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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. [ABSTRACT FROM AUTHOR]

Bone age assessments measure the growth and development of children and adolescents by evaluating their skeletal maturity, which is influenced by various factors like heredity, ethnicity, culture, and nutrition. The clinical standards for this assessment should be up to date and appropriate for the specific population being studied. This study validates the GP-Canary Atlas for accurately predicting bone age by analyzing posteroanterior left hand and wrist radiographs of healthy children (80 females and 134 males) from the Canary Islands across various developmental stages and genders. We found strong intra-rater reliability among all three raters, with Raters 1 and 2 indicating very high consistency (intra-class coefficients = 0.990 to 0.996) and Rater 3 displaying slightly lower but still strong reliability (intra-class coefficients = 0.921 to 0.976). The inter-rater agreement was excellent between Raters 1 and 2 but significantly lower between Rater 3 and the other two raters, with intra-class coefficients of 0.408 and 0.463 for Rater 1 and 0.327 and 0.509 for Rater 2. The accuracy analysis revealed a substantial underestimation of bone age compared to chronological age for preschool- (mean difference = 17.036 months; p < 0.001) and school-age males (mean difference = 13.298 months; p < 0.001). However, this was not observed in females, where the mean difference was minimal (3.949 months; p < 0.239). In contrast, the Atlas showed greater accuracy for teenagers, showing only a slight overestimation (mean difference = 3.159 months; p = 0.823). In conclusion, the GP-Canary Atlas demonstrates overall precision but requires caution as it underestimates the BA in preschool children and overestimates it in school-age girls and adolescents. [ABSTRACT FROM AUTHOR]

Introduction: Automated bone age assessment (BAA) is of growing interest because of its accuracy and time efficiency in daily practice. In this study, we validated the clinical applicability of a commercially available artificial intelligence (AI)-powered X-ray bone age analyzer equipped with a deep learning-based automated BAA system and compared its performance with that of the Tanner–Whitehouse 3 (TW-3) method. Methods: Radiographs prospectively collected from 30 centers across various regions in China, including 900 Chinese children and adolescents, were assessed independently by six doctors (three experts and three residents) and an AI analyzer for TW3 radius, ulna, and short bones (RUS) and TW3 carpal bone age. The experts' mean estimates were accepted as the gold standard. The performance of the AI analyzer was compared with that of each resident. Results: For the estimation of TW3-RUS, the AI analyzer had a mean absolute error (MAE) of 0.48 ± 0.42. The percentage of patients with an absolute error of < 1.0 years was 86.78%. The MAE was significantly lower than that of rater 1 (0.54 ± 0.49, P = 0.0068); however, it was not significant for rater 2 (0.48 ± 0.48) or rater 3 (0.49 ± 0.46). For TW3 carpal, the AI analyzer had an MAE of 0.48 ± 0.65. The percentage of patients with an absolute error of < 1.0 years was 88.78%. The MAE was significantly lower than that of rater 2 (0.58 ± 0.67, P = 0.0018) and numerically lower for rater 1 (0.54 ± 0.64) and rater 3 (0.50 ± 0.53). These results were consistent for the subgroups according to sex, and differences between the age groups were observed. Conclusion: In this comprehensive validation study conducted in China, an AI-powered X-ray bone age analyzer showed accuracies that matched or exceeded those of doctor raters. This method may improve the efficiency of clinical routines by reducing reading time without compromising accuracy. Plain Language Summary: Assessing bone age, or how developed a child's skeleton is, is important in medical care, but the standard method can be time-consuming. Using AI to automatically assess bone age from X-ray images may improve efficiency without reducing accuracy. In this study, we evaluated how well an AI-powered X-ray bone age analyzer performed compared to the established Tanner–Whitehouse 3 (TW-3) method. X-ray images from 900 Chinese children and adolescents were collected from 30 centers. Six doctors (three experts, three residents) and the AI system independently assessed the TW-3 radius, ulna, and short bones (RUS) and TW-3 carpal bone age. The experts' assessments were considered the gold standard. The AI analyzer had an average error of 0.48 years for TW3-RUS bone age, with 87% of assessments within 1 year of the experts. For TW3 carpal bone age, the AI had an average error of 0.48 years, with 89% within 1 year. These results were similar to or better than those of the resident raters. These findings show the AI-powered analyzer can assess bone age as accurately as human raters. This technology may improve clinical efficiency by reducing the time required for bone age assessments without compromising accuracy. [ABSTRACT FROM AUTHOR]

Background: Forensic age estimation is a procedure which utilises many methods to estimate the age of both living and deceased individuals, including those who have died in natural disasters or man-made catastrophes. The pattern and closure of spheno-occipital synchondrosis (SOS) fusion, along with subchondral ossification of the mandibular condyle, can be used to estimate age. Aim and objectives: This study aims to estimate age using computed tomographic (CT) images of spheno-occipital synchondrosis fusion (SOS) and mandibular condylar cortication (MCC), and to correlate these findings with chronological age. Materials and methods: The present study included 435 CT images of individuals aged 10-25 years. SOS fusion was assessed using a four-stage system, and MCC was assessed bilaterally using a three-stage system on the sagittal plane. Data on fusion stages and cortication types were entered along with chronological age, and then statistically analysed. Results: SOS fusion stage 2 occurred at similar age in males (19.82 ± 2.67 years) and females (19.23 ± 2.93 years). Earlier fusion of other stages was observed in females by a mean age of 2 years. MCC was completed 1 year earlier in females, with statistically significant differences (p ≤ 0.001). When comparing cortication types and different fusion stages, only type II cortication showed statistically significant differences compared to different fusion stages (p ≤ 0.001). Conclusion: Mandibular condylar cortication (MCC) and spheno-occipital synchondrosis (SOS) fusion were positively correlated with chronological age, suggesting that these parameters can be used as an adjunct method for age estimation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

ABSTRACT Objective: To evaluate the intra and inter observer agreement of the Sauvegrain, Greulich and Pyle methods. Material and methods: This is an observational, retrospective and cross-sectional study ethically approved by opinion 6,192,391. 100 radiographic images of the elbow and 100 of the left wrist and hand were collected from children whose images were selected by a researcher who did not carry out the evaluations. The Sauvegrain, Greulich and Pyle methods were used to determine bone age. We provided a detailed explanation of each method and the evaluators received a file with the study images. After three weeks, the exams were randomized and the radiograms were reevaluated. Of the 100 patients in group A, 61 (61%) were boys and 39 (39%) were girls. In group B, 67 (67%) were boys and 33 (33%) were girls. Four statistical analyzes were used: correlation; intraclass correlation; analysis using the Bland-Altman graph; differences between groups. Results: Intra and interobserver agreement between groups was considered excellent. Conclusions: Despite the excellent agreement, group A presented a significantly better value than B. Biological ages show a greater difference compared to chronological ages in group A. In group B, skeletal and chronological ages do not show statistical difference according to the accuracy test. Level of Evidence III, Cross-Sectional Observational Study.

ObjectivesIt is important to understand what variables influence change in predicted adult height (PAH) throughout GnRHa treatment for central precocious puberty (CPP) to individualize treatment decisions and optimize care.MethodsChanges in PAH, chronological age (CA), bone age (BA), BA/CA, and height velocity (HV) were evaluated in girls with CPP throughout treatment with leuprolide acetate (n=77). A second analysis focused on changes in the 3 years preceding the first observed BA of ≥12 years. Relationships were characterized using plot inspection and linear mixed-effects analyses. Association between treatment duration and last assessed PAH was examined using multiple linear regression models.ResultsBA/CA and HV showed a nonlinear change during treatment, with the largest changes and improvement in PAH observed in the first 6-18 months. Rate of BA advancement tended to decrease more slowly in girls initiating treatment at a younger BA. On-treatment change in PAH was predicted by concurrent BA/CA change, HV, and BA, as well as CA at treatment initiation. Last assessed PAH was positively associated with longer treatment durations (primary/exploratory models cut-offs of ≥33/≥55 months).ConclusionsThese findings support individualized monitoring during GnRHa treatment. Initial response should be interpreted with caution until 6-18 months after treatment initiation and failure should not be assumed based on continued bone maturation in girls starting therapy at a younger age. Treatment cessation should not be automatically based on a diminishing change in PAH or HV, as ongoing treatment may result in continued increase or maintenance of PAH.

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

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Bone age assessments measure the growth and development of children and adolescents by evaluating their skeletal maturity, which is influenced by various factors like heredity, ethnicity, culture, and nutrition. The clinical standards for this assessment should be up to date and appropriate for the specific population being studied. This study validates the GP-Canary Atlas for accurately predicting bone age by analyzing posteroanterior left hand and wrist radiographs of healthy children (80 females and 134 males) from the Canary Islands across various developmental stages and genders. We found strong intra-rater reliability among all three raters, with Raters 1 and 2 indicating very high consistency (intra-class coefficients = 0.990 to 0.996) and Rater 3 displaying slightly lower but still strong reliability (intra-class coefficients = 0.921 to 0.976). The inter-rater agreement was excellent between Raters 1 and 2 but significantly lower between Rater 3 and the other two raters, with intra-class coefficients of 0.408 and 0.463 for Rater 1 and 0.327 and 0.509 for Rater 2. The accuracy analysis revealed a substantial underestimation of bone age compared to chronological age for preschool- (mean difference = 17.036 months; p < 0.001) and school-age males (mean difference = 13.298 months; p < 0.001). However, this was not observed in females, where the mean difference was minimal (3.949 months; p < 0.239). In contrast, the Atlas showed greater accuracy for teenagers, showing only a slight overestimation (mean difference = 3.159 months; p = 0.823). In conclusion, the GP-Canary Atlas demonstrates overall precision but requires caution as it underestimates the BA in preschool children and overestimates it in school-age girls and adolescents.

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Objective To discuss deep learning modeling using T1-weighted images (T1WI) in magnetic resonance imaging (MRI) of the knee joints and its use in age estimation of adolescents. Methods The T1WI of the knee joints were collected from 1 212 male patients aged 10-18 years who were admitted to The Affiliated Hospital of Qingdao University from January 2015 to December 2021 (internal data set) and 341 male patients of the same ages who were admitted to Qingdao Municipal Hospital during the same period (external data set). After labeling and image segmentation of the epiphyseal plates of the distal femurs and proximal tibiae, the internal data set was divided into training group (971 cases) and validation group (241 cases) at a ratio of 8∶2 according to their age groups using a random number table for modeling, and the external data set (test group) was used for model evaluation. The performance of the model was tested and validated based on accuracy, precision, recall rate, sensitivity, and specificity. Results The accuracy, precision, recall rate, specificity, and sensitivity of the validation group were 85.713%, 84.732%, 85.713%, 97.729%, and 85.713%, respectively; the same indicators of the test group were 82.578%, 83.145%, 82.578%, 97.442%, and 82.578%, respectively. There were no significant differences in the above indicators between the validation group and the test group (P>0.05). Conclusion A deep learning model based on the T1WI of the knee joints is successfully constructed, and it can be used for age estimation of adolescents aged 10-18 years.

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.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Graphical abstract 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. [ABSTRACT FROM AUTHOR]

BackgroundStatural growth impairment is more common in males with Crohn's disease (CD). We assessed sex differences in height Z score differences and bone age (BA) Z scores and characterized age of menarche in a novel contemporary cohort of pediatric CD patients undergoing screening for enrollment in the multicenter longitudinal Growth Study.MethodsCrohn's disease patients (females with chronological age [CA] 5 years and older and younger than 14 years; males with CA 6 years and older and younger than 16 years) participated in a screening visit for the Growth Study. Height BA-Z scores are height Z scores calculated based on BA. Height CA-Z scores are height Z scores calculated based on CA. The height Z score difference equals height CA-Z score minus height BA-Z score.ResultsOne hundred seventy-one patients (60% male) qualified for this analysis. Mean CA was 12.2 years. Mean height CA-Z score was -0.4, and mean height BA-Z score was 0.4 in females. Mean height CA-Z score was -0.1, and mean height BA-Z score was 0.2 in males. The absolute value of the mean height Z score difference was significantly greater in females (0.8) than males (0.3; P = 0.005). The mean BA-Z score in females (-1.0) was significantly lower than in males (-0.2; P = 0.002). The median CA at menarche was 13.6 (95% CI, 12.6-14.6) years.ConclusionsOur screening visit data suggest that standardized height gain is lower in males with skeletal maturation and delayed puberty is common in females in CD. We are investigating these findings in the ongoing Growth Study.

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

Featured Application: The aim of this article is to provide the reader with an overview of current applications of AI in orthodontics. Artificial intelligence (AI) has already arrived in many areas of our lives and, because of the increasing availability of computing power, can now be used for complex tasks in medicine and dentistry. This is reflected by an exponential increase in scientific publications aiming to integrate AI into everyday clinical routines. Applications of AI in orthodontics are already manifold and range from the identification of anatomical/pathological structures or reference points in imaging to the support of complex decision-making in orthodontic treatment planning. The aim of this article is to give the reader an overview of the current state of the art regarding applications of AI in orthodontics and to provide a perspective for the use of such AI solutions in clinical routine. For this purpose, we present various use cases for AI in orthodontics, for which research is already available. Considering the current scientific progress, it is not unreasonable to assume that AI will become an integral part of orthodontic diagnostics and treatment planning in the near future. Although AI will equally likely not be able to replace the knowledge and experience of human experts in the not-too-distant future, it probably will be able to support practitioners, thus serving as a quality-assuring component in orthodontic patient care. [ABSTRACT FROM AUTHOR]

Purpose The Radiological Society of North America (RSNA) Pediatric Bone Age Machine Learning Challenge was created to show an application of machine learning (ML) and artificial intelligence (AI) in medical imaging, promote collaboration to catalyze AI model creation, and identify innovators in medical imaging. Materials and Methods The goal of this challenge was to solicit individuals and teams to create an algorithm or model using ML techniques that would accurately determine skeletal age in a curated data set of pediatric hand radiographs. The primary evaluation measure was the mean absolute distance (MAD) in months, which was calculated as the mean of the absolute values of the difference between the model estimates and those of the reference standard, bone age. Results A data set consisting of 14 236 hand radiographs (12 611 training set, 1425 validation set, 200 test set) was made available to registered challenge participants. A total of 260 individuals or teams registered on the Challenge website. A total of 105 submissions were uploaded from 48 unique users during the training, validation, and test phases. Almost all methods used deep neural network techniques based on one or more convolutional neural networks (CNNs). The best five results based on MAD were 4.2, 4.4, 4.4, 4.5, and 4.5 months, respectively. Conclusion The RSNA Pediatric Bone Age Machine Learning Challenge showed how a coordinated approach to solving a medical imaging problem can be successfully conducted. Future ML challenges will catalyze collaboration and development of ML tools and methods that can potentially improve diagnostic accuracy and patient care. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Siegel in this issue.

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.

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 assess-ment. 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.

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. [ABSTRACT FROM AUTHOR]

Background: Serum insulin-like growth factor 1 (IGF-1) is an important biochemical tool to diagnose and monitor growth hormone (GH)-related disorders. However, ethnicity-specific Indian data, following consensus criteria for the establishment of normative data, are not available. Our objective was to generate chronological age (CA)-, bone age (BA)- and Tanner stage-specific normative data for IGF-1 in healthy Indian children and adolescents., Methods: A cross-sectional epidemiological study was conducted in schools and the community, which enrolled apparently healthy children and adolescents with robust exclusion criteria. The outcome measure was serum IGF-1 assessed using an electro-chemiluminescence immunoassay (ECLIA). The 2.5th, 5th, 10th, 25th, 50th (median), 75th, 90th, 95th, and 97.5th centiles for IGF-1 were estimated using generalized additive models., Results: We recruited 2226 apparently healthy participants and following exclusion, 1948 (1006 boys, 942 girls) were included in the final analysis. Girls had median IGF-1 peak at CA of 13 years (321.7 ng/mL), BA of 14 years (350.2 ng/mL) and Tanner stage IV (345 ng/mL), while boys had median IGF-1 peak at CA of 15 years (318.9 ng/mL) BA of 15 years (340.6 ng/mL) and Tanner stage III (304.8 ng/mL). Girls had earlier rise, earlier peak, and higher IGF-1 values. The reference interval (2.5th to 97.5th percentile) was broader during peripubertal ages, indicating a higher physiological variability., Conclusion: This study provides ethnicity-specific normative data on serum IGF-1 and will improve the diagnostic utility of IGF-1 in the evaluation and management of growth disorders in Indian children and adolescents., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com. See the journal About page for additional terms.)

Objective: To evaluate the intra and inter observer agreement of the Sauvegrain, Greulich and Pyle methods., Material and Methods: This is an observational, retrospective and cross-sectional study ethically approved by opinion 6,192,391. 100 radiographic images of the elbow and 100 of the left wrist and hand were collected from children whose images were selected by a researcher who did not carry out the evaluations. The Sauvegrain, Greulich and Pyle methods were used to determine bone age. We provided a detailed explanation of each method and the evaluators received a file with the study images. After three weeks, the exams were randomized and the radiograms were reevaluated. Of the 100 patients in group A, 61 (61%) were boys and 39 (39%) were girls. In group B, 67 (67%) were boys and 33 (33%) were girls., Four Statistical Analyzes Were Used: correlation; intraclass correlation; analysis using the Bland-Altman graph; differences between groups., Results: Intra and interobserver agreement between groups was considered excellent., Conclusions: Despite the excellent agreement, group A presented a significantly better value than B. Biological ages show a greater difference compared to chronological ages in group A. In group B, skeletal and chronological ages do not show statistical difference according to the accuracy test. Level of Evidence III, Cross-Sectional Observational Study. , Competing Interests: All authors declare no potential conflict of interest related to this article.

Objectives: The aim of the present work is to develop a novel method for predicting age in individuals over 50 years old, utilizing regression models., Methods: The conducted study is of an analytical cross-sectional nature, involving a sample of 44 young subjects and 107 elderly subjects. The necessary data for this research were extracted from "The New Mexico Decedent Image Database." Based on the phenomenon of height shrinkage with age, we created models for young subjects and applied them to elderly subjects, allowing us to extract the variables., Results: We obtained highly encouraging results with an R 2 of 0.73, a mean absolute error of 3.94, and stable cross-validation. We used a Student's t-test, which demonstrated no significant difference between predicted and actual values (p-value >0.05). We also conducted a learning curve analysis and examined residuals against predicted values. This suggests that the forecasts are accurate, with no significant bias in predictions., Conclusion: This work has allowed us to conclude that it is possible to reliably estimate the age of subjects over 50, taking into account age-related physiological and pathological changes., (Copyright © 2024 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.)

Purpose: Height age (HA) and bone age (BA) delay is well known in the patients with short stature. Therefore assessing pituitary hypoplasia based on chronological age (CA) might cause overdiagnosis of pituitary hypoplasia. We aimed to investigate the diagnostic and prognostic value of the PH and PV based on CA, HA, or BA in the patients with GHD., Methods: Fifty-seven patients with severe and 40 patients with partial GHD and 39 patients with ISS assigned to the study. For defining the most accurate diagnosis of pituitary hypoplasia, PH and PV were evaluated based on CA, BA and HA. The relationship of each method with clinical features was examined., Results: The mean PV was significantly larger in patients with ISS compared to the GH-deficient patients. PV was more correlated with clinical features including height SDS, stimulated GH concentration, IGF-1 and IGFBP-3 SDS, height velocity before and after rGH therapy. We found BA-based PV could discriminate GHD from ISS (Sensitivity: 17%, specificity: 98%, positive predictive value: 94%, negative predictive value: 39%), compared to the other methods based on PH or PV respect to CA and HA. 3% of patients with ISS, 17% of patients with GHD had pituitary hypoplasia based on PV-BA., Conclusion: PV based on BA, has the most accurate diagnostic value for defining pituitary hypoplasia. But it should be kept in mind that there might be still misdiagnosed patients by this method. PV is also a significant predictor for the rGH response., (© 2024. The Author(s).)

Osteogenesis imperfecta (OI)is a rare genetically heterogeneous disorder caused by changes in the expression or processing of type I collagen. Clinical manifestations include bone fragility, decreased linear growth, and skeletal deformities that vary in severity. In typically growing children, skeletal maturation proceeds in a predictable pattern of changes in the size, shape, and mineralization on the hand and wrist bones that can be followed radiographically known at the bone age. Assessment of bone age can be clinically used to assess time remaining for linear growth, and the onset and duration of puberty, both of which can be useful in determining the timing of some surgeries or the interpretation of other imaging modalities such as bone densitometry. Additionally, deviations in the expected maturation process of the bone age may prompt or assist in the work up of a significant delay or advancement in a child's growth pattern. The primary aim of our study was to determine whether the bone age in children with a skeletal disorder such as OI follow the same pattern and rate of bone maturation compared to a control population. Using participants from the Natural History Study of the Brittle Bone Disorders Consortium, we analyzed 159 left hand and wrist radiographs (bone age) for a cross-sectional analysis and 55 bone ages repeated at approximately 24 months for a longitudinal analysis of skeletal maturation. Bone ages were read by a pediatric endocrinologist and by an automated analysis using a program called BoneXpert. Our results demonstrated that in children with mild-to-moderate OI (types I and IV), the skeletal maturation is comparable to chronological age-mated controls. For those with more severe forms of OI (type III), there is a delayed pattern of skeletal maturation of less than a year (10.5 months CI 5.1-16) P = 0.0012) at baseline and a delayed rate of maturation over the two-year follow up compared to type I (P = 0.06) and type III (P = 0.02). However, despite these parameters being statistically different, they may not be clinically significant. We conclude the bone age, with careful interpretation, can be used in the OI population in a way that is similar to the general pediatric population., Competing Interests: Declaration of competing interest LN: Consultant for Ultragenxy, Data Safety Monitoring Committee, Consultant Egetis, Data Safety Monitoring Committee, HB: No disclosures, SK: No disclosures, WL: No disclosures, EO: No disclosures., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has already arrived in many areas of our lives and, because of the increasing availability of computing power, can now be used for complex tasks in medicine and dentistry. This is reflected by an exponential increase in scientific publications aiming to integrate AI into everyday clinical routines. Applications of AI in orthodontics are already manifold and range from the identification of anatomical/pathological structures or reference points in imaging to the support of complex decision-making in orthodontic treatment planning. The aim of this article is to give the reader an overview of the current state of the art regarding applications of AI in orthodontics and to provide a perspective for the use of such AI solutions in clinical routine. For this purpose, we present various use cases for AI in orthodontics, for which research is already available. Considering the current scientific progress, it is not unreasonable to assume that AI will become an integral part of orthodontic diagnostics and treatment planning in the near future. Although AI will equally likely not be able to replace the knowledge and experience of human experts in the not-too-distant future, it probably will be able to support practitioners, thus serving as a quality-assuring component in orthodontic patient care.

Objective: To assess the evidence on the relationship between dental and cervical vertebrae maturation. Materials and methods: Searches were performed (up to December 2021) in seven databases, as well as in gray literature. Studies that included growing subjects, which evaluated the correlation between the stages of tooth formation and the maturation of cervical vertebrae, were eligible. The methodological quality of the selected reports was assessed using the JBI checklist for cross-sectional studies. Random-effects meta-analyses were implemented to calculate the pooled correlation coefficients between the maturation stages of cervical vertebrae and each tooth type. The GRADE approach was followed to assess the certainty of evidence. Results: Seventy-seven studies were selected. Most of them had limitations related to sampling and reliability of the methods to evaluate maturation. The syntheses evidenced positive correlations between the maturation of cervical vertebrae and the formation/calcification stages of the following lower teeth: canine (n = 7318; r = 0.692; 95% CI: 0.656–0.724), first premolar (n = 6194; r = 0.689; 95% CI: 0.649–0.725), second premolar (n = 6194; r = 0.695; 95% CI: 0.659–0.729), and second molar (n = 7905; r = 0.698; 95% CI: 0.652–0.739). All estimates were affected by the risk of bias and the presence of unexplained statistical heterogeneity. Conclusions: The evidence points to an apparent positive correlation between dental and cervical vertebrae maturation. These results need to be carefully evaluated as the body of evidence is of very low quality. Clinical relevance: Dental maturation might be a potential indicator of skeletal craniofacial growth status. A routine dental x-ray could be sufficient, reducing radiation exposure by requesting additional radiographs. [ABSTRACT FROM AUTHOR]

Introduction: the mandible undergoes changes in morphology and density related to the aging process. These changes are measured by radiomorphometric indices that allow inferring bone density indirectly. Also, the age estimation process in adults is challenging since the modifications in the anatomical structures are gradual and require long intervals of time to be noticeable. Objective: this study aimed to evaluate the usefulness of radiomoformometric indices of the mandible in age estimation in adults. The sample consisted of 230 digital panoramic radiographs of individuals of both genders (115 men and 115 women), with chronological ages between 20 and 81 years, where radiomorphometric indices were obtained in the premolar region and the mandible ramus, using the software ImageJ. An analysis of variance was conducted considering individuals' gender and age group classification categories with Tukey's post hoc test. Results: a downward trend was observed in means indices values for the older age groups, showing lower values for females. Based on the interaction detected between gender and age groups, multiple regression models were applied to estimate age. These showed a better adjustment for males between 50 and 59 years (R2=82,85%) and males over 60 (R2=80,16%). Conclusions: the radiomorphometric indices used in this study allowed to infer age from 50 years onwards in males. [ABSTRACT FROM AUTHOR]

Purpose: This study assessed the associations between chronological age, dental maturation (DM), cervical vertebrae maturation (CVM), and hand-wrist maturation (HWM) in individuals aged 9-19 years. In addition, this study aimed to derive practical methods to evaluate the skeletal age using DM, CVM, or HWM for orthodontic, medical, and forensic purposes and to compare which of these 3 developmental parameters is more accurate for estimating the age of individuals in a Turkish population. Materials and Methods: Panoramic, lateral cephalometric, and hand-wrist radiographs of 284 patients aged 9-19 years were used in this study. The DM, CVM, and HWM stages were determined. The Kolmogorov-Smirnov, kappa, Wilcoxon, Kruskal-Wallis, chi-square, and Spearman correlation tests and simple linear regression analysis were used for statistical analysis. The significance level was 0.05. Results: Statistically significant differences were found between chronological age and DM, chronological age and CVM, and chronological age and HWM in both sexes(P<0.05). DM did not show statistically significant differences according to sex (P>0.05), but CVM and HWM were statistically different between males and females(P<0.05). The DM-estimated age yielded more accurate values than the other methods. Conclusion: All correlations between skeletal and dental stages were statistically significant. Our results showed that there was no statistically significant difference between chronological age and DM-estimated age. Therefore, it can be concluded that DM stages have the potential to be used for legal purposes. [ABSTRACT FROM AUTHOR]

Objective To explore the feasibility of the CT image reconstruction of laryngeal cartilage and hyoid bone in adult age estimation using data mining methods. Methods The neck thin slice CT scans of 413 individuals aged 18 to

Background: Evidence shows that overweight and obesity are associated with advanced bone age (BA)., Objective: To analyse the effect of adiposity on BA among Mexican children., Methods: This cross-sectional study included 902 children (5-18 years old). Anthropometric measurements, dual-energy X-ray absorptiometry (DXA) and automated hand X-ray-based BA measurements were obtained. BA curves of children stratified by sex and age were created based on nutritional status. We also calculated odds ratios for advanced BA associated with the body mass index (BMI), waist/height ratio and adiposity estimated using DXA (total and truncal fat mass)., Results: Participants with overweight/obesity by BMI (SDS ≥1) advanced earlier in BA than did normal weight participants (6.0 vs. 12.0 years in boys and 6.0 vs. 10.3 in girls, p < 0.01); similarly, participants with a greater body fat percentage (SDS ≥1) exhibited earlier advanced BA (7.5 vs. 10.0 years in boys and 6.0 vs. 9.6 in girls, p < 0.01). Differences were also observed according to the waist/height ratio and truncal fat. Children with a BMI or DXA SDS ≥1 had greater odds of presenting an advanced BA of more than 1 year (OR 1.79-3.55, p < 0.05)., Conclusions: Increased adiposity in children, mainly in boys, is associated with advanced BA at earlier ages., (© 2024 World Obesity Federation.)

Background: We aimed to determine the growth‐development periods and gender from the cervical vertebrae using the artificial neural network (ANN). Setting and Sample Population: The cephalometric and hand‐wrist radiographs obtained from 419 patients aged between 8 and 17 years were included in our study. Materials and Methods: Our retrospective study consisted of 419 patients' cephalometric and hand‐wrist radiographs. The cephalometric radiographs were divided into six cervical vertebrae stages (CVS). Correlations were evaluated between hand‐wrist maturation level, CVS, and ages. Twenty‐seven vertebral reference points are marked on the cephalometric radiograph, and 32 linear measurements were taken. With the combination of these measurements, 24 different data sets were formed to train ANN. Thus, 24 different ANN models for the determination of the growth‐development periods were obtained. According to the results, seven ANN models that have a high success level and clinically applicable were selected. Also, an ANN model was done by all measurements and age for the determination of gender from cervical vertebrae. Results: Significantly positive correlations between hand‐wrist maturation level, CVS and ages were detected. The ANN‐7 model (32 linear measurements and age) accuracy value was found 0.9427. The highest model accuracy, 0.8687, with the least linear measurements, was obtained by drawing 13 linear measurements, using vertical measurements and indents. Gender was determined using ANN (0.8950) on cervical vertebrae data. Conclusion: The growth‐development periods and gender were determined from the cervical vertebrae by using ANN. The success of the ANN algorithm has been satisfactory. Further studies are needed for a fully automatic decision support system. [ABSTRACT FROM AUTHOR]

Abstract Proper bone age assessment is crucial for the clinical diagnosis and evaluation of treatment responses. We investigated the applicability of Greulich and Pyle (GP), and Tanner and Whitehouse 3 (TW3) methods for children in modern Taiwan, using computer‐aided diagnosis. Hand and wrist radiographs were obtained from 611 children (3‐17 years) who came to our emergency department due to trauma. Ages 0 to 2 years old were excluded because of a limited number of cases. Skeletal maturation was assessed using the BoneXpert (version 2.5.4.1 automated software), which determines GP and TW3 bone age. The two scoring systems were evaluated for comparing the chronological ages in each subgroup. In boys, mean GP bone age vs mean chronological ages were delayed for ages 3 to 11 and advanced for age 12 to 17. In girls, mean GP bone age vs mean chronological ages was delayed for ages 4 to 8 and 17, and advanced for ages 3 and 9 to 17. In boys, the mean TW3 bone ages vs mean chronological ages were delayed for ages 5 to 10 except age 8, and advanced for ages 3 to 4, 8, and 11 to 15. In girls, the mean TW3 bone ages vs mean chronological ages were delayed for ages 4 to 12, and advanced for ages 3 and 13 to 14. By using the BoneXpert automatic software, we established bone age reference standards for children in Taiwan. Clinical application of GP and TW3 scoring methods can be adjusted according to our results to better assess bone age.

Adult age determination plays an important role in individual identification, criminal investigation and social welfare. The most popular adult age determination indicators are pubic symphysis, iliac auricular surface, costal cartilage, cranial sutures, teeth, laryngeal cartilage, etc. In recent years, with the progress of CT imaging and 3D reconstruction technology, the adult age determination study gradually has transferred from a time-consuming general observation of bones with complex pre-processing in the past to the non-destructive, convenient, time-saving and easy to store image analysis technology. To explore more accurate, rapid and convenient adult age determination methods, multiple imaging methods and artificial intelligence have been applied in adult age determination. This paper reviews the common methods and research progress of adult age determination at home and abroad, infers the development direction of adult age determination, in order to provide reference for the improvement and optimization of forensic adult age determination.

Objective To compare the performance of three deep-learning models （VGG19, Inception-V3 and Inception-ResNet-V2） in automatic bone age assessment based on pelvic X-ray radiographs. Methods A total of 962 pelvic X ray radiographs taken from adolescents （481 males, 481 females） aged from 11.0 to 21.0 years in five provinces and cities of China were collected, preprocessed and used as objects of study. Eighty percent of these X ray radiographs were divided into training set and validation set with random sampling method and used for model fitting and hyper-parameters adjustment. Twenty percent were used as test sets, to evaluate the ability of model generalization. The performances of the three models were assessed by comparing the root mean square error （RMSE）, mean absolute error （MAE） and Bland-Altman plots between the model estimates and the chronological ages. Results The mean RMSE and MAE between bone age estimates of the VGG19 model and the chronological ages were 1.29 and 1.02 years, respectively. The mean RMSE and MAE between bone age estimates of the Inception-V3 model and the chronological ages were 1.17 and 0.82 years, respectively. The mean RMSE and MAE between bone age estimates of the Inception-ResNet-V2 model and the chronological ages were 1.11 and 0.84 years, respectively. The Bland-Altman plots showed that the mean value of differences between bone age estimates of Inception-ResNet-V2 model and the chronological ages was the lowest. Conclusion In the automatic bone age assessment of adolescent pelvis, the Inception-ResNet-V2 model performs the best while the Inception-V3 model achieves a similar accuracy as VGG19 model.

One of the major tasks in the forensic field is age estimation in living individuals, especially in adolescents and young adults. The X-ray examination of left hand, panoramic radiograph and CT scan of the sternal end of clavicles are mature means that are widely used. However, the X-ray technique has great radiation on the human body, and imaging radiation for non-diagnosis and treatment purposes does not conform to the current mainstream medical ethics. MRI is nonradioactive tomographic imaging and is one of the research and development directions in forensic age estimation in living individuals now. This paper summarizes the common indicators and analysis methods of MRI in previous research of age estimation, in order to get better understanding of its trends and provide a clue for future relevant studies.

Bone age is an important indicator of human growth and development, which can objectively reflect the growth level and maturity of individuals. Traditional manual bone age assessment usually compares the X-ray of the left wrist with the reference standard to obtain the corresponding bone age. This method is time-consuming and its results vary with different observers. In recent years, with the continuous development of computer science, bone age assessment has began to change from traditional manual assessment to automatic assessment. Although there has already been numerous researches on automatic bone age assessment, most of them are still in the experimental stage. This paper reviews related research and progress on automatic bone age assessment at home and abroad in recent years, in order to provide reference and research ideas for relevant researchers.

Introduction: Biochemical markers play an important role in the growth and repair of bone and can be evaluated in different biological fluids. Therefore, the aim of this study was to evaluate the level of insulin-like growth factor-I (IGF-I) in urine as a puberty index and to compare it with cervical vertebral maturational stages (CVMSs) in lateral cephalometry. Materials&Methods: In this cross-sectional study, 50 8-18-year-old patients (males and females), referred to Faculty of Dentistry with CVMS of 2 to 6 and needed orthodontic treatment as well as lateral cephalometry were selected. All of these patients were healthy. The CVMSs were recorded based on lateral cephalometric radiographs and urinary levels of IGF-I was determined using a urine-based ELISA kit. Mean IGF-I values for each CVMS were analyzed by Mann-Whitney, Kruskal-Wallis and Chi-square tests in SPSS. P

Bone age assessment has always been one of the key issues and difficulties in forensic science. With the gradual development of machine learning in many industries, it has been widely introduced to imageology, genomics, oncology, pathology, surgery and other medical research fields in recent years. The reason why the above research fields can be closely combined with machine learning, is because the research subjects of the above branches of medicine belong to the computer vision category. Machine learning provides unique advantages for computer vision research and has made breakthroughs in medical image recognition. Based on the advantages of machine learning in image recognition, it was combined with bone age assessment research, in order to construct a recognition model suitable for forensic skeletal images. This paper reviews the research progress in bone age assessment made by scholars at home and abroad using machine learning technology in recent years.

Objective: The present study aimed to evaluate the possibility of sex and age estimation in a Brazilian sample, using the following five mandibular measurements: coronoid height, gonial angle, bigo-nial distance, ramus height, maximum length. Material and methods: In total, 103 mandibles were measured, 53 female and 50 male, grouped according to the age in 5 different groups. Two differ-ent observers performed all these measurements. For statistical analysis, one-way ANOVA was used with a significance level of 5% to verify if the mandibular measurements were related to age. The Stu-dent t-test was used for comparisons between the sex samples. Eighty three samples were used to develop a logistic regression model. The intra-observer and inter-observer differences were evalu-ated using the Kappa coefficient. Results: The sex differences were statistically significant in all the variables, and all of them were larger in men, apart from the variable related to the gonial angle (GA), which was larger in women. For the logistic regression formula, the variables used were: bigonial dis-tance and mandibular ramus, with 90% accuracy. For age, the measurements did not show a pattern. The inter and intra-observer values were greater than 0.85. Conclusion: In conclusion, it is possible to estimate sex using mandibular measurements, but it is not recommended to use these measure-ments for age estimation.

Abstract Background Bone age (BA) assessment performed by artificial intelligence (AI) is of growing interest due to improved accuracy, precision and time efficiency in daily routine. The aim of this study was to investigate the accuracy and efficiency of a novel AI software version for automated BA assessment in comparison to the Greulich-Pyle method. Methods Radiographs of 514 patients were analysed in this retrospective study. Total BA was assessed independently by three blinded radiologists applying the GP method and by the AI software. Overall and gender-specific BA assessment results, as well as reading times of both approaches, were compared, while the reference BA was defined by two blinded experienced paediatric radiologists in consensus by application of the Greulich-Pyle method. Results Mean absolute deviation (MAD) and root mean square deviation (RSMD) were significantly lower between AI-derived BA and reference BA (MAD 0.34 years, RSMD 0.38 years) than between reader-calculated BA and reference BA (MAD 0.79 years, RSMD 0.89 years; p