Olecranon bone age assessment in puberty using a lateral elbow radiograph and a deep-learning model.

OBJECTIVES: To improve pubertal bone age (BA) evaluation by developing a precise and practical elbow BA classification using the olecranon, and a deep-learning AI model. MATERIALS AND METHODS: Lateral elbow radiographs taken for BA evaluation in children under 18 years were collected from January 2020 to June 2022, retrospectively. A novel classification and the olecranon BA were established based on the morphological changes in the olecranon ossification process during puberty. The olecranon BA was compared with other elbow and hand BA methods, using intraclass correlation coefficients (ICCs), and a deep-learning AI model was developed. RESULTS: A total of 3508 lateral elbow radiographs (mean age 9.8 ± 1.8 years) were collected. The olecranon BA showed the highest applicability (100%) and interobserver agreement (ICC 0.993) among elbow BA methods. It showed excellent reliability with Sauvegrain (0.967 in girls, 0.969 in boys) and Dimeglio (0.978 in girls, 0.978 in boys) elbow BA methods, as well as Korean standard (KS) hand BA in boys (0.917), and good reliability with KS in girls (0.896) and Greulich-Pyle (GP)/Tanner-Whitehouse (TW)3 (0.835 in girls, 0.895 in boys) hand BA methods. The AI model for olecranon BA showed an accuracy of 0.96 and a specificity of 0.98 with EfficientDet-b4. External validation showed an accuracy of 0.86 and a specificity of 0.91. CONCLUSION: The olecranon BA evaluation for puberty, requiring only a lateral elbow radiograph, showed the highest applicability and interobserver agreement, and excellent reliability with other BA evaluation methods, along with a high performance of the AI model. CLINICAL RELEVANCE STATEMENT: This AI model uses a single lateral elbow radiograph to determine bone age for puberty from the olecranon ossification center and can improve pubertal bone age assessment with the highest applicability and excellent reliability compared to previous methods. KEY POINTS: Elbow bone age is valuable for pubertal bone age assessment, but conventional methods have limitations. Olecranon bone age and its AI model showed high performances for pubertal bone age assessment. Olecranon bone age system is practical and accurate while requiring only a single lateral elbow radiograph.

Bone age assessment based on different MRI modalities of the proximal humerus epiphysis: the comparisons of T1WI, T2WI, and PDWI.

Bone age assessment (BAA) is crucial in various fields, including legal proceedings, athletic competitions, and clinical medicine. However, the use of X-ray methods for age estimation without medical indication is subject to ethical debate, especially in forensic and athletic fields. The application of magnetic resonance imaging (MRI) with non-ionizing radiation can overcome this limitation in BAA. This study aimed to compare the application value of several MRI modalities of proximal humeral in BAA. A total of 468 patients with shoulder MRIs were retrospectively collected from a Chinese Han population aged 12-30 years (259 males and 209 females) for training and testing, including T1 weighted MRI (T1WI), T2 weighted MRI (T2WI), and Proton density weighted MRI (PDWI). Optimal regression models were established for age estimation, yielding mean absolute error (MAE) values below 2.0 years. The MAE values of T1WI were the lowest, with 1.700 years in males and 1.798 years in females. The area under the curve (AUC) and accuracy values of different MRI modalities of 16-year and 18-year thresholds were all around 0.9. For the 18-year threshold, T1WI outperformed T2WI and PDWI. In conclusion, the three MRI modalities of the proximal humerus can serve as reliable indicators for age assessment, while the T1WI performed better in age assessment and classification.

Radiological age assessment based on clavicle ossification in CT: enhanced accuracy through deep learning.

BACKGROUND: Radiological age assessment using reference studies is inherently limited in accuracy due to a finite number of assignable skeletal maturation stages. To overcome this limitation, we present a deep learning approach for continuous age assessment based on clavicle ossification in computed tomography (CT). METHODS: Thoracic CT scans were retrospectively collected from the picture archiving and communication system. Individuals aged 15.0 to 30.0 years examined in routine clinical practice were included. All scans were automatically cropped around the medial clavicular epiphyseal cartilages. A deep learning model was trained to predict a person's chronological age based on these scans. Performance was evaluated using mean absolute error (MAE). Model performance was compared to an optimistic human reader performance estimate for an established reference study method. RESULTS: The deep learning model was trained on 4,400 scans of 1,935 patients (training set: mean age = 24.2 years ± 4.0, 1132 female) and evaluated on 300 scans of 300 patients with a balanced age and sex distribution (test set: mean age = 22.5 years ± 4.4, 150 female). Model MAE was 1.65 years, and the highest absolute error was 6.40 years for females and 7.32 years for males. However, performance could be attributed to norm-variants or pathologic disorders. Human reader estimate MAE was 1.84 years and the highest absolute error was 3.40 years for females and 3.78 years for males. CONCLUSIONS: We present a deep learning approach for continuous age predictions using CT volumes highlighting the medial clavicular epiphyseal cartilage with performance comparable to the human reader estimate.

Adult age estimation from the sternum using maximum intensity projection images of CT and data mining in a Chinese population.

This study aimed to explore and develop data mining models for adult age estimation based on CT reconstruction images from the sternum. Maximum intensity projection (MIP) images of chest CT were retrospectively collected from a modern Chinese population, and data from 2700 patients (1349 males and 1351 females) aged 20 to 70 years were obtained. A staging technique within four indicators was applied. Several data mining models were established, and mean absolute error (MAE) was the primary comparison parameter. The intraobserver and interobserver agreement levels were good. Within internal validation, the optimal data mining model obtained the lowest MAE of 9.08 in males and 10.41 in females. For the external validation (N = 200), MAEs were 7.09 in males and 7.15 in females. In conclusion, the accuracy of our model for adult age estimation was among similar studies. MIP images of the sternum could be a potential age indicator. However, it should be combined with other indicators since the accuracy level is still unsatisfactory.

Clinical validation of a deep-learning-based bone age software in healthy Korean children.

PURPOSE: Bone age (BA) is needed to assess developmental status and growth disorders. We evaluated the clinical performance of a deep-learning-based BA software to estimate the chronological age (CA) of healthy Korean children. METHODS: This retrospective study included 371 healthy children (217 boys, 154 girls), aged between 4 and 17 years, who visited the Department of Pediatrics for health check-ups between January 2017 and December 2018. A total of 553 left-hand radiographs from 371 healthy Korean children were evaluated using a commercial deep-learning-based BA software (BoneAge, Vuno, Seoul, Korea). The clinical performance of the deep learning (DL) software was determined using the concordance rate and Bland-Altman analysis via comparison with the CA. RESULTS: A 2-sample t-test (P<0.001) and Fisher exact test (P=0.011) showed a significant difference between the normal CA and the BA estimated by the DL software. There was good correlation between the 2 variables (r=0.96, P<0.001); however, the root mean square error was 15.4 months. With a 12-month cutoff, the concordance rate was 58.8%. The Bland-Altman plot showed that the DL software tended to underestimate the BA compared with the CA, especially in children under the age of 8.3 years. CONCLUSION: The DL-based BA software showed a low concordance rate and a tendency to underestimate the BA in healthy Korean children.

Machine learning and deep learning enabled age estimation on medial clavicle CT images.

The medial clavicle epiphysis is a crucial indicator for bone age estimation (BAE) after hand maturation. This study aimed to develop machine learning (ML) and deep learning (DL) models for BAE based on medial clavicle CT images and evaluate the performance on normal and variant clavicles. This study retrospectively collected 1049 patients (mean± SD: 22.50±4.34 years) and split them into normal training and test sets, and variant training and test sets. An additional 53 variant clavicles were incorporated into the variant test set. The development stages of normal MCE were used to build a linear model and support vector machine (SVM) for BAE. The CT slices of MCE were automatically segmented and used to train DL models for automated BAE. Comparisons were performed by linear versus ML versus DL, and normal versus variant clavicles. Mean absolute error (MAE) and classification accuracy was the primary parameter of comparison. For BAE, the SVM had the best MAE of 1.73 years, followed by the commonly-used CNNs (1.77-1.93 years), the linear model (1.94 years), and the hybrid neural network CoAt Net (2.01 years). In DL models, SE Net 18 was the best-performing DL model with similar results to SVM in the normal test set and achieved an MAE of 2.08 years in the external variant test. For age classification, all the models exhibit superior performance in the classification of 18-, 20-, 21-, and 22-year thresholds with limited value in the 16-year threshold. Both ML and DL models produce desirable performance in BAE based on medial clavicle CT.

Automated bone age assessment from knee joint by integrating deep learning and MRI-based radiomics.

Bone age assessment (BAA) is a crucial task in clinical, forensic, and athletic fields. Since traditional age estimation methods are suffered from potential radiation damage, this study aimed to develop and evaluate a deep learning radiomics method based on multiparametric knee MRI for noninvasive and automatic BAA. This retrospective study enrolled 598 patients (age range,10.00-29.99 years) who underwent MR examinations of the knee joint (T1/T2*/PD-weighted imaging). Three-dimensional convolutional neural networks (3D CNNs) were trained to extract and fuse multimodal and multiscale MRI radiomic features for age estimation and compared to traditional machine learning models based on hand-crafted features. The age estimation error was greater in individuals aged 25-30 years; thus, this method may not be suitable for individuals over 25 years old. In the test set aged 10-25 years (n = 95), the 3D CNN (a fusion of T1WI, T2*WI, and PDWI) demonstrated the lowest mean absolute error of 1.32 ± 1.01 years, which is higher than that of other MRI modalities and the hand-crafted models. In the classification for 12-, 14-, 16-, and 18- year thresholds, accuracies and the areas under the ROC curves were all over 0.91 and 0.96, which is similar to the manual methods. Visualization of important features showed that 3D CNN estimated age by focusing on the epiphyseal plates. The deep learning radiomics method enables non-invasive and automated BAA from multimodal knee MR images. The use of 3D CNN and MRI-based radiomics has the potential to assist radiologists or medicolegists in age estimation.

Precision and Accuracy of Radiological Bone Age Assessment in Children among Different Ethnic Groups: A Systematic Review.

AIM: The aim was to identify, evaluate, and summarize the findings of relevant individual studies on the precision and accuracy of radiological BA assessment procedures among children from different ethnic groups. MATERIALS AND METHODS: A qualitative systematic review was carried out following the MOOSE statement and previously registered in PROSPERO (CRD42023449512). A search was performed in MEDLINE (PubMed) (n = 561), the Cochrane Library (n = 261), CINAHL (n = 103), Web of Science (WOS) (n = 181), and institutional repositories (n = 37) using MeSH and free terms combined with the Booleans "AND" and "OR". NOS and ROBINS-E were used to assess the methodological quality and the risk of bias of the included studies, respectively. RESULTS: A total of 51 articles (n = 20,100) on radiological BA assessment procedures were precise in terms of intra-observer and inter-observer reliability for all ethnic groups. In Caucasian and Hispanic children, the Greulich-Pyle Atlas (GPA) was accurate at all ages, but in youths, Tanner-Whitehouse radius-ulna-short bones 3 (TW3-RUS) could be an alternative. In Asian and Arab subjects, GPA and Tanner-Whitehouse 3 (TW3) overestimated the BA in adolescents near adulthood. In African youths, GPA overestimated the BA while TW3 was more accurate. CONCLUSION: GPA and TW3 radiological BA assessment procedures are both precise but their accuracy in estimating CA among children of different ethnic groups can be altered by racial bias.

A Importância dos Métodos de Determinação das Idades Esquelética e Dentária na Ortodontia e Odontopediatria ­ uma Revisão de Literatura / The Importance of Methods for Determination of Squeletal and Dental Age in Orthodontics and Pediatric Dentistry ­ a Literature Review

O estágio de desenvolvimento humano é intimamente relacionado à sua maturidade óssea ou dentária, sendo essencial para a escolha do tratamento de alterações dentofaciais em crianças e adolescentes por ortodontistas e odontopediatras. Existem diversos indicadores biológicos para determinar a maturação do indivíduo, como a idade cronológica e as alterações hormonais, porém esses indicadores podem sofrer interferências. Visando uma determinação de desenvolvimento e dos picos de crescimento mais precisa, para um melhor diagnóstico e plano de tratamento, foram desenvolvidos diversos métodos para determinar a idade esquelética e a idade dentária, sendo estes a avaliação da maturação carpal, da morfologia das vértebras cervicais, da fusão óssea da sincondrose esfeno-occipital e da sutura palatina mediana, bem como dos estágios da calcificação dentária. A avaliação das radiografias de mão e punho é o padrão ouro da predição da idade esquelética, e sua correlação com outros métodos já é evidente. Sendo assim, é possível utilizar a avaliação das vértebras cervicais e das idades dentárias de Nolla e Demirjian.

The stage of human development is closely related to bone or dental maturity, being essential for the choice of treatment for dentofacial changes in children and adolescents by orthodontists and pediatric dentists. There are several biological indicators to determine an individual's maturation, such as chronological age and hormonal changes, but these indicators can suffer interference. Aiming at a more accurate determination of development and growth peaks, for a better diagnosis and treatment plan, several methods have been developed to determine skeletal age and dental age, these being the assessment of carpal maturation, the morphology of the cervical vertebrae, bone fusion of the spheno-occipital synchondrosis and the median palatal suture, as well as the stages of dental calcification. The evaluation of hand and wrist radiographs is the gold standard for predicting skeletal age, and its correlation with other methods is already evident. Therefore, it is possible to use the assessment of cervical vertebrae and dental ages by Nolla and Demirjian.

Comparison of Dental and Skeletal Age Estimating Methods in Children.

Objectives: Estimating age is a crucial determinant of forensic science. Various methods have been used to estimate dental age (DA) and skeletal age (SA).The aim of the current study was to compare the Cameriere's DA method with the Cameriere's SA method in estimating CA in children. Materials and methods: A total of 216 radiographs of 130 females and 86 males (between 9 to 14.99 years of age) were evaluated in northwestern Turkey. DA was calculated on the panoramic images using Cameriere's open-apex method. SA was determined on the lateral cephalograms using the fourth cervical vertebrae method by Cameriere. The DA, SA, and CA data were compared using a paired t-test and Wilcoxon test. Results: The mean CA of all groups was calculated as 12.96±0.30, the mean DA of 12.74±0.68 and the mean SA of 12.89±0.89. In males, the DA method presented an underestimation between ages of 14.00 and 14.99 (p<0.05) and an overestimation between ages 9.00 and 11.99 (p<0.05). In females, the DA method showed an underestimation in the 13.00- and 14.99-year-old age groups (p<0.05) and an overestimation in the 10.00- and 11.99-year-old age groups (p<0.05). The SA method revealed a significant underestimation in females between the ages of 13.00 and 14.99 and in males between the ages of 14.00 and 14.99 (p<0.05). Conclusions: The SA estimation method may provide more accurate results compared to the DA method with children of both sexes aged between 9.00 to 12.99 in the determination of CA.

Using artificial intelligence models to evaluate envisaged points initially: A pilot study.

The morphology of the finger bones in hand-wrist radiographs (HWRs) can be considered as a radiological skeletal maturity indicator, along with the other indicators. This study aims to validate the anatomical landmarks envisaged to be used for classification of the morphology of the phalanges, by developing classical neural network (NN) classifiers based on a sub-dataset of 136 HWRs. A web-based tool was developed and 22 anatomical landmarks were labeled on four region of interests (proximal (PP3), medial (MP3), distal (DP3) phalanges of the third and medial phalanx (MP5) of the fifth finger) and the epiphysis-diaphysis relationships were saved as "narrow,""equal,""capping" or "fusion" by three observers. In each region, 18 ratios and 15 angles were extracted using anatomical points. The data set is analyzed by developing two NN classifiers, without (NN-1) and with (NN-2) the 5-fold cross-validation. The performance of the models was evaluated with percentage of agreement, Cohen's (cκ) and Weighted (wκ) Kappa coefficients, precision, recall, F1-score and accuracy (statistically significance: p < 0.05). Method error was found to be in the range of cκ: 0.7-1. Overall classification performance of the models was changed between 82.14% and 89.29%. On average, performance of the NN-1 and NN-2 models were found to be 85.71% and 85.52%, respectively. The cκ and wκ of the NN-1 model were changed between -0.08 (p > 0.05) and 0.91 among regions. The average performance was found to be promising except the regions without adequate samples and the anatomical points are validated to be used in the future studies, initially.

Deep learning enables automatic adult age estimation based on CT reconstruction images of the costal cartilage.

OBJECTIVE: Adult age estimation (AAE) is a challenging task. Deep learning (DL) could be a supportive tool. This study aimed to develop DL models for AAE based on CT images and compare their performance to the manual visual scoring method. METHODS: Chest CT were reconstructed using volume rendering (VR) and maximum intensity projection (MIP) separately. Retrospective data of 2500 patients aged 20.00-69.99 years were obtained. The cohort was split into training (80%) and validation (20%) sets. Additional independent data from 200 patients were used as the test set and external validation set. Different modality DL models were developed accordingly. Comparisons were hierarchically performed by VR versus MIP, single-modality versus multi-modality, and DL versus manual method. Mean absolute error (MAE) was the primary parameter of comparison. RESULTS: A total of 2700 patients (mean age = 45.24 years ± 14.03 [SD]) were evaluated. Of single-modality models, MAEs yielded by VR were lower than MIP. Multi-modality models generally yielded lower MAEs than the optimal single-modality model. The best-performing multi-modality model obtained the lowest MAEs of 3.78 in males and 3.40 in females. On the test set, DL achieved MAEs of 3.78 in males and 3.92 in females, which were far better than the MAEs of 8.90 and 6.42 respectively, for the manual method. For the external validation, MAEs were 6.05 in males and 6.68 in females for DL, and 6.93 and 8.28 for the manual method. CONCLUSIONS: DL demonstrated better performance than the manual method in AAE based on CT reconstruction of the costal cartilage. CLINICAL RELEVANCE STATEMENT: Aging leads to diseases, functional performance deterioration, and both physical and physiological damage over time. Accurate AAE may aid in diagnosing the personalization of aging processes. KEY POINTS: • VR-based DL models outperformed MIP-based models with lower MAEs and higher R2 values. • All multi-modality DL models showed better performance than single-modality models in adult age estimation. • DL models achieved a better performance than expert assessments.

Magnetic resonance imaging of the knee for chronological age estimation-a systematic review.

INTRODUCTION: Radiographs of the hand and teeth are frequently used for medical age assessment, as skeletal and dental maturation correlates with chronological age. These methods have been criticized for their lack of precision, and magnetic resonance imaging (MRI) of the knee has been proposed as a more accurate method. The aim of this systematic review is to explore the scientific and statistical evidence for medical age estimation based on skeletal maturation as assessed by MRI of the knee. MATERIALS AND METHODS: A systematic review was conducted that included studies published before April 2021 on living individuals between 8 and 30 years old, with presumptively healthy knees for whom the ossification stages had been evaluated using MRI. The correlation between "mature knee" and chronological age and the risk of misclassifying a child as an adult and vice versa was calculated. RESULTS: We found a considerable heterogeneity in the published studies -in terms of study population, MRI protocols, and grading systems used. There is a wide variation in the correlation between maturation stage and chronological age. CONCLUSION: Data from published literature is deemed too heterogenous to support the use of MRI of the knee for chronological age determination. Further, it is not possible to assess the sensitivity, specificity, negative predictive value, or positive predictive value for the ability of MRI to determine whether a person is over or under 18 years old. KEY POINTS: • There is an insufficient scientific basis for the use of magnetic resonance imaging of the knee in age determination by skeleton. • It is not possible to assess the predictive value of MRI of the knee to determine whether a person is over or under 18 years of age.

Accuracy of bone age assessment system based on deep learning in children with abnormal growth and development / 中华放射学杂志

Objective:To explore the accuracy of artificial intelligence (AI) system based on deep learning in evaluating bone age of children with abnormal growth and development.Methods:The positive X-ray films of the left wrist of children with abnormal growth and development who were treated at the Affiliated Hospital of Guizhou Medical University from January 2020 to December 2021 were collected retrospectively. A total of 717 children were collected, including 266 males and 451 females, aged 2-18 (11±3) years. Based on Tanner Whitehouse 3 (TW 3)-RUS (radius, ulna, short bone) and TW3-Carpal (carpal bone) method, bone age was measured by 3 senior radiologists, and the mean value was taken as reference standard. The bone ages were independently evaluated by the AI system (Dr.Wise bone age prediction software) and two junior radiologists (physicians 1 and 2). The accuracy within 0.5 year, the accuracy within 1 year, the mean absolute error (MAE) and the root mean square error (RMSE) between the evaluation results and the reference standard were analyzed. Paired sample t-test was used to compare MAE between AI system and junior physicians. Intraclass correlation coefficient (ICC) was used to evaluate the consistency between AI system, junior physician and reference standard. The Bland-Altman diagram was drawn and the 95% consistency limit was calculated between AI system and reference standard. Results:For TW3-RUS bone age, compared with the reference standard, the accuracy within 0.5 year of AI system, physician 1 and physician 2 was 75.3% (540/717), 62.1% (445/717) and 66.2% (475/717), respectively. The accuracy within 1 year was 96.9% (695/717), 86.3% (619/717) and 89.1% (639/717), respectively. MAE was 0.360, 0.565 and 0.496 years, and RMSE was 0.469, 0.634 and 0.572 years, respectively. For TW3-Carpal bone age, compared with the reference standard, the accuracy within 0.5 year of AI system, physician 1 and physician 2 was 80.9% (580/717), 65.1% (467/717) and 71.7% (514/717), respectively. The accuracy within 1 year was 96.0% (688/717), 87.3% (626/717) and 90.4% (648/717), respectively. MAE was 0.330, 0.527 and 0.455 years, and RMSE was 0.458, 0.612, 0.538 years, respectively. Based on TW3-RUS and TW3-Carpal bone age, the MAE of AI system were lower than those of physician 1 and physician 2, and the differences were statistically significant ( P all<0.001). The evaluation results of AI, physician 1 and physician 2 were in good agreement with the reference standard (ICC all>0.950). The Bland-Altman analysis showed that the 95% agreement limits of AI system for assessing TW3-RUS and TW3-Carpal bone age were -0.75-1.02 years and-0.86-0.91 years, respectively. Conclusion:The accuracy of AI system in evaluating the bone age of children with abnormal growth and development is close to that of senior doctors, better than that of junior doctors, and in good agreement with senior doctors.

Clinical application differences between TW3-Carpal and TW3-RUS based artificial intelligence-assisted bone age assessment system: a real-world pilot study / 中华放射学杂志

Objective:To investigate the differences between Tanner-Whitehouse (TW)3-Carpal and TW3-RUS(radius, ulna and short bone)-based artificial intelligence (AI)-assisted bone age assessment system using real world data.Methods:The image data of 262 children who received X-ray examination of left wrist in the Affiliated Children′s Hospital, Capital Institute of Pediatrics from July to September 2021 were retrospectively collected. The AI bone age assistant methods based on TW3-RUS and TW3-Carpal criteria were used to obtain the bone age results, respectively. Two senior pediatric radiologists evaluated the bone age on the basis of TW3-RUS and TW3-Carpal criteria, and the averaged values of two reviewers was calculated and taken as the gold standard reference. The cases were stratified into six age groups at 3-year intervals according to the gold standard reference, including 1-3 ( n=10), 4-6 ( n=35), 7-9 ( n=70), 10-12 ( n=118), 13-15 ( n=27) and 16-18 ( n=2) years old groups. Intraclass correlation coefficient (ICC) was used to evaluate the consistency between AI results and the gold standard bone age results. Pearson correlation method was used to measure the reliability between AI results and the gold standard results. The difference of bone age results between using TW3-RUS and TW3-Carpal criteria in different age groups was compared using paired t-test. Results:As for the whole sample, the results based on TW3-RUS criteria were 8.9±3.1 years old for AI assessment and 8.7±2.9 years old for the golden standard reference, with the ICC of 0.983; and the results based on TW3-Carpal criteria were 8.7±3.0 years old for AI and 8.8±2.8 years old for the golden standard reference, with the ICC of 0.976. Positive correlation were found in both TW3-RUS ( r=0.985, P<0.001) and TW3-Carpal criteria groups ( r=0.978, P<0.001). There were significant differences between TW3-RUS and TW3-Carpal at age groups of 7-9( t=-3.36, P=0.001), 10-12( t=-1.77, P=0.046), and 13-15 years old ( t=1.84, P=0.040). The bone age assessment using TW3-RUS and TW3-Carpal criteria were both in good agreement with the gold standard reference in age group of 4-6 years old (ICC=0.929 and 0.940), as well as in age group of 7-9 years old (ICC=0.882 and 0.927, respectively), with the results using TW3-Carpal criteria were slightly higher. As for the age groups of 10-12 and 13-15 years old, the method using TW3-RUS criteria showed excellent agreement with the gold standard reference (ICC=0.962 and 0.963, respectively), which were better than the performance of method using TW3-Carpal criteria (ICC=0.744 and 0.605, respectively). Conclusions:AI-assisted bone age system based TW3-Carpal and TW3-RUS criteria both show good reliability and accuracy in the bone age measurements. The AI method based TW3-Carpal criteria shows better performance in age group of 4-9 years old, while the method based on TW3-RUS criteria may be better for children of age 10-15 years old.

Applicability analysis of bone age assessment standards for children in rural areas of Beijing / 中华放射学杂志

Objective:To evaluate the applicability of bone age (BA) assessment methods and to investigate the difference between BA and chronological age (CA) based on the data of children in rural areas of Beijing.Methods:A total of 412 healthy children (226 boys, 186 girls) with the age 8.6 (6.8, 10.3) years old were included in this study. The data of the prospective study were from a subgroup of the project "National Nutrition and Health Systematic Survey for 0-18 Years Old Children in China", which included children with age of 3-12 years old in Beijing rural areas. The non-dominant hand-wrist radiographs of all participants were obtained in April 2021. The Dr.Wise BA detection and analysis system was used to assess the BA according to the Tanner Whitehouse 3 (TW3) radius-ulna-short bone score (TW3-RUS), TW3 carpal bone score (TW3-Carpal), China-05 TW3-Chinese RUS (TW3-C RUS), China-05 TW3-Chinese carpal (TW3-C Carpal), and Greulich-Pyle (G-P) standards. The cases were stratified by the sex and different CA in the statistical analysis. The estimated BA obtained using different methods were compared with the CA using Wilcoxon signed ranks test.Results:The sex-stratified results showed that no significant difference was found between the estimated BA using G-P standards and CA in boys ( Z=-0.694, P=0.488), while all the other estimated BA results were statistically significantly higher than CA ( P<0.05). Stratified by both sex and CA, the estimated BA using G-P standards in 4-6 years old boy groups, as well as the estimated BA using TW3-Carpal and TW3-C Carpal standards in 11-12 years old girl groups were lower than CA, while in the other groups, the estimated BA were higher than CA. Conclusions:There were varying degrees of deviations in the BA estimations using TW3, China 05, and G-P methods for children in rural areas of Beijing. It is imperative to establish a new standard for the BA evaluation of the contemporary Chinese children.

Sampling study design and radiography protocol of a large-sample investigation on skeletal maturation in 3 to 18-year-old children in China / 中华放射学杂志

Objective:To report the sampling study design and radiography protocol of a large-sample investigation on skeletal maturation of 3 to 18-year-old children in China.Methods:Multi-stage stratified random sampling was employed in this study. Two provinces, municipalities, or autonomous regions were randomly selected from each of the seven regions of China, including Northeast China, Northwest China, North China, Central China, East China, Southwest China, and South China. Then one rural and one urban investigation site were randomly selected from each province, municipality, or autonomous region. In total 28 sites were included. Among those sites, four residential districts were randomly selected from each urban site, and four townships from each rural site. For each residential district or township, 1-4 kindergartens, primary schools, and middle schools were chosen. Random cluster sampling was used to extract 3-<6-year-old children in kindergartens, and 6-18-year-old children in primary schools and middle schools. The investigation on skeletal maturation was sampled proportionate to the sampling of the whole study. The estimated simple size was 780 for each site, and 21 840 for all 28 sites in total. There were six groups of 3-<6-year-old children classified at 0.5-year intervals, and 12 groups of 6-18-year-old children classified at 1-year intervals. Posteroanterior position radiography of the left hand and wrist was achieved for all subjects.Results:The study was performed from August 26, 2019 to October 16, 2021. In total, 20 444 children received posteroanterior position radiography of the left hand and wrist, including 10 196 males and 10 248 females, 9 711 urban and 10 733 rural, respectively. The 3-<6-year-old group included 1 611 (male 819, female 792) subjects, and the 6 to 18-year-old group included 18 833 (male 9 377, female 9 456) subjects.Conclusion:This nationwide investigation on skeletal maturation of 3 to 18-year-old children in seven regions of China was successfully preformed. The results of this study can provide an important reference for establishing the current evaluation criteria of bone age in Chinese children and adolescents.

Re-Assessment of Applicability of Greulich and Pyle-Based Bone Age to Korean Children Using Manual and Deep Learning-Based Automated Method.

PURPOSE: To evaluate the applicability of Greulich-Pyle (GP) standards to bone age (BA) assessment in healthy Korean children using manual and deep learning-based methods. MATERIALS AND METHODS: We collected 485 hand radiographs of healthy children aged 2-17 years (262 boys) between 2008 and 2017. Based on GP method, BA was assessed manually by two radiologists and automatically by two deep learning-based BA assessment (DLBAA), which estimated GP-assigned (original model) and optimal (modified model) BAs. Estimated BA was compared to chronological age (CA) using intraclass correlation (ICC), Bland-Altman analysis, linear regression, mean absolute error, and root mean square error. The proportion of children showing a difference >12 months between the estimated BA and CA was calculated. RESULTS: CA and all estimated BA showed excellent agreement (ICC ≥0.978, p<0.001) and significant positive linear correlations (R²≥0.935, p<0.001). The estimated BA of all methods showed systematic bias and tended to be lower than CA in younger patients, and higher than CA in older patients (regression slopes ≤-0.11, p<0.001). The mean absolute error of radiologist 1, radiologist 2, original, and modified DLBAA models were 13.09, 13.12, 11.52, and 11.31 months, respectively. The difference between estimated BA and CA was >12 months in 44.3%, 44.5%, 39.2%, and 36.1% for radiologist 1, radiologist 2, original, and modified DLBAA models, respectively. CONCLUSION: Contemporary healthy Korean children showed different rates of skeletal development than GP standard-BA, and systemic bias should be considered when determining children's skeletal maturation.

Forensic age assessment by 3.0 T MRI of the wrist: adaption of the Vieth classification.

OBJECTIVES: In order to find a reliable method to correctly assess majority in both sexes by MRI, a study was conducted to evaluate the applicability of the recently presented Vieth classification in wrist MRI, after it had originally been proposed for knee MRI. METHODS: After receiving a positive vote by the ethics committee, the left-hand wrists of 347 male and 348 female volunteers of German nationality in the age bracket 12-24 years were scanned. Before conducting the prospective, cross-sectional examinations, an informed consent was obtained from each volunteer. A 3.0 T MRI scanner was used, acquiring a T1 turbo spin-echo sequence (TSE) and a T2 TSE sequence with fat suppression by spectral presaturation with inversion recovery (SPIR). The images were assessed by applying the Vieth classification. Minimum, maximum, mean ± standard deviation, and median with lower and upper quartiles were defined. Intra- and interobserver agreements were determined by calculating the kappa coefficients. Differences between the sexes were analyzed using the Mann-Whitney U test. RESULTS: By applying the unmodified Vieth classification with corresponding schematics, it was possible to assess majority in both sexes via the epiphyseal-diaphyseal fusion of the distal radius and in males also via the epiphyseal-diaphyseal fusion of the distal ulna. The Mann-Whitney U test implied significant sex-related differences for all stages. For both epiphyses, the intra- and interobserver agreement levels were very good (κ > 0.8). CONCLUSION: If confirmed by further studies, it would be possible to determine the completion of the 18th year of life in both sexes by 3.0 T MRI of the wrist and using the Vieth classification. KEY POINTS: • The Vieth classification allows determining majority in males and females alike based on the distal radius' epiphysis by 3.0 T MRI of the wrist. • The Vieth classification also allows determining majority in males based on the distal ulna's epiphysis by 3.0 T MRI of the wrist, but not in females. • The presented data can be deemed referential within certain discussed boundaries.

Dental and Skeletal Age in Patients with Palatally Displaced Canines.

Objective: To determine potential associations between dental and skeletal maturation and palatally displaced canines (PDC) considering gender and chronological age. Material and methods: This study included pretreatment panoramic and lateral cephalometric radiographs of 43 subjects with PDCs and 203 randomly selected orthodontic subjects with normally erupted canines. Both groups were non syndromic patients. Chronological age of subjects was rounded and noted in years with decimal points and compared with chronological age according to Demirjian's dental age assessment. Skeletal maturation was determined by cervical vertebrae changes on cephalometric radiographs. Results: Female subjects with PDC were more affected by left side canine displacement than males (p=0.027) with five times higher odds ratio (OR = 4.9; 95% CIL=1.2-19.7). A comparison of chronologic and skeletal age indicated that PDC subjects were skeletally younger than unaffected groups with statistically significant differences at the age of 10, 12 and 13. (p=0.05). Conclusion: Young subjects with PDCs showed skeletal maturation delay compared to control group, indicating that skeletal maturation assessment could be one of unexplored predicting factors of a PDC, especially at the age between 10 and 13 years in both genders. Subjects with PDC showed intensive growth spurt after the age of 12 years in females, and after the age of 13 in males. Dental maturation delay showed no statistical significance in PDC prediction.