Bone Age Assessment Method Research Articles

Study on the Influence of Sports Factors on the Growth and Development of Athletes' Height

In this study, literature, expert interviews, mathematical statistics and bone age assessment methods were used to study 2,347 athletes participating in the 2017 youth sports competitions in Jiangxi province. The results show that sports training has a significant effect on the bone age of young athletes, and precocity of bones is easy to occur. The effect was not significant in early childhood, but significant in adolescence.

Radiographs and texts fusion learning based deep networks for skeletal bone age assessment

Bone age assessment is a pediatric examination that determines the difference between skeletal age and chronological age. The discrepancy between the two ages will often trigger the likelihood of genetic disorders, hormonal complications and abnormalities of maturity in the skeletal system. Recently, although some automated bone age assessment methods by analyzing radiographs have been researched, the available text data from radiological reports are not used. Texts and radiographs are two different modals, the fusion of them can give us much more information for bone age assessment. In this paper, we present a novel multi-modal data fusion-learning network, called RT-FuseNet, for bone age assessment utilizing radiographs and texts. Specifically, we develop a convolutional neural network with spatial pyramid pooling layer and attention mechanism module to ensure the integrity of the image space information and enhance the subtle difference of features among radiographs respectively. In addition, texts are incorporated into the learning model to jointly learn non-linear correlations between various heterogeneous data. To evaluate the proposed approach, two datasets are used and several neural network structures are compared. Experimental results show that the proposed approach performs well.

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.

Applicability of two bone age assessment methods to children from Saudi Arabia

To assess the applicability of the Greulich & Pyle (G&P) and Tanner & Whitehouse (TW3) methods to children from Saudi Arabia using both subjective (manual) rating and BoneXpert software. Bone age (BA) was assessed using the G&P and TW3 methods, firstly by independent manual rating of two observers, followed by a single observer using the BoneXpert software program. In total, 420 hand trauma radiographs for Saudi Arabians (220 males, 329 left, age range 1-18 years) performed in the period January 2012 to September 2016 were assessed. Paired sample t test was used to compare the difference between mean BA and mean chronological age (CA) and to compare the difference between manual and BoneXpert ratings. Statistical analysis was undertaken using SPSS v.25. A statistically significant difference was found between BA and CA in males when using the G&P (mean difference -0.36±1 years, p<0.01) and TW3 (mean difference -0.22±0.9 years, p=0.03) methods, but not in females for either G&P (mean difference 0.13±1.2 years) or TW3 (mean difference 0.08±1.1 years). In males, BoneXpert results conformed to the manual ratings for TW3, but not for G&P, for which the mean difference between manual and BoneXpert ratings was -0.27±0.5 years (p<0.01). The present results indicate that manual and BoneXpert-derived G&P and TW3 bone age assessment can be applied with no modification to Saudi Arabian females; however, only TW3 BoneXpert-derived BA can be applied without caution to Saudi Arabian males.

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

ObjectivesTo 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.MethodsBoneXpert 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).ResultsCA 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.ConclusionNo 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.

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

BACKGROUNDThe 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.OBJECTIVEThe 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.METHODA 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.RESULTS26 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.CONCLUSIONSThere 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.

Bone age - alternatives for skeletal maturity assessment for the EOS scanner

Hand and wrist bone age assessment methods cannot be performed when using the recommended patient position within the EOS scanner. We aimed to assess alternative methods for use with the EOS. After investigating 9 alternatives, five methods were selected - cervical vertebra (Hassel-Farman), iliac crest (Risser 'plus'), hip (Oxford), knee (O'Connor), calcaneus (Nicholson) - and applied to EOS scans of 114, 2-21-year-old normal individuals. Intraclass correlation coefficient tests for reliability and Spearman correlation with calendar age were assessed. Intra- and interobserver reliabilities were all excellent, except with the knee method (0.865 - 'good'). Calcaneal and cervical methods were the fastest to apply (mean 17.5 s, 33.4 s per evaluation), however, calcanei were unassessable in 14% of scans (versus 1% of cervical). All methods correlated significantly with calendar age (r>0.829, p<0.05). Difficulties were principally absent (12%) or obscured (23%) landmarks. Bone age assessment is possible with all 5 methods, however, the Hassel-Farman method proved to be easily useable, fast and reliable. Orv Hetil. 2019; 160(16): 619-628.

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.

A deep automated skeletal bone age assessment model via region-based convolutional neural network

Abstract Skeletal bone age assessment is widely applied in growth prediction and auxiliary diagnosis of medical problems. X-ray images of hands are observed in the evaluation of bone age, where the ossification centers of epiphysis and carpal bones are the key regions. Traditional skeletal bone age assessment methods extract these areas to predict the bone age but few of them can achieve satisfactory efficiency or accuracy. While automatic bone age assessment methods with deep learning techniques have achieved the leading performance, most of them can only accept fixed-size small images and ignore these key regions. In this paper, we take full consideration of the significant regions and propose a novel deep automated skeletal bone age assessment model via region-based convolutional neural network (R-CNN). We transfer Faster Region-based Convolutional Neural Network (Faster R-CNN) model from object detection to bone age regression in order to detect the ossification centers of epiphysis and carpal bones and evaluate bone age. The proposed model has overcome the limitation of other CNN based models, taking large-scale original X-ray images as inputs. It can automatically extract the features, detect the key regions and further predict the bone age. To validate the effectiveness of the proposed model, we realized different prior methods and conducted a series of experiments on two data sets using 10-fold cross-validation to compute the Mean absolute errors (MAEs). The results show that the MAEs of the proposed model are 0.51 and 0.48 years old respectively, better than other bone age assessment methods including state of the art.

Application of Deep learning in Bone age assessment

Bone age assessment is a common method for evaluating the growth and development of children and adolescents. It can be used for diagnosing problems such as rickets and short stature during the growth of adolescents. The traditional bone age assessment method is the doctor’s manual treatment of hand bone X-ray images, and comparing with medical standard pictures to achieve bone age assessment. In order to reduce the workload of doctors in identifying X-ray images and the subjective effect of doctors, the method is proposed by combining deep learning with medical images to implement automatic bone age assessment. Different methods are used to segment the hand bone X-ray images and transfer learning for classification and identification of bone age. The test results show that female bone age test precision were assigned 94.4% within 20 months, male test precision were assigned 90.5% within 20 months.

Regression Convolutional Neural Network for Automated Pediatric Bone Age Assessment From Hand Radiograph

Skeletal bone age assessment is a common clinical practice to investigate endocrinology, and genetic and growth disorders of children. However, clinical interpretation and bone age analyses are time-consuming, labor intensive, and often subject to inter-observer variability. This advocates the need of a fully automated method for bone age assessment. We propose a regression convolutional neural network (CNN) to automatically assess the pediatric bone age from hand radiograph. Our network is specifically trained to place more attention to those bone age related regions in the X-ray images. Specifically, we first adopt the attention module to process all images and generate the coarse/fine attention maps as inputs for the regression network. Then, the regression CNN follows the supervision of the dynamic attention loss during training; thus, it can estimate the bone age of the hard (or "outlier") images more accurately. The experimental results show that our method achieves an average discrepancy of 5.2-5.3 months between clinical and automatic bone age evaluations on two large datasets. In conclusion, we propose a fully automated deep learning solution to process X-ray images of the hand for bone age assessment, with the accuracy comparable to human experts but with much better efficiency.

A simple method for bone age assessment: the capitohamate planimetry

ObjectivesTo determine if the capitohamate (CH) planimetry could be a reliable indicator of bone age, and to compare it with Greulich-Pyle (GP) method.MethodsThis retrospective study included 391 children (age, 1–180 months). Two reviewers manually measured the areas of the capitate and hamate on plain radiographs. CH planimetry was defined as the measurement of the sum of areas of the capitate and hamate. Two reviewers independently applied the CH planimetry and GP methods in 109 children whose heights were at the 50th percentile of the growth chart.ResultsThere was a strong positive correlation between chronological age and CH planimetry measurement (right, r = 0.9702; left, r = 0.9709). There was no significant difference in accuracy between CH planimetry (84.39–84.46 %) and the GP method (85.15–87.66 %) (p ≥ 0.0867). The interobserver reproducibility of CH planimetry (precision, 4.42 %; 95 % limits of agreement [LOA], −10.5 to 13.4 months) was greater than that of the GP method (precision, 8.45 %; LOA, −29.5 to 21.1 months).ConclusionsCH planimetry may be a reliable method for bone age assessment.Key Points• Bone age assessment is important in the work-up of paediatric endocrine disorders.• Radiography of the left hand is widely used to estimate bone age.• Capitatohamate planimetry is a reliable and reproducible method for assessing bone age.

A test of four innominate bone age assessment methods in a modern skeletal collection from Medellin, Colombia

Studying bone collections with known data has proven to be useful in assessing reliability and accuracy of biological profile reconstruction methods used in Forensic Anthropology. Thus, it is necessary to calibrate these methods to clarify issues such as population variability and accuracy of estimations for the elderly. This work considers observations of morphological features examined by four innominate bone age assessment methods: (1) Suchey–Brooks Pubic Symphysis, (2) Lovejoy Iliac Auricular Surface, (3) Buckberry and Chamberlain Iliac Auricular Surface, and (4) Rouge–Maillart Iliac Auricular Surface and Acetabulum. This study conducted a blind test of a sample of 277 individuals from two contemporary skeletal collections from Universal and San Pedro cemeteries in Medellin, for which known pre-mortem data support the statistical analysis of results obtained using the four age assessment methods. Results from every method show tendency to increase bias and inaccuracy in relation to age, but Buckberry–Chamberlain and Rougé–Maillart’s methods are the most precise for this particular Colombian population, where Buckberry–Chamberlain’s is the best for analysis of older individuals.

Dental and Skeletal Age Estimations in Lebanese Children: A Retrospective Cross-sectional Study.

Aims and Objectives:Assessing dental and bone ages is frequently required in a wide range of fields such as odontology, forensic science, as well as orthopedics. The aim of this study was to evaluate applicability of two methods of bone age assessment and two methods of dental age (DA) assessment for Lebanese children.Materials and Methods:Skeletal age (SA) of 260 orthodontic patients (124 males, 136 females divided into four groups each) was consecutively assessed using Greulich and Pyle and Fishman's SMI methods. DA was evaluated using both Demirjian's and Willem's methods. Mean age was 11.89 ± 1.38 years for males and 11.75 ± 1.58 years for females. Data were collected and statistically analyzed using the SPSS software (IBM SPSS Statistics, version 21, USA). The differences between estimated DA, estimated SA, and chronological age (CA) were compared by gender and age group.Results:Greulich and Pyle method showed nonsignificant difference with CA in male sample, while in both assessment methods, the difference between skeletal and CAs is significant in female sample. Results of Willem's method in the whole sample suggested a statistically nonsignificant difference, when compared to CA. Demirjian's method delivered higher mean value than Willem's assessment in both genders.Conclusions:Greulich and Pyle method is accurate for SA assessment in males and only in one group of females, while it significantly overestimates age in all other female groups. Willem's method is more suitable to assess DA in both genders. A strong correlation exists between both dental and skeletal assessment methods and CA.

Radiological Indicators of Bone Age Assessment in Cephalometric Images. Review

SummaryThe ability to assess bone age accurately is important and allows to diagnose the patient correctly and to plan orthodontic treatment appropriately. The aim of the work is to present views of different authors on the subject of using cephalometric images to determine bone age and its significance for conducting appropriate orthodontic treatment.Publications from the PubMed medical database were analyzed. Search criteria: bone age assessment, CVM method. Ultimately, 36 papers out of 1354 publications were selected.The research of many authors confirms the usefulness of various methods using cephalometric images to assess skeletal age. Currently, the CVM method devised by Baccetti et al. is the most frequently mentioned one in literature.It seems that bone age assessment methods based on evaluating the morphological structure of the cervical vertebrae in cephalometric images can clearly differentiate skeletal maturity in children regardless of their race or sex. Bearing in mind the constant technological progress in medicine and stomatology, bone age assessment methods need to be perfected in order to alleviate their impact on the patient as much as possible.

Bone age: assessment methods and clinical applications.

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

The Shorthand Bone Age Assessment

Radiographic assessment of skeletal age in pediatric patients is a common practice among orthopaedic surgeons. Current methods of assessment remain labor intensive and require special resources. This study sought to investigate a novel, abridged method of bone age assessment that may serve as a simpler and more efficient alternative to the current standard. A shorthand bone age (SBA) method developed at our institution was compared against the Greulich and Pyle method from which it was derived. Standard left hand bone age radiographs of 140 male and 120 female patients, previously assigned skeletal ages ranging from 12.5 to 16 years in males and 10 to 16 years in females by musculoskeletal radiologists using the Greulich and Pyle radiographic atlas, were read using the shorthand method by 3 attending pediatric orthopaedic surgeons and an orthopaedic surgery resident. The shorthand method utilizes a single, univariable criterion for each age, rather than a multivariable subjective comparison to a radiographic atlas. All reviewers were blinded to the original bone age determination. Interobserver reliability, intraobserver reliability, and agreement with the previous records utilizing the atlas were calculated using weighted κ. The SBA method readings demonstrated substantial agreement with readings by the Greulich and Pyle atlas, demonstrating weighted κ values ranging from 0.71 to 0.75. The SBA method also demonstrated substantial to almost perfect interobserver and intraobserver reliability, with values ranging from 0.77 to 0.87 and from 0.87 to 0.95, respectively. These results are comparable or superior to previous reports which investigate the validity and reliability of other skeletal age assessment tools. The SBA assessment tool offers a simple and efficient alternative to current methods. Diagnostic study, level III.

Evaluation and development of a bone age assessment method in patients with Down syndrome

The objective of this paper was to evaluate the applicability of the method developed by Caldas to measure the vertebral bone age of Brazilians suffering from Down syndrome. A database comprised of 57 case records of individuals with this syndrome, both male and female, with ages ranging between 5 and 18 years, was used for this purpose. These records had lateral cephalometric radiographs and radiographs of hand and wrist, all of which had been obtained on the same date. There were 48 other records of individuals who did not suffer from Down syndrome. The Tanner and Whitehouse (TW3) method was used to perform the hand and wrist radiographs for obtaining bone age. The Caldas method was employed on the lateral cephalometric radiographs in order to obtain the vertebral bone age. From the information acquired on bone age, vertebral bone age and chronological age, it could be concluded that there is a statistically significant difference between the three ages for both the male and the female control group and for the female Down syndrome group. Therefore, this method was employed only on male Down syndrome individuals. Based on the results, a formula was developed to obtain the bone age for Down syndrome individuals.

Bone age assessment using cephalometric photographs

SummaryBackground:The assessment of bone age comprises the basic element of orthodontic diagnostics as it enables the recognition of deviations from normal growth, determines the choice of treatment, helps determine the appropriate moment to begin treatment, establish prognosis and plan a retention strategy. In order to make an assessment of skeletal maturity possible in a single examination, radiological methods were adopted. The following characteristics are evaluated on a radiograph: the appearance, size and shape of ossification centers, the width and the shape of growth cartilage and the degree of fusion between diaphyses and epiphyses. In order to assess the maturity of bones, hand-wrist radiographs were introduced in the second decade of the 20th century. Bone age assessment of bone age could also be made based on an analysis of a morphological maturity of cervical vertebrae utilizing cephalometric radiographs.Objective:The objective of the study was to evaluate the correspondence between bone age assessments made from hand-wrist radiographs and those from cephalometric radiographs.Material/Methods:In order to fulfill the objectives, hand-wrist radiographs as well as cephalometric radiographs of 30 patients (15 girls and 15 boys) between 10 and 17 years of age were collected. Bone age of hand, wrist and cervical spine was assessed. Bone age on hand-wrist radiographs was evaluated using the Björk method, whereas cephalometric radiographs were analyzed by the Baccetti et al. method.Results:A strong and statistically highly significant (r=0.98; p<0.00001) Pearson’s correlation was found between bone age assessed from hand-wrist radiographs using Björk’s method and bone age assessed from cephalometric radiographs using the method by Baccetti et al.Conclusions:The analysis of cervical vertebrae in cephalometric radiographs appears to be the most desirable method of bone age assessment. Performing the analysis on routinely taken cephalograms eliminates the need for additional exposure to X-ray radiation and shortens the duration of examination.

Automated Bone Age Assessment: Motivation, Taxonomies, and Challenges

Bone age assessment (BAA) of unknown people is one of the most important topics in clinical procedure for evaluation of biological maturity of children. BAA is performed usually by comparing an X-ray of left hand wrist with an atlas of known sample bones. Recently, BAA has gained remarkable ground from academia and medicine. Manual methods of BAA are time-consuming and prone to observer variability. This is a motivation for developing automated methods of BAA. However, there is considerable research on the automated assessment, much of which are still in the experimental stage. This survey provides taxonomy of automated BAA approaches and discusses the challenges. Finally, we present suggestions for future research.