated age estimation from MRI volumes of the hand

Highly relevant for both clinical and legal medicine applications, the established radiological methods for estimating unknown age in children and adolescents are based on visual examination of bone ossification in X-ray images of the hand. Our group has initiated the development of fully automatic age estimation methods from 3D MRI scans of the hand, in order to simultaneously overcome the problems of the radiological methods including (1) exposure to ionizing radiation, (2) necessity to define new, MRI specific staging systems, and (3) subjective influence of the examiner. The present work provides a theoretical background for understanding the nonlinear regression problem of biological age estimation and chronological age approximation. Based on this theoretical background, we comprehensively evaluate machine learning methods (random forests, deep convolutional neural networks) with different simplifications of the image information used as an input for learning. Trained on a large dataset of 328 MR images, we compare the performance of the different input strategies and demonstrate unprecedented results. For estimating biological age, we obtain a mean absolute error of 0.37 ± 0.51 years for the age range of the subjects  ≤  18 years, i.e. where bone ossification has not yet saturated. Finally, we validate our findings by adapting our best performing method to 2D images and applying it to a publicly available dataset of X-ray images, showing that we are in line with the state-of-the-art automatic methods for this task.

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.

Damage of the distal radial physis in young gymnasts: can three-dimensional assessment of physeal volume on MRI serve as a biomarker?

Objective

To explore the use of quantitative volume assessment to identify the presence and extent of stress-related changes of the distal radial physis in gymnasts with suspected physeal injury, asymptomatic gymnasts, and non-gymnasts.

Methods

Symptomatic gymnasts with clinically suspected distal radial physeal injury, asymptomatic gymnasts, and non-gymnasts (n = 69) were included and matched on skeletal age and sex. Volume measurements were performed on coronal water selective cartilage MRI images by creating three-dimensional physeal reconstructions semi-automatically using active-contour segmentation based on image-intensity thresholding. Inter- and intra-rater reliability of the measurements were assessed using intra-class correlation coefficients (ICC) for absolute agreement.

Results

Twenty-seven symptomatic-, 18 asymptomatic-, and 24 non-gymnasts were included with a median age of 13.9 years (interquartile range (IQR) 13.0–15.0 years). Median physeal volume was significantly increased (p < 0.05) in symptomatic- (971 mm³, IQR 787–1237 mm³) and asymptomatic gymnasts (951 mm³, IQR 871–1004 mm³) compared with non-gymnasts (646 mm³, IQR 538–795 mm³). Inter-rater (ICC 0.96, 95% CI 0.92–0.98) and intra-rater (ICC 0.93, 95% CI 0.85–0.97) reliability of volume measurements were excellent. Of the 10 participants with the highest physeal volumes, nine were symptomatic gymnasts.

Conclusion

Increased volume of the distal radial physis can reliably be assessed and is a sign of physeal stress that can be present in both symptomatic- and asymptomatic gymnasts, but gymnasts with suspected physeal injury showed larger volume increases. Future studies should explore if volume assessment can be used to (early) identify athletes with or at risk for physeal stress injuries of the wrist.

Key Points

• The volume of the distal radial physis can be reliably assessed by creating three-dimensional physeal reconstructions.

• Stress-related volume increase of the distal radial physis is present in symptomatic and asymptomatic gymnasts.

• Gymnasts with clinically suspected physeal injury showed larger volume increases compared with asymptomatic gymnasts and may therefore be a valuable addition in the (early) diagnostic workup of physeal stress injuries.

Incorporated region detection and classification using deep convolutional networks for bone age assessment

Bone age assessment plays an important role in the endocrinology and genetic investigation of patients. In this paper, we proposed a deep learning-based approach for bone age assessment by integration of the Tanner-Whitehouse (TW3) methods and deep convolution networks based on extracted regions of interest (ROI)-detection and classification using Faster-RCNN and Inception-v4 networks, respectively. The proposed method allows exploration of expert knowledge from TW3 and features engineering from deep convolution networks to enhance the accuracy of bone age assessment. The experimental results showed a mean absolute error of about 0.59 years between expert radiologists and the proposed method, which is the best performance among state-of-the-art methods.

A multi-scale data fusion framework for bone age assessment with convolutional neural networks

Bone age assessment (BAA) has various clinical applications such as diagnosis of endocrine disorders and prediction of final adult height for adolescents. Recent studies indicate that deep learning techniques have great potential in developing automated BAA methods with significant advantages over the conventional methods based on handcrafted features. In this paper, we propose a multi-scale data fusion framework for bone age assessment with X-ray images based on non-subsampled contourlet transform (NSCT) and convolutional neural networks (CNNs). Unlike the existing CNN-based BAA methods that adopt the original spatial domain image as network input directly, we pre-extract a rich set of features for the input image by performing NSCT to obtain its multi-scale and multi-direction representations. This feature pre-extraction strategy could be beneficial to network training as the number of annotated examples in the problem of BAA is typically quite limited. The obtained NSCT coefficient maps at each scale are fed into a convolutional network individually and the information from different scales are then merged to achieve the final prediction. Specifically, two CNN models with different data fusion strategies are presented for BAA: a regression model with feature-level fusion and a classification model with decision-level fusion. Experiments on the public BAA dataset Digital Hand Atlas demonstrate that the proposed method can obtain promising results and outperform many state-of-the-art BAA methods. In particular, the proposed approaches exhibit obvious advantages over the corresponding spatial domain approaches (generally with an improvement of more than 0.1 years on the mean absolute error), showing great potential in the future study of this field.

Bone age as a correction factor for the analysis of trabecular bone score (TBS) in children

Trabecular bone score (TBS) is a tool to improve evaluation of DXA scans, barely used in children. We proposed to evaluate TBS with bone age (BA) compared to chronological age (CA). In girls, TBS value using BA is constant until age 8, and in boys until age 10, and then starts to increase steadily. This data may help widen TBS use in pediatric populations.

INTRODUCTION

Trabecular bone score (TBS) is a software-based tool for the analysis of DXA images to assess bone microarchitecture in the lumbar region. It is used widely in adults to improve evaluation of fracture risk, yet it has been rarely studied in children and no normal curves have been developed for pediatrics. The purpose of this study was to evaluate bone (skeletal) age compared to chronological age to determine which is better in the pediatric population since both bone age (BA) and trabecular density are equally susceptible to change in response to similar factors.

METHODS

Total body, lumbar region, and non-dominant hand scans were obtained with an iDXA device in all participants. DXA scans of lumbar region for TBS analysis and AP images of non-dominant hand-for-BA were obtained for 565 children (269 female) aged 4to 19.

RESULTS

Simple correlation was calculated and r² values for TBS and chronological age were obtained by linear regression, with low correlations (0.36 for boys and 0.38 for girls), and then we created Loess curves to show the change for consecutive ages. In girls, the curve forms a U shape with a nadir point at approximately age 10. We then replaced chronological age with BA, and significant change was seen in the girls' curve, where a turning point is seen at age 8. In boys, a similar trend shows a turning point at age 10. Finally, BA-corrected TBS curves were constructed using LMS, obtaining curves with percentiles.

CONCLUSIONS

The use of BA in the analysis and interpretation of TBS may help widen its use in pediatric populations by enabling the appearance of normative data, but more information is needed to confirm this finding.

Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability

OBJECTIVE

Radiographic bone age assessment (BAA) is used in the evaluation of pediatric endocrine and metabolic disorders. We previously developed an automated artificial intelligence (AI) deep learning algorithm to perform BAA using convolutional neural networks. We compared the BAA performance of a cohort of pediatric radiologists with and without AI assistance.

MATERIALS AND METHODS

Six board-certified, subspecialty trained pediatric radiologists interpreted 280 age- and gender-matched bone age radiographs ranging from 5 to 18 years. Three of those radiologists then performed BAA with AI assistance. Bone age accuracy and root mean squared error (RMSE) were used as measures of accuracy. Intraclass correlation coefficient evaluated inter-rater variation.

RESULTS

AI BAA accuracy was 68.2% overall and 98.6% within 1 year, and the mean six-reader cohort accuracy was 63.6 and 97.4% within 1 year. AI RMSE was 0.601 years, while mean single-reader RMSE was 0.661 years. Pooled RMSE decreased from 0.661 to 0.508 years, all individually decreasing with AI assistance. ICC without AI was 0.9914 and with AI was 0.9951.

CONCLUSIONS

AI improves radiologist's bone age assessment by increasing accuracy and decreasing variability and RMSE. The utilization of AI by radiologists improves performance compared to AI alone, a radiologist alone, or a pooled cohort of experts. This suggests that AI may optimally be utilized as an adjunct to radiologist interpretation of imaging studies to improve performance.

Letrozole versus testosterone for promotion of endogenous puberty in boys with constitutional delay of growth and puberty: a randomised controlled phase 3 trial

BACKGROUND

The treatment of constitutional delay of growth and puberty (CDGP) is an underinvestigated area in adolescent medicine. We tested the hypothesis that peroral aromatase inhibition with letrozole is more efficacious than intramuscular injection of low-dose testosterone in inducing puberty in boys with CDGP.

METHODS

We did a randomised, controlled, open-label trial at four paediatric centres in Finland. Boys aged at least 14 years with CDGP who wanted medical intervention and exhibited the first signs of puberty were randomly assigned in blocks of ten to receive either six intramuscular injections of low-dose testosterone (about 1 mg/kg bodyweight) every 4 weeks for 6 months or peroral letrozole 2·5 mg once daily for 6 months. All boys were followed up for 6 months after the end of treatment. The primary outcomes were changes in testicular volume and hormonal markers of puberty at 6 months after treatment initiation, which were assessed in all participants who received the assigned treatment. All patients were included in the safety analysis. This study is registered with ClinicalTrials.gov, number NCT01797718.

FINDINGS

Between Aug 1, 2013, and Jan 30, 2017, 30 boys were randomly assigned to receive testosterone (n=15) or letrozole (n=15). One boy in the testosterone group was excluded from the primary analyses because of a protocol deviation. During treatment, boys in the letrozole group had higher serum concentrations of luteinising hormone, follicle-stimulating hormone, testosterone, and inhibin B than did boys in the testosterone group. Testicular growth from baseline to 6 months was greater in the letrozole group than in the testosterone group (7·2 mL [95% CI 5·2-9·3] vs 2·2 mL [1·4-2·9]; between-group difference per month 0·9 mL [95% CI 0·6-1·2], p<0·0001). Most adverse events were mild. One boy in the testosterone group had aggressive behaviour for 1 week after each injection, and one boy in the letrozole group had increased irritability at 6 months.

INTERPRETATION

Letrozole might be a feasible alternative treatment to low-dose testosterone for boys with CDGP who opt for medical intervention. However, the risks and benefits of manipulating the reproductive axis during early puberty should be weighed carefully.

FUNDING

Helsinki University Hospital, Academy of Finland, and Finnish Foundation for Pediatric Research.

Ossification area localization in pediatric hand radiographs using deep neural networks for object detection

BACKGROUND

Detection of ossification areas of hand bones in X-ray images is an important task, e.g. as a preprocessing step in automated bone age estimation. Deep neural networks have emerged recently as de facto standard detection methods, but their drawback is the need of large annotated datasets. Finetuning pre-trained networks is a viable alternative, but it is not clear a priori if training with small annotated datasets will be successful, as it depends on the problem at hand. In this paper, we show that pre-trained networks can be utilized to produce an effective detector of ossification areas in pediatric X-ray images of hands.

METHODS AND FINDINGS

A publicly available Faster R-CNN network, pre-trained on the COCO dataset, was utilized and finetuned with 240 manually annotated radiographs from the RSNA Pediatric Bone Age Challenge, which comprises over 14.000 pediatric radiographs. The validation is done on another 89 radiographs from the dataset and the performance is measured by Intersection-over-Union (IoU). To understand the effect of the data size on the pre-trained network, subsampling was applied to the training data and the training was repeated. Additionally, the network was trained from scratch without any pre-trained weights. Finally, to understand whether the trained model could be useful, we compared the inference of the network to an annotation of an expert radiologist. The finetuned network was able to achieve an average precision (mAP@0.5IoU) of 92.92 ± 1.93. Apart from the wrist region, all ossification areas were able to benefit from more data. In contrast, the network trained from scratch was not able to produce any correct results. When compared to the annotations of the expert radiologist, the network was able to localize the regions quite well, as the F1-Score was on average 91.85 ± 1.06.

CONCLUSIONS

By finetuning a pre-trained deep neural network, with 240 annotated radiographs, we were able to successfully detect ossification areas in prediatric hand radiographs.

A Deep Automated Skeletal Bone Age Assessment Model with Heterogeneous Features Learning

Skeletal bone age assessment is a widely used standard procedure in both disease detection and growth prediction for children in endocrinology. Conventional manual assessment methods mainly rely on personal experience in observing X-ray images of left hand and wrist to calculate bone age, which show some intrinsic limitations from low efficiency to unstable accuracy. To address these problems, some automated methods based on image processing or machine learning have been proposed, while their performances are not satisfying enough yet in assessment accuracy. Motivated by the remarkable success of deep learning (DL) techniques in the fields of image classification and speech recognition, we develop a deep automated skeletal bone age assessment model based on convolutional neural networks (CNNs) and support vector regression (SVR) using multiple kernel learning (MKL) algorithm to process heterogeneous features in this paper. This deep framework has been constructed, not only exploring the X-ray images of hand and twist but also some other heterogeneous information like race and gender. The experiment results prove its better performance with higher bone age assessment accuracy on two different data sets compared with the state of the art, indicating that the fused heterogeneous features provide a better description of the degree of bones' maturation.

Automatic Age Estimation and Majority Age Classification From Multi-Factorial MRI Data

Age estimation from radiologic data is an important topic both in clinical medicine as well as in forensic applications, where it is used to assess unknown chronological age or to discriminate minors from adults. In this paper, we propose an automatic multi-factorial age estimation method based on MRI data of hand, clavicle, and teeth to extend the maximal age range from up to 19 years, as commonly used for age assessment based on hand bones, to up to 25 years, when combined with clavicle bones and wisdom teeth. Fusing age-relevant information from all three anatomical sites, our method utilizes a deep convolutional neural network that is trained on a dataset of 322 subjects in the age range between 13 and 25 years, to achieve a mean absolute prediction error in regressing chronological age of 1.01±0.74 years. Furthermore, when used for majority age classification, we show that a classifier derived from thresholding our regression-based predictor is better suited than a classifier directly trained with a classification loss, especially when taking into account that those cases of minors being wrongly classified as adults need to be minimized. In conclusion, we overcome the limitations of the multi-factorial methods currently used in forensic practice, i.e., dependence on ionizing radiation, subjectivity in quantifying age-relevant information, and lack of an established approach to fuse this information from individual anatomical sites.

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.

MABAL: a Novel Deep-Learning Architecture for Machine-Assisted Bone Age Labeling

Bone age assessment (BAA) is a commonly performed diagnostic study in pediatric radiology to assess skeletal maturity. The most commonly utilized method for assessment of BAA is the Greulich and Pyle method (Pediatr Radiol 46.9:1269-1274, 2016; Arch Dis Child 81.2:172-173, 1999) atlas. The evaluation of BAA can be a tedious and time-consuming process for the radiologist. As such, several computer-assisted detection/diagnosis (CAD) methods have been proposed for automation of BAA. Classical CAD tools have traditionally relied on hard-coded algorithmic features for BAA which suffer from a variety of drawbacks. Recently, the advent and proliferation of convolutional neural networks (CNNs) has shown promise in a variety of medical imaging applications. There have been at least two published applications of using deep learning for evaluation of bone age (Med Image Anal 36:41-51, 2017; JDI 1-5, 2017). However, current implementations are limited by a combination of both architecture design and relatively small datasets. The purpose of this study is to demonstrate the benefits of a customized neural network algorithm carefully calibrated to the evaluation of bone age utilizing a relatively large institutional dataset. In doing so, this study will aim to show that advanced architectures can be successfully trained from scratch in the medical imaging domain and can generate results that outperform any existing proposed algorithm. The training data consisted of 10,289 images of different skeletal age examinations, 8909 from the hospital Picture Archiving and Communication System at our institution and 1383 from the public Digital Hand Atlas Database. The data was separated into four cohorts, one each for male and female children above the age of 8, and one each for male and female children below the age of 10. The testing set consisted of 20 radiographs of each 1-year-age cohort from 0 to 1 years to 14-15+ years, half male and half female. The testing set included left-hand radiographs done for bone age assessment, trauma evaluation without significant findings, and skeletal surveys. A 14 hidden layer-customized neural network was designed for this study. The network included several state of the art techniques including residual-style connections, inception layers, and spatial transformer layers. Data augmentation was applied to the network inputs to prevent overfitting. A linear regression output was utilized. Mean square error was used as the network loss function and mean absolute error (MAE) was utilized as the primary performance metric. MAE accuracies on the validation and test sets for young females were 0.654 and 0.561 respectively. For older females, validation and test accuracies were 0.662 and 0.497 respectively. For young males, validation and test accuracies were 0.649 and 0.585 respectively. Finally, for older males, validation and test set accuracies were 0.581 and 0.501 respectively. The female cohorts were trained for 900 epochs each and the male cohorts were trained for 600 epochs. An eightfold cross-validation set was employed for hyperparameter tuning. Test error was obtained after training on a full data set with the selected hyperparameters. Using our proposed customized neural network architecture on our large available data, we achieved an aggregate validation and test set mean absolute errors of 0.637 and 0.536 respectively. To date, this is the best published performance on utilizing deep learning for bone age assessment. Our results support our initial hypothesis that customized, purpose-built neural networks provide improved performance over networks derived from pre-trained imaging data sets. We build on that initial work by showing that the addition of state-of-the-art techniques such as residual connections and inception architecture further improves prediction accuracy. This is important because the current assumption for use of residual and/or inception architectures is that a large pre-trained network is required for successful implementation given the relatively small datasets in medical imaging. Instead we show that a small, customized architecture incorporating advanced CNN strategies can indeed be trained from scratch, yielding significant improvements in algorithm accuracy. It should be noted that for all four cohorts, testing error outperformed validation error. One reason for this is that our ground truth for our test set was obtained by averaging two pediatric radiologist reads compared to our training data for which only a single read was used. This suggests that despite relatively noisy training data, the algorithm could successfully model the variation between observers and generate estimates that are close to the expected ground truth.

Quantification of osteoblastic activity in epiphyseal growth plates by quantitative bone SPECT/CT

OBJECTIVE

Quantifying the function of the epiphyseal plate is worthwhile for the management of children with growth disorders. The aim of this retrospective study was to quantify the osteoblastic activity at the epiphyseal plate using the quantitative bone SPECT/CT.

MATERIALS AND METHODS

We enrolled patients under the age of 20 years who received Tc-99m hydroxymethylene diphosphonate bone scintigraphy acquired by a quantitative SPECT/CT scanner. The images were reconstructed by ordered subset conjugate-gradient minimizer, and the uptake on the distal margin of the femur was quantified by peak standardized uptake value (SUVpeak). A public database of standard body height was used to calculate growth velocities (cm/year).

RESULTS

Fifteen patients (6.9-19.7 years, 9 female, 6 male) were enrolled and a total of 25 legs were analyzed. SUVpeak in the epiphyseal plate was 18.9 ± 2.4 (average ± standard deviation) in the subjects under 15 years and decreased gradually by aging. The SUVpeak correlated significantly with the age- and sex-matched growth velocity obtained from the database (R² = 0.83, p < 0.0001).

CONCLUSION

The SUV measured by quantitative bone SPECT/CT was increased at the epiphyseal plates of children under the age of 15 years in comparison with the older group, corresponding to higher osteoblastic activity. Moreover, this study suggested a correlation between growth velocity and the SUV. Although this is a small retrospective pilot study, the objective and quantitative values measured by the quantitative bone SPECT/CT has the potential to improve the management of children with growth disorder.

An automated technique to stage lower third molar development on panoramic radiographs for age estimation: a pilot study

BACKGROUND

Automated methods to evaluate growth of hand and wrist bones on radiographs and magnetic resonance imaging have been developed. They can be applied to estimate age in children and subadults. Automated methods require the software to (1) recognise the region of interest in the image(s), (2) evaluate the degree of development and (3) correlate this to the age of the subject based on a reference population. For age estimation based on third molars an automated method for step (1) has been presented for 3D magnetic resonance imaging and is currently being optimised (Unterpirker et al. 2015).

AIM

To develop an automated method for step (2) based on lower third molars on panoramic radiographs.

MATERIALS AND METHODS

A modified Demirjian staging technique including ten developmental stages was developed. Twenty panoramic radiographs per stage per gender were retrospectively selected for FDI element 38. Two observers decided in consensus about the stages. When necessary, a third observer acted as a referee to establish the reference stage for the considered third molar. This set of radiographs was used as training data for machine learning algorithms for automated staging. First, image contrast settings were optimised to evaluate the third molar of interest and a rectangular bounding box was placed around it in a standardised way using Adobe Photoshop CC 2017 software. This bounding box indicated the region of interest for the next step. Second, several machine learning algorithms available in MATLAB R2017a software were applied for automated stage recognition. Third, the classification performance was evaluated in a 5-fold cross-validation scenario, using different validation metrics (accuracy, Rank-N recognition rate, mean absolute difference, linear kappa coefficient).

RESULTS

Transfer Learning as a type of Deep Learning Convolutional Neural Network approach outperformed all other tested approaches. Mean accuracy equalled 0.51, mean absolute difference was 0.6 stages and mean linearly weighted kappa was 0.82.

CONCLUSION

The overall performance of the presented automated pilot technique to stage lower third molar development on panoramic radiographs was similar to staging by human observers. It will be further optimised in future research, since it represents a necessary step to achieve a fully automated dental age estimation method, which to date is not available.

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.

GnRH receptor gene mutations in adolescents and young adults presenting with signs of partial gonadotropin deficiency

Biallelic, partial loss-of-function mutations in GNRHR cause a wide spectrum of reproductive phenotypes from constitutional delay of growth and puberty to complete congenital hypogonadotropic hypogonadism. We studied the frequency of GNRHR, FGFR1, TAC3, and TACR3 mutations in nine adolescent and young adult females with clinical cues consistent with partial gonadotropin deficiency (stalled puberty, unexplained secondary amenorrhea), and describe phenotypic features and molecular genetic findings of monozygotic twin brothers with stalled puberty.

Two girls out of nine (22%, 95%CI 6–55%) carried biallelic mutations in GNRHR. The girl with compound heterozygous c.317A>G p.(Gln106Arg) and c.924_926delCTT p.(Phe309del) GNRHR mutations displayed incomplete puberty and clinical signs of hypoestrogenism. The patient carrying a homozygous c.785G>A p.(Arg262Gln) mutation presented with signs of hypoestrogenism and unexplained secondary amenorrhea. None of the patients exhibited mutations in FGFR1, TAC3, or TACR3. The twin brothers, compound heterozygous for GNRHR mutations c.317A>G p.(Gln106Arg) and c.785G>A p.(Arg262Gln), presented with stalled puberty and were discordant for weight, and the heavier of them had lower testosterone levels.

These results suggest that genetic testing of the GNRHR gene should be offered to adolescent females with low-normal gonadotropins and unexplained stalled puberty or menstrual dysfunction. In male patients with partial gonadotropin deficiency, excess adipose tissue may suppress hypothalamic-pituitary-gonadal axis.

Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs

Purpose To compare the performance of a deep-learning bone age assessment model based on hand radiographs with that of expert radiologists and that of existing automated models. Materials and Methods The institutional review board approved the study. A total of 14 036 clinical hand radiographs and corresponding reports were obtained from two children's hospitals to train and validate the model. For the first test set, composed of 200 examinations, the mean of bone age estimates from the clinical report and three additional human reviewers was used as the reference standard. Overall model performance was assessed by comparing the root mean square (RMS) and mean absolute difference (MAD) between the model estimates and the reference standard bone ages. Ninety-five percent limits of agreement were calculated in a pairwise fashion for all reviewers and the model. The RMS of a second test set composed of 913 examinations from the publicly available Digital Hand Atlas was compared with published reports of an existing automated model. Results The mean difference between bone age estimates of the model and of the reviewers was 0 years, with a mean RMS and MAD of 0.63 and 0.50 years, respectively. The estimates of the model, the clinical report, and the three reviewers were within the 95% limits of agreement. RMS for the Digital Hand Atlas data set was 0.73 years, compared with 0.61 years of a previously reported model. Conclusion A deep-learning convolutional neural network model can estimate skeletal maturity with accuracy similar to that of an expert radiologist and to that of existing automated models. ^© RSNA, 2017 An earlier incorrect version of this article appeared online. This article was corrected on January 19, 2018.

Fourier-based quantification of renal glomeruli size using Hough transform and shape descriptors

BACKGROUND AND OBJECTIVE

Analysis of glomeruli geometry is important in histopathological evaluation of renal microscopic images. Due to the shape and size disparity of even glomeruli of same kidney, automatic detection of these renal objects is not an easy task. Although manual measurements are time consuming and at times are not very accurate, it is commonly used in medical centers. In this paper, a new method based on Fourier transform following usage of some shape descriptors is proposed to detect these objects and their geometrical parameters.

METHODS

Reaching the goal, a database of 400 regions are selected randomly. 200 regions of which are part of glomeruli and the other 200 regions are not belong to renal corpuscles. ROC curve is used to decide which descriptor could classify two groups better. f_measure, which is a combination of both tpr (true positive rate) and fpr (false positive rate), is also proposed to select optimal threshold for descriptors. Combination of three parameters (solidity, eccentricity, and also mean squared error of fitted ellipse) provided better result in terms of f_measure to distinguish desired regions. Then, Fourier transform of outer edges is calculated to form a complete curve out of separated region(s).

RESULTS

The generality of proposed model is verified by use of cross validation method, which resulted tpr of 94%, and fpr of 5%. Calculation of glomerulus' and Bowman's space with use of the algorithm are also compared with a non-automatic measurement done by a renal pathologist, and errors of 5.9%, 5.4%, and 6.26% are resulted in calculation of Capsule area, Bowman space, and glomeruli area, respectively.

CONCLUSIONS

Having tested different glomeruli with various shapes, the experimental consequences show robustness and reliability of our method. Therefore, it could be used to illustrate renal diseases and glomerular disorders by measuring the morphological changes accurately and expeditiously.

Managing the Pediatric Spine: Growth Assessment

Management of pediatric spinal deformities requires an accurate prediction of growth spurts to allow for timely initiation of treatment and prevention of curve progression. Determining remaining growth potential is also important for avoiding prolonged unnecessary treatment, e.g. bracing for patients nearing skeletal maturity. Many clinical and radiological growth parameters have been developed to aid clinicians in growth prediction. Of these, several commonly used measures such as height and arm span growth trends, timing of menarche, and the Risser sign are mostly retrospective and lack strong predictive utility. Bone age assessments, such as digital skeletal age and the distal radius and ulna classification, are more accurate parameters, but further research is required to determine interethnic variations and develop their role in management decisions.

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.

Fully Automated Deep Learning System for Bone Age Assessment

Skeletal maturity progresses through discrete phases, a fact that is used routinely in pediatrics where bone age assessments (BAAs) are compared to chronological age in the evaluation of endocrine and metabolic disorders. While central to many disease evaluations, little has changed to improve the tedious process since its introduction in 1950. In this study, we propose a fully automated deep learning pipeline to segment a region of interest, standardize and preprocess input radiographs, and perform BAA. Our models use an ImageNet pretrained, fine-tuned convolutional neural network (CNN) to achieve 57.32 and 61.40% accuracies for the female and male cohorts on our held-out test images. Female test radiographs were assigned a BAA within 1 year 90.39% and within 2 years 98.11% of the time. Male test radiographs were assigned 94.18% within 1 year and 99.00% within 2 years. Using the input occlusion method, attention maps were created which reveal what features the trained model uses to perform BAA. These correspond to what human experts look at when manually performing BAA. Finally, the fully automated BAA system was deployed in the clinical environment as a decision supporting system for more accurate and efficient BAAs at much faster interpretation time (<2 s) than the conventional method.

Determinants of Advanced Bone Age in Childhood Obesity

BACKGROUND

Childhood obesity is associated with advanced bone age (BA). Previous studies suggest that androgens, oestrogens, sex hormone-binding globulin, and insulin are responsible for this phenomenon, but results are contradictory and might be biased by confounders. We aim to elucidate this matter by applying a multivariate approach.

METHOD

We performed a correlation analysis of BA standard deviation score (SDS) with age- and sex-specific SDS for androgens, oestrogens, and with indicators of insulin secretion derived from oral glucose tolerance testing, in a group of obese children. A multivariate analysis was performed to investigate which parameters were independently predictive of BA SDS.

RESULTS

In this cohort (n = 101; mean age 10.9 years; mean BA 11.8 years; mean BMI SDS 3.3), BMI SDS was significantly correlated to BA SDS (r = 0.55, p < 0.001). In a regression analysis in the total cohort (B = 0.27, p < 0.001) as well as in females (B = 0.34, p = 0.042), males (B = 0.31, p = 0.006), and pubertal children (B = 0.32, p = 0.046), dehydroepiandrosterone sulphate (DHEAS) showed a positive, independent association with BA SDS. No association with indicators of insulin secretion was found.

CONCLUSION

BMI SDS is highly correlated to BA SDS in obese children. Increased DHEAS has a central role in advanced BA in obese children.
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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.