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Bone age is an important index objectively reflecting the growth and development level of children and adolescents, as well as predicting growth potential.It plays a key role in various complicated situations involving clinical medicine, forensic and sports science.The wrist and the extremitas sternalis of clavicle are the most common sites for bone age assessment.Several techniques are available to evaluate bone age, including X-ray plain radiography, ultrasound, CT and magnetic resonance imaging.The accuracy and efficiency of bone age assessment have been continuously improved from traditional manual assessment to automatic assessment.This paper mainly reviews the advances in the techniques and methods for bone age assessment of the wrist and the extremitas sternalis of clavicle.
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Objective To explore the feasibility and effectiveness of the artificial intelligence(AI)diagnostic model for bone age imaging in the radiology information system-picture archiving and communication system(RIS-PACS)environment operation.Methods The optimized bone age AI model was integrated into the RIS-PACS platform.The bone age imaging data of 88 038 patients aged 0-18 years old were automatically evaluated.The reference bone age was determined by the consensus of two experienced radi-ologists based on GP map,with an error of±1.0 year old.The success rate,accuracy,system compatibility,stability,and influen-cing factors of results were further analyzed.Results The time for bone age AI evaluation of each case did not exceed 3 seconds,and the success rate of automatic evaluation reached 100%.AI model of bone age and RIS-PACS in hospital could be well integrated.Accord-ing to the readings evaluated by pediatric radiologists based on GP maps,the accuracy rate was 93.05%for girls and 89.53%for boys,with a mean absolute error(MAE)of(0.42±0.54)years old for girls and(0.45±0.60)years old for boys,respectively.The AI model could run efficiency in the RIS-PACS,which significantly reduced the burden of radiologists.The factors that affect the accuracy of the model were image position,exposures,multiple images in a single sequence and hand deformity,etc.Conclusion The bone age imaging AI diagnostic model can be seamlessly embedded into RIS-PACS in hospital,achieving one click bone age imaging diagnosis.
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BACKGROUND:The application of miniscrew in adolescents is increasing day by day,but at present,there are few studies on bone mass in the external oblique line of the mandible in adolescents at home and abroad,and there is no systematic study on bone mass in the external oblique line of the mandible in adolescents in different growth and development periods. OBJECTIVE:To measure the bone mass in the external oblique line of the mandible in adolescents with different cervical vertebral bone ages using a cone-beam CT and to investigate the difference of bone mass in the external oblique line of the mandible in adolescents with different cervical vertebral bone ages and the correlation between bone mass in this area and the cervical vertebral bone age. METHODS:The cone-beam CT data of 105 adolescent patients before orthodontic treatment were collected and divided into CS3 group(n=24),CS4 group(n=26),CS5 group(n=29)and CS6 group(n=26)using the cervical vertebral maturation method.The adolescent mandibular buccal shelf was reconstructed by Mimics Medical 21.0 software.The width of buccal bone at 6 and 11 mm under the cemento-enamel junction and the bone height at 4 and 5 mm buccal to the cemento-enamel junction of right mandibular first and second molars were measured.The measured data were statistically analyzed.The measurement was made on four planes:plane 1 is the plane where the proximal mesial root of the mandibular right first molar is located;plane 2 is the plane where the distal mesial root of the mandibular right first molar is located;plane 3 is the plane where the proximal mesial root of the mandibular right second molar is located;and plane 4 is the plane where the distal mesial root of the mandibular right second molar is located. RESULTS AND CONCLUSION:In each group,the bone width on the buccal side of the external oblique line increased gradually from the first molar proximally to the second molar distally in adolescents,and the width of buccal bone at 6 and 11 mm under the cemento-enamel junction showed significant difference among different layers(P<0.05).The bone width of buccal bone at 11 mm under the cemento-enamel junction was greater than that at 6 mm.The bone height on the buccal side of the external oblique line increased gradually from the first molar proximally to the second molar distally in all four groups,and the bone height at 4 and 5 mm buccal to the cemento-enamel junction showed significant differences at different layers(P<0.05).The bone height at 4 mm buccal to the cemento-enamel junction was greater than that at 5 mm.On the fourth plane,the bone width at 11 mm buccal to the cemento-enamel junction was smaller in the CS3,CS4,and CS5 groups than in the CS6 group(P<0.05).On the third plane,the bone heights at 4 mm and 5 mm buccal to the cemento-enamel junction were smaller in the CS3 and CS4 groups than in the CS6 group(P<0.05).On the fourth plane,the bone height at 5 mm buccal to the cemento-enamel junction was smaller in the CS3 and CS4 groups than in the CS6 group(P<0.05).On the fourth plane,the bone height at 4 mm buccal to the cemento-enamel junction was smaller in the CS3 group than in the CS6 group(P<0.05).Spearman correlation analysis showed that there was no correlation between bone mass and the cervical vertebral bone age,except that there was a weak correlation between bone mass at some measurement sites and cervical vertebral bone age.To conclude,the bone mass in the external oblique area of the mandible in adolescents does not change significantly with the increase of cervical vertebral bone age.The buccal side of the mesial root and distal root of the mandibular second molar in the external oblique area of CS3-CS6 adolescents meets the requirement of bone mass for miniscrew implantation,which is a site available for miniscrew implantation.
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BACKGROUND:Traditional lateral cephalometric radiographs always suffer from some problems,such as magnification distortion,left and right overlap inconsistency and so on,while the cone-beam CT can truly display the three-dimensional structure of the craniofacial region.Performing three-dimensional reconstruction of cone-beam CT and then transforming the cone-beam CT in the selected area into the two-dimensional image can make the overlap between the left and right sides consistent and reduce the influence of surrounding tissue structures. OBJECTIVE:To explore the consistency of quantitative analysis of cervical vertebral bone age between two kinds of cone-beam CT transformed two-dimensional images with different integrated thicknesses and traditional lateral cephalometric radiographs. METHODS:The cone-beam CT and lateral cephalometric radiograph data of 118 adolescent orthodontic patients were collected.Firstly,the cone-beam CT image was reconstructed in 3D imaging software.After reconstruction,two types of cone-beam CT images with different integrated thicknesses were selected in the sagittal interface and transformed into two-dimensional images,which were named ICB-1 and ICB-2,respectively.The Zhibeiyun system was used to measure and calculate the angle between the concave base of the second cervical vertebra and the lower edge of the vertebral body(@2),the ratio of the third cervical spine to the posterior height(AH3/PH3),the ratio of height to width of the fourth cervical spine(H4/W4)in lateral cephalometric radiograph,ICB-1,ICB-2 and the cervical vertebral bone age.After an interval of two weeks,20 adolescents were randomly selected to repeat the above measurements.The intraclass correlation coefficient(ICC)method was used to evaluate the repeatability of the three images in measuring cervical bone age.Paired t-test was used to analyze the consistency of cervical bone age measurements between the three images.The Kappa test was used to analyze the consistency of cervical vertebral bone age staging assessment between the three images. RESULTS AND CONCLUSION:(1)ICC of AH3/PH3 in the lateral cephalometric radiograph group was<0.9,and the ICC of other measurement items in each group was>0.9.(2)Paired t-test results showed that there were statistical differences in AH3/PH3 and H4/W4 between the ICB-1 group and lateral cephalometric radiograph group and between the ICB-1 group and ICB-2 group(P<0.05),and there were no statistically significant differences in the other items between the three groups(P>0.05).(3)The Kappa test results showed that the Kappa coefficients of the two groups were all more than 0.8 according to the staging of cervical vertebral bone age in three groups(P<0.001).(4)It is indicated that the repeatability of ICB-1 and ICB-2 in the measurement of cervical vertebral bone age is better than that of lateral cephalometric radiographs.Lateral cephalometric radiographs,ICB-1 and ICB-2 have good consistency in the measurement of cervical vertebral bone age,but considering the integrity of cervical vertebra structure,ICB-2 is more suitable for quantitative analysis of cervical vertebral bone age than ICB-1.
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BACKGROUND:Artificial intelligence-assisted bone age assessment has become a research hotspot.Domestic and foreign studies have shown that the rapid development of artificial intelligence technology in the field of medical imaging provides the possibility of more accurate and rapid assessment of bone age. OBJECTIVE:To investigate the consistency between a domestically developed artificial intelligent Greulich-Pyle(GP)bone age assessment system and an expert manually assessed GP(expert GP),and to provide a basis for the feasibility of applying an artificial intelligent GP in clinical practice or in other fields. METHODS:Wrist radiographs were sampled from children and adolescents aged 6-15 years,of whom 672 were male and 650 were female.Bone age assessment of the same wrist radiograph was performed using the artificial intelligent GP and the expert GP.The accuracy of the artificial intelligent GP reading results was assessed by the absolute value of the difference.The consistency of the bone age results was assessed by Pearson correlation and Bland-Altamn distribution;and the consistency of the assessment was checked by Kappa test. RESULTS AND CONCLUSION:The absolute value of the difference(95%confidence interval)of the difference between artificial intelligent GP and expert GP for male and female was 0.39 years(0.37-0.41 years)and 0.32 years(0.29-0.34 years),respectively.The deviation of Bland-Altamn values for male and female was(-0.096±0.482)years and(0.014±0.415)years,respectively.The correlation results between artificial intelligent GP bone age and expert GP bone age for male and female were r=0.991 and r=0.992,respectively(P<0.000 1).The median difference between all age groups for male and female was within 0.5 years.Kappa test values were greater than 0.4 for both sexes at all ages except for the 9-year age group for male.Overall Kappa values were 0.603 and 0.659 for male and female respectively.To conclude,there is a high degree of consistency between the artificial intelligence and expert evaluation results of GP bone age values in children and adolescents aged 6-15 years.
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【Objective】 To analyze the measurement of body mass index (BMI) and body fat percentage in girls aged 6 to 8 years, in order to provide a scientific basis for the prevention and treatment of childhood obesity. 【Methods】 The medical examination data of 968 girls with bone age of 6 - 8 years who underwent obesity screening in General Hospital of Northern Quarter Command from January 2022 to July 2022 were retrospectively analyzed. BMI, body fat percentage, nutritional indicators, and obesity rate of girls with bone age of 6 - 8 years were analyzed.Multivariate Logistic regression analysis was used to identify the risk factors for obesity in girls with bone age of 6 - 8 years old. 【Results】 BMI, body fat percentage and visceral fat grade of girls aged 6 - 8 years followed an increasing trend with age: 6 years old 3times/week (OR=1.432, 95%CI: 1.172 - 1.749),frequency of sweet food >3times/week (OR=2.670, 95%CI:1.170 - 6.093), eating speed < 15min/meal (OR=1.366, 95%CI:1.108 - 1.685), outdoor activity frequency <30min/day (OR=2.083, 95%CI:1.162 - 3.736), and parents lack of knowledge or with limited understanding of nutrition (OR=1.721, 95%CI:1.129 - 2.623) were independent risk factors for obesity in girls aged 6 - 8 years old (P<0.05). 【Conclusion】 The obesity rate of girls with bone age of 6 - 8 years old is high and should be addressed through a scientific diet, reasonable exercise, and educating parents to improve their knowledge of nutrition.
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Objective:Based on the questionnaire, to analyze the current status of children′s bone age assessment in China, especially the application of artificial intelligence (AI)-assisted bone age assessment system in the clinic.Methods:This was a cross-sectional study. The questionnaire was adapted by ourselves through the literature method and expert interview method, and the whole volume included 22 questions, which were released in the form of WeChat applet questionnaire star to the physician groups of several associations and entrusted to the radiology and paediatricians with senior titles. The results of the different types of questions were summarised and analyzed, and the chi-square test was used to compare the count data.Results:A total of 450 valid questionnaires were collected from 162 medical institutions in 26 provinces and cities and autonomous regions, of which 232 (51.6%) were from 87 (53.7%) tertiary hospitals and 218 (48.4%) from 75 (46.3%) secondary hospitals. Of the respondents, 115 (25.6%) were senior, 137 (30.4%) middle and 198 (44.0%) junior. Child bone age measurement was performed at 75.9% (66/87) of tertiary care organizations and 26.7% (20/75) of secondary care organizations, and the difference was statistically significant ( χ2=39.10, P<0.001). Left wrist radiographs were predominantly used for bone age assessment (76.0%, 123/162), with 72.8% (118/162) of sites using the ATLAS method of assessment and 17.9% (29/162) using the scoring method. A total of 98.4% (443/450) of respondents agreed that AI technology should be used to assist in bone age assessment, but only 9.3% (15/162) of healthcare organizations used AI-assisted technology. Conclusion:At present, bone age assessment is widely used in medical institutions, but there are problems with non-standardized examination methods, inconsistent assessment standards, and imprecise assessment results. Expectations for AI technology-assisted diagnosis exist among a wide range of physicians, but there are fewer users.
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Bone age is a quantitative representation of the skeletal development pattern.X-ray imaging of the wrist with the Greulich-Pyle method is commonly used to assess bone age in clinic.In adolescent children, the sensitivity and specificity of the the Greulich-Pyle method are not sufficient because the bones of the wrist are already mature.In contrast, epiphyseal morphological changes in the knee joint throughout adolescence can provide information for the assessment of bone age in adolescent children, and the feasibility of knee joint bone age assessment has been verified.With the application of artificial intelligence (AI) in the medical field, the accuracy of AI interpretation of bone age is also recognized.One of the important uses of bone age assessment in adolescent children is to predict the remaining growth potential.Based on knee images, exploring the use of AI to build a model for predicting residual growth potential is a more meaningful research direction for clinical purposes.This paper reviews the anatomical characteristics of the knee joint, the application of knee joint imaging and the research progress of AI in bone age assessment.
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Bone age can objectively reflect the human body growth and accurately assess the physical development level.Bone age assessment plays an important role in the growth and development, disease diagnosis and the monitoring of therapeutic efficacy in children and adolescents.In recent years, the artificial intelligence technology has been developed continuously.Applying artificial intelligence technology is expected to realize the automatic assessment of bone age.At present, the artificial intelligence technology of bone age assessment is mainly based on the deep learning (DL) algorithm.Although there have been many research on DL and bone age assessment, most are still in the experimental stage.This study reviews the research and progress of artificial intelligence technology based on DL applied to bone age assessment, aiming to provide reference and research ideas for relevant staff.
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The growth and development issues of children and adolescents have received increasing attention in recent years, and bone age measurement is of great significance for the healthy development and disease diagnosis of children and adolescents.At present, traditional X-ray is still used as the main method for bone age assessment both domestically and internationally, and as the gold standard.The main methods for evaluating bone age by X-ray include the counting method, graph method, and scoring method.However, traditional X-ray bone age assessment methods have drawbacks, such as radiation and time-consuming.New bone age assessment methods include ultrasound, dual-energy X-ray absorptiometry(DXA), magnetic resonance imaging (MRI), and artificial intelligence.This article reviews the current research status and progress of various imaging methods for evaluating bone age, to provide a basis for selecting appropriate bone age imaging methods in clinical practice.
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Objective To use the deep learning methods to extract features of the 1st to 7th adult costal cartilage CT reconstruction images to realize the automatic estimation of adult costal cartilage bone age.Methods 625 male and 625 female samples aged between 20 and 70 years old were collected retrospectively,and the corresponding VRT images were reconstructed by volume rendering technology(VRT).After image preprocessing and data augmentation,500 cases were used as the training set and 125 cases as the test set.The performance of ResNet,ResNeXt,DenseNet and GoogleNet networks was evaluated by using 5-fold cross-validation,and the average value of 5-fold cross-validation results was taken as the final estimation result.Results The ResNet50 network achieved the best results in both male and female datasets.The mean absolute error was 4.56 years and 3.91 years,the accuracy rate was 64.00%and 70.88%in the range of±5.0 years,88.96%and 94.40%in the range of±10.0 years,respectively.Conclusion Compared with traditional methods and machine learning methods,the deep learning models can avoid the influence of human factors,greatly improve the accuracy of adult costal cartilage bone age estimation,and reduce the error between predicted age and real age,which has high clinical application value.
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Objective To explore the common parameters of cervical spine bone age of Uighur adolescents in Urumqi,derive the regression equation of cervical spine bone age of Uighur adolescents in Urumqi,and evaluate the significance of its application in prediction of biological age.Methods Part I:A total of 300 Uygur adolescent patients(150 males and 150 females)aged 7～16 years who were admitted to the orthodontic Department of Urumqi Stomatology Hospital from January 2017 to October 2020 were selected.Fixed point measurements were performed on the third and fourth cervical vertebrae(AH3,PH3,AP3,H3,AH4,PH4,AP4,H4).The corresponding ratios representing the morphological changes of cervical cone were obtained(AH3/AP3,AH3/H3,H3/AP3,H3/PH3,PH3/AP3,AH4/AP4,AH4/cervical bone age of Uygur teenagers in Urumqi city.Part II:A total of 192 Uygur adolescent patients(91 males and 101 females)aged 7～16 years old who were admitted to the orthodontic Department of Urumqi Stomatology Hospital from November 2020 to December 2022 were selected.The measured values of the third and fourth cervical vertebrae were substituted into the regression equation of adolescent cervical bone age obtained in part I to analyze the correlation between biological age and cervical bone age.Results Part I:The estimated age was obtained through the regression equation of cervical spine bone age of female and male adolescents proposed by Mito.The matching analysis results with biological age showed that the estimated age of cervical spine bone age of female and male adolescents was different from biological age,and the difference was statistically significant.Taking the actual age of the samples as the dependent variable and the measured ratios of the third and fourth cones as the independent variables,the calculation equation of cervical bone age of Uygur teenagers in Urumqi city was derived by using multiple stepwise regression analysis. Male:CVBA = 2.551-2.151×AH3/AP3 + 8.884×H4/AP4 + 3.198×H3/PH3,(R = 0.804); Female:CVBA = 3.607 + 2.743×H3/AP3 + 9.967×H4/AP4-3.135×H4/PH4,(R = 0.792). Part Ⅱ:By Spearman correlation analysis,male cervical spine bone age was highly correlated with biological age(r = 0.821,P<0.05).There was a high correlation between cervical spine bone age and biological age(r = 0.830,P<0.05).Conclusion The equation of cervical vertebra bone age of Uygur adolescents in Urumqi derived from lateral radiographs is helpful to predict individual growth and development potential in clinical work,and to guide clinical diagnosis and treatment.It can provide some reference for inferring the biological age of Uygur Adolescents in Forensic Science;However,it can not accurately reflect the biological age,and the accuracy still needs further improvement.
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Purpose: The assessment of bone age has applications in a wide variety of fields: from orthodontics to immigration. The traditional non?automated methods are time?consuming and subject to inter? and intra?observer variability. This is the first study of its kind done on the Indian population. In this study, we analyse different pre?processing techniques and architectures to determine the degree of maturation (i.e. cervical vertebral maturation [CVM]) from cephalometric radiographs using machine learning algorithms. Methods: Cephalometric radiographs—labelled with the correct CVM stage using Baccetti et al. method—from 383 individuals aged between 10 and 36 years were used in the study. Data expansion and in?place data augmentation were used to handle high data imbalances. Different pre?processing techniques like Sobel filters and canny edge detectors were employed. Several deep learning convolutional neural network (CNN) architectures along with numerous pre?trained models like ResNet?50 and VGG?19 were analysed for their efficacy on the dataset. Results: Models with 6 and 8 convolutional layers trained on 64 × 64–size grayscale images trained the fastest and achieved the highest accuracy of 94%. Pre?trained ResNet?50 with the first 49 layers frozen and VGG?19 with 10 layers frozen to training had remarkable performances on the dataset with accuracies of 91% and 89%, respectively. Conclusions: Custom deep CNN models with 6–8 layers on 64 × 64–sized greyscale images were successfully used to achieve high accuracies to classify the majority classes. This study is a launchpad in the development of an automated method for bone age assessment from lateral cephalograms for clinical purposes.
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Objectives To validate adult height predictions (BX) using automated and Greulich–Pyle bone age determinations in children with chronic endocrine diseases. Methods Heights and near-adult heights were measured in 82 patients (48 females) with chronic endocrinopathies at the age of 10.45±2.12 y and at time of transition to adult care (17.98±3.02 y). Further, bone age (BA) was assessed using the conventional Greulich–Pyle (GP) method by three experts, and by BoneXpert™. PAH were calculated using conventional BP tables and BoneXpert™. Results The conventional and the automated BA determinations revealed a mean diference of 0.25±0.72 y (p=0.0027). The automated PAH by BoneXpert™ were 156.26 ± 0.86 cm (SDS ? 2.01 ± 1.07) in females and 171.75 ± 1.6 cm (SDS ? 1.29 ± 1.06) in males, compared to 153.95 ± 1.12 cm (SDS ? 2.56 ± 1.5) in females and 169.31 ± 1.6 cm (SDS?1.66±1.56) in males by conventional BP, respectively and in comparison to near-adult heights 156.38±5.84 cm (SDS?1.91±1.15) in females and 168.94±8.18 cm (SDS?1.72±1.22) in males, respectively. Conclusion BA ratings and adult height predictions by BoneXpert™ in children with chronic endocrinopathies abolish rater-dependent variability and enhance reproducibility of estimates thereby refning care in growth disorders. Conventional methods may outperform automated analyses in specifc cases.
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Objective To study the correlation of bone age and bone mineral density with age, height and weight of short children. Methods Sixty-four short children who were consulted and treated at the author's hospital from January 2016 to October 2018 were selected as research subjects. The general information including age, sex, height and weight of the children were recorded. The ultrasound bone density test was carried out at the same time. The bone mineral density and bone age were evaluated through plain carpal bone radiograph. The relationship between different bone age and bone mineral density value with age, height and weight was analyzed. Results The actual age of the enrolled children was positively correlated with bone mineral density and bone age (boys r=0.658, 0.919, girls r=0.641, 0.906). The height of the enrolled children was positively correlated with bone mineral density and bone age (boy r=0.561, 0.326, girls r=0.586, 0.349). The weight of the enrolled children was positively correlated with bone mineral density and bone age (boys r=0.340, 0.314, girls r=0.395, 0.282). Conclusion The bone age and bone mineral density of short children were positively correlated with their age, height and weight. In clinical diagnosis and treatment, the use of bone age and bone mineral density as a guide can produce more significant effects, which can be used as scientific indicators for the evaluation and prediction of short children.
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OBJECTIVE@#To explore the integration method and technical realization of artificial intelligence bone age assessment system with the hospital RIS-PACS network and workflow.@*METHODS@#Two sets of artificial intelligence based on bone age assessment systems (CHBoneAI 1.0/2.0) were developed. The intelligent system was further integrated with RIS-PACS based on the http protocol in Python flask web framework.@*RESULTS@#The two sets of systems were successfully integrated into the local network and RIS-PACS in hospital. The deployment has been smoothly running for nearly 3 years. Within the current network setting, it takes less than 3 s to complete bone age assessment for a single patient.@*CONCLUSIONS@#The artificial intelligence based bone age assessment system has been deployed in clinical RIS-PACS platform and the "running in parallel", which is marking a success of Stage-I and paving the way to Stage-II where the intelligent systems can evolve to become more powerful in particular of the system self-evolution and the "running alternatively".
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Humanos , Determinación de la Edad por el Esqueleto , Inteligencia Artificial , Huesos , Sistemas de Información en Hospital , Hospitales , Sistemas de Información Radiológica , Integración de SistemasRESUMEN
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Niño , Humanos , Índice de Masa Corporal , Insulina , Enfermedad del Hígado Graso no Alcohólico , Obesidad , Sobrepeso , Obesidad Infantil , Prevalencia , Circunferencia de la CinturaRESUMEN
BACKGROUND: In China, three bone age assessment methods have been widely used in the medical and sports fields, including the Greulich-Pyle atlas method (GP method), CHN scoring method (CHN method), and China 05 method. A large-sample empirical study is required to determine which method is more suitable for assessing bone age of children and adolescents. OBJECTIVE: To provide a scientific evidence for appropriate bone age evaluation standards for children and adolescents in the eastern developed areas, by comparing the GGP method, CHN method and China 05 method based on samples of healthy children from Shanghai. METHODS: A total of 4 152 healthy children and adolescents (2 185 boys and 1 967 girls) from the urban area of Shanghai were selected for the study. Their digital X-ray of the left hand and wrist were collected and evaluated by the GGP method, CHN method and China 05 method. The difference between the bone age and the chronological age was used to assess the applicability of different bone age standards. The study was approved by the Ethics Committee of Shanghai Research Institute of Sports Science, and informed consent was given by all parents of the enrolled students. RESULTS AND CONCLUSION: For the GP method, the difference between bone age and chronological age in both genders at the age of ≥ 8 years was-0.12 to-0.65 year with significant difference, except for 8-year-old girls. The significant age difference at the age of ≥ 9 years was 0.18 to 1.62 year, except for the 9-year-old age group. For the CHN method, the difference between bone age and chronological age among 6-17-year-old boys and 6-16-year-old girls was 0.42 to 1.56 years (P 0.05), and-0.60 in 18-year-old boys (P < 0.01); the age difference among 6-17-year-old girls was-0.01 to 0.56 year, and the difference was not significant in most age groups. Among the three methods, the result of China 05 method is relatively better, which is the best method that matches the current development of teenagers in Shanghai, suggesting that the China 05 method is more suitable for the eastern developed areas with economic level similar to Shanghai. All the three methods have some limitations. Considering the long-term growth trend of adolescents, it is necessary to revise the current bone age evaluation standards.
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@#AIM:To explore the relationship among bone age, age, height, weight and ocular biological parameters of myopic children in Whenzhou.<p>METHODS: Totally 410 cases(820 eyes)of myopic children with age distribution of 5-11 years old were collected. The height, weight, bone age and axial length(AL)of each child were measured, and body mass index(BMI)was calculated. Refractive was measured by medical optometry after rapid mydriasis, and the spherical equivalent(SE)was calculated. Children of each age group were divided into groups according to bone age difference(biological age-life age), and the prevalence of myopia in each group was statistically analyzed and compared among different bone age groups.<p>RESULTS: There was an association among the age, height, weight, BMI, SE and axis length(<i>r</i>s=0.853, 0.776, 0.291, -0.274 and 0.312; <i>P</i><0.05). There was no significant correlation between the age and the bone age(<i>rs</i>=0.045, <i>P</i>>0.05). 40.5% of myopic children are ahead of their bone age, 40.2% of them are in the normal range, 19.3% of them are behind(χ2=21.576, <i>P</i><0.05). Among the myopic children aged 5-11, 46.3% were boys and 53.7% were girls(χ2=17.322, <i>P</i><0.01). The proportion of girls was much higher than boys in children with advanced bone age.<p>CONCLUSION: There was an association between the age and height, weight, BMI, SE, axis length in Whenzhou. Among the children with low age myopia, there are more children with advanced bone age, especially girls.
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Objective To investigate the serum vitamin and trace element levels in children with short stature and their correlation with bone age. Methods Levels of serum VA and VD, and trace elements Ca, Fe, Zn, Mg, Cu, Pb and Cd were measured in 322 children who were referred for height consultation. Bone ages were evaluated and the correlation between bone age and serum vitamin and trace element levels was analyzed. Results The VA and VD deficiency rates of these 322 children were 22.05% and 34.16%, respectively. The deficiency rates of trace elements Ca, Fe and Zn were14.29%, 21.43% and 6.83%, respectively. The Pb excess rate was as high as 42.55%. The rates of bone age (BA) retardation in Group Ⅰ (short) and Group Ⅱ (slightly short) were 49.38% and 37.57%, respectively, which was significantly higher than that of Group Ⅲ (normal). The Ca level of BA retardation children was lower than that of the normal BA children in Group I. The VD level of BA retardation children was lower than that of the normal BA children in Group Ⅱ. BA was negatively correlated with VD, Ca, and Cu levels in children (r=-0.241; r=-0.136; r=-0.162), and positively correlated with Fe (r=0.286) . Conclusion There were significant abnormalities of vitamins and trace elements in short children. Children's bone age had a certain correlation with serum vitamin D, calcium, copper, and iron levels. Serum vitamin and trace element levels in children should be monitored to guide a reasonable diet to better promote child growth and development.