The BoneXpert method for automated determination of skeletal maturity

In children under two years of age, does the bone health index value differ between those with and without osteogenesis imperfecta?

BACKGROUND

In children with unexplained fractures who are below the age of two years, it may be difficult to distinguish those with low bone mineral density (BMD) due to conditions such as osteogenesis imperfecta (OI) from those who have been abused. Currently, no imaging modality can readily or reliably assess BMD or evaluate bone strength in this age group.

AIM

To investigate whether bone health index (BHI) and bone health index standard deviation scores (SDS) are sufficiently sensitive to distinguish between children under two years old with and without OI.

METHODS

In this retrospective pilot study, we measured BHI and BHI SDS from 122 radiographs (33 OI, 89 suspected abuse) using BoneXpert software. Standard statistical methods (t-test, Pearson's correlation) were applied in addition to clinical diagnostics, sensitivity, specificity, and receiver operating characteristic (ROC) curves. An arbitrary level of p < 0.05 was assumed.

RESULTS

BHI was significantly greater in the group without OI compared to the group with OI, 3.75 and 3.41, respectively (p = 0.003). The percentage of children in the OI/non-OI groups with BHI ≤ 2.49, 2.5-2.99, 3-3.49, and ≥4 was 0 %/0 %, 27 %/7 %, 58 %/28 %, 18 %/29 %, and 12 %/36 %, respectively. While BHI SDS was significantly greater in the group without OI compared to the group with OI, -0.039 and -0.451, respectively (p = 0.01), BHI SDS was within the normal range (±2) for both groups.

CONCLUSION

Although BHI SDS is lower in OI children, it remained within the normal range. Infants without OI had better volumetric bone mineral density, associated with stronger bones. This suggests BHI might be used to differentiate between young children with low BMD and those with healthy bones. Clinicians may find the cut-points established in this study useful for assessing the sensitivity and specificity of BHI in detecting OI and identifying individuals without OI. Further research is needed to assess BHI's clinical utility in this age group.

Charting the growth through intelligence: A SWOC analysis on AI-assisted radiologic bone age estimation

Bone age estimation (BAE) is based on skeletal maturity and degenerative process of the skeleton. The clinical importance of BAE is in understanding the pediatric and growth-related disorders; whereas medicolegally it is important in determining criminal responsibility and establishing identification. Artificial Intelligence (AI) has been used in the field of the field of medicine and specifically in diagnostics using medical images. AI can greatly benefit the BAE techniques by decreasing the intra observer and inter observer variability as well as by reducing the analytical time. The AI techniques rely on object identification, feature extraction and segregation. Bone age assessment is the classical example where the concepts of AI such as object recognition and segregation can be used effectively. The paper describes various AI based algorithms developed for the purpose of radiologic BAE and the performances of the models. In the current paper we have also carried out qualitative analysis using Strength, Weakness, Opportunities and Challenges (SWOC) to examine critical factors that contribute to the application of AI in BAE. To best of our knowledge, the SWOC analysis is being carried out for the first time to assess the applicability of AI in BAE. Based on the SWOC analysis we have provided strategies for successful implementation of AI in BAE in forensic and medicolegal context.

A critical comparative study of the performance of three AI-assisted programs for bone age determination

OBJECTIVES

To date, AI-supported programs for bone age (BA) determination for medical use in Europe have almost only been validated separately, according to Greulich and Pyle (G&P). Therefore, the current study aimed to compare the performance of three programs, namely BoneXpert, PANDA, and BoneView, on a single Central European population.

MATERIALS AND METHODS

For this retrospective study, hand radiographs of 306 children aged 1-18 years, stratified by gender and age, were included. A subgroup consisting of the age group accounting for 90% of examinations in clinical practice was formed. The G&P BA was estimated by three human experts-as ground truth-and three AI-supported programs. The mean absolute deviation, the root mean squared error (RMSE), and dropouts by the AI were calculated.

RESULTS

The correlation between all programs and the ground truth was prominent (R2 ≥ 0.98). In the total group, BoneXpert had a lower RMSE than BoneView and PANDA (0.62 vs. 0.65 and 0.75 years) with a dropout rate of 2.3%, 20.3% and 0%, respectively. In the subgroup, there was less difference in RMSE (0.66 vs. 0.68 and 0.65 years, max. 4% dropouts). The standard deviation between the AI readers was lower than that between the human readers (0.54 vs. 0.62 years, p < 0.01).

CONCLUSION

All three AI programs predict BA after G&P in the main age range with similar high reliability. Differences arise at the boundaries of childhood.

KEY POINTS

Question There is a lack of comparative, independent validation for artificial intelligence-based bone age estimation in children. Findings Three commercially available programs estimate bone age after Greulich and Pyle with similarly high reliability in a central European cohort. Clinical relevance The comparative study will help the reader choose a software for bone age estimation approved for the European market depending on the targeted age group and economic considerations.

Automatic Evaluation of Bone Age Using Hand Radiographs and Pancorporal Radiographs in Adolescent Idiopathic Scoliosis

Background/Objectives: Adolescent idiopathic scoliosis (AIS) is a complex, three-dimensional spinal deformity that requires monitoring of skeletal maturity for effective management. Accurate bone age assessment is important for evaluating developmental progress in AIS. Traditional methods rely on ossification center observations, but recent advances in deep learning (DL) might pave the way for automatic grading of bone age. Methods: The goal of this research is to propose a new deep neural network (DNN) and evaluate class activation maps for bone age assessment in AIS using hand radiographs. We developed a custom neural network based on DenseNet201 and trained it on the RSNA Bone Age dataset. Results: The model achieves an average mean absolute error (MAE) of 4.87 months on more than 250 clinical testing AIS patient dataset. To enhance transparency and trust, we introduced Score-CAM, an explainability tool that reveals the regions of interest contributing to accurate bone age predictions. We compared our model with the BoneXpert system, demonstrating similar performance, which signifies the potential of our approach to reduce inter-rater variability and expedite clinical decision-making. Conclusions: This study outlines the role of deep learning in improving the precision and efficiency of bone age assessment, particularly for AIS patients. Future work involves the detection of other regions of interest and the integration of other ossification centers.

Enhancing Pediatric Bone Age Assessment Using Artificial Intelligence: Implications for Orthopedic Surgery

Background Bone age assessment is a critical tool in pediatric orthopedic surgery, guiding treatment decisions for growth-related disorders and surgical interventions. Traditional methods, such as the Greulich-Pyle and Tanner-Whitehouse techniques, rely on manual interpretation of hand and wrist radiographs, making them time-intensive and susceptible to inter-operator variability. Artificial intelligence (AI) has emerged as a promising tool to enhance accuracy, efficiency, and standardization in skeletal maturity assessment. Methods This study evaluates the application of AI in pediatric bone age prediction using the Radiological Society of North America (RSNA) 2017 Pediatric Bone Age Challenge dataset. A deep learning model based on the ResNet-50 architecture (Microsoft Research, Redmond, Washington, USA) was developed and trained on 12,611 hand and wrist radiographs, validated on 1,425 images, and tested on 200 images. Model performance was assessed using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). Results The AI model achieved an RMSE of 11.07 months, an MAE of 8.54 months, and an R² of 0.929, indicating strong alignment with radiologist-determined bone ages. The Pearson correlation coefficient (0.963) and Spearman's rank correlation (0.955) confirmed the model's predictive robustness. Compared to traditional methods, which have reported variability with errors ranging from 6 to 18 months, the AI model demonstrated a reduction in inter-operator variability and improved reliability. Conclusion The implementation of AI in bone age assessment offers a more standardized, rapid, and precise alternative to conventional methods. By improving the accuracy and efficiency of skeletal maturity evaluations, AI has significant implications for pediatric orthopedic surgery, optimizing treatment timing and expanding access to high-quality bone age assessments. Further validation studies are needed to ensure clinical applicability across diverse patient populations.

Bone Age Assessment Using Various Medical Imaging Techniques Enhanced by Artificial Intelligence

Bone age (BA) reflects skeletal maturity and is crucial in clinical and forensic contexts, particularly for growth assessment, adult height prediction, and managing conditions like short stature and precocious puberty, often using X-ray, MRI, CT, or ultrasound imaging. Traditional BA assessment methods, including the Greulich-Pyle and Tanner-Whitehouse techniques, compare morphological changes to reference atlases. Despite their effectiveness, factors like genetics and environment complicate evaluations, emphasizing the need for new methods that account for comprehensive variations in skeletal maturity. The limitations of classical BA assessment methods increase the demand for automated solutions. The first automated tool, HANDX, was introduced in 1989. Researchers now focus on developing reliable artificial intelligence (AI)-driven tools, utilizing machine learning and deep learning techniques to improve accuracy and efficiency in BA evaluations, addressing traditional methods' shortcomings. Recent reviews on BA assessment methods rarely compare AI-based approaches across imaging technologies. This article explores advancements in BA estimation, focusing on machine learning methods and their clinical implications while providing a historical context and highlighting each approach's benefits and limitations.

Association between bone age maturity and childhood adiposity

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.

Automated Distal Radius and Ulna Skeletal Maturity Grading from Hand Radiographs with an Attention Multi-Task Learning Method

Background: Assessment of skeletal maturity is a common clinical practice to investigate adolescent growth and endocrine disorders. The distal radius and ulna (DRU) maturity classification is a practical and easy-to-use scheme that was designed for adolescent idiopathic scoliosis clinical management and presents high sensitivity in predicting the growth peak and cessation among adolescents. However, time-consuming and error-prone manual assessment limits DRU in clinical application. Methods: In this study, we propose a multi-task learning framework with an attention mechanism for the joint segmentation and classification of the distal radius and ulna in hand X-ray images. The proposed framework consists of two sub-networks: an encoder-decoder structure with attention gates for segmentation and a slight convolutional network for classification. Results: With a transfer learning strategy, the proposed framework improved DRU segmentation and classification over the single task learning counterparts and previously reported methods, achieving an accuracy of 94.3% and 90.8% for radius and ulna maturity grading. Findings: Our automatic DRU assessment platform covers the whole process of growth acceleration and cessation during puberty. Upon incorporation into advanced scoliosis progression prognostic tools, clinical decision making will be potentially improved in the conservative and operative management of scoliosis patients.

Relationships between bone age, physical performance, and motor coordination among adolescent male and female athletes

Biological maturity significantly impacts youth athletes' physical performance throughout adolescence. However, how this differs between male and female youth athletes remains unclear. Thus, the present study aimed to assess associations between maturity, physical performance and motor coordination in females and males. Sixty-eight youth athletes (mean age 13.9 ± 0.8 years, 26 females) were included in the present study. Participants performed a 40 m sprint, standing long jump (SLJ), push-ups and a 2,000 m run. Motor coordination was evaluated using the short form of the Körperkoordinationstest für Kinder test. Bone age (BA), assessed by x-ray of the left hand and analyzed with an automated software, was used as a biomarker of biological maturity. Results showed that BA was significantly associated with performance for males on 40 m sprint (r = -.556, p < .001), SLJ (r = .500, p < .001) and 2,000 m run (r = -.435, p = .011). No associations were found between BA and physical performance among females, nor between BA and motor coordination for either females or males. In conclusion, maturity is associated with exercises that require maximal speed, explosive leg strength and endurance in males, but not in females, with maturity showing no impact on the motor coordination in either sex.

Performance of two different artificial intelligence (AI) methods for assessing carpal bone age compared to the standard Greulich and Pyle method

PURPOSE

Evaluate the agreement between bone age assessments conducted by two distinct machine learning system and standard Greulich and Pyle method.

MATERIALS AND METHODS

Carpal radiographs of 225 patients (mean age 8 years and 10 months, SD = 3 years and 1 month) were retrospectively analysed at two separate institutions (October 2018 and May 2022) by both expert radiologists and radiologists in training as well as by two distinct AI software programmes, 16-bit AItm and BoneXpert® in a blinded manner.

RESULTS

The bone age range estimated by the 16-bit AItm system in our sample varied between 1 year and 1 month and 15 years and 8 months (mean bone age 9 years and 5 months SD = 3 years and 3 months). BoneXpert® estimated bone age ranged between 8 months and 15 years and 7 months (mean bone age 8 years and 11 months SD = 3 years and 3 months). The average bone age estimated by the Greulich and Pyle method was between 11 months and 14 years, 9 months (mean bone age 8 years and 4 months SD = 3 years and 3 months). Radiologists' assessments using the Greulich and Pyle method were significantly correlated (Pearson's r > 0.80, p < 0.001). There was no statistical difference between BoneXpert® and 16-bit AItm (mean difference = - 0.19, 95%CI = (- 0.45; 0.08)), and the agreement between two measurements varies between - 3.45 (95%CI = (- 3.95; - 3.03) and 3.07 (95%CI - 3.03; 3.57).

CONCLUSIONS

Both AI methods and GP provide correlated results, although the measurements made by AI were closer to each other compared to the GP method.

Validation of an AI-Powered Automated X-ray Bone Age Analyzer in Chinese Children and Adolescents: A Comparison with the Tanner-Whitehouse 3 Method

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.

Doctor simulator: Delta-Age-Sex-AdaIn enhancing bone age assessment through AdaIn style transfer

BACKGROUND

Bone age assessment assists physicians in evaluating the growth and development of children. However, deep learning methods for bone age estimation do not currently incorporate differential features obtained through comparisons with other bone atlases.

OBJECTIVE

To propose a more accurate method, Delta-Age-Sex-AdaIn (DASA-net), for bone age assessment, this paper combines age and sex distribution through adaptive instance normalization (AdaIN) and style transfer, simulating the process of visually comparing hand images with a standard bone atlas to determine bone age.

MATERIALS AND METHODS

The proposed Delta-Age-Sex-AdaIn (DASA-net) consists of four modules: BoneEncoder, Binary code distribution, Delta-Age-Sex-AdaIn, and AgeDecoder. It is compared with state-of-the-art methods on both a public Radiological Society of North America (RSNA) pediatric bone age prediction dataset (14,236 hand radiographs, ranging from 1 to 228 months) and a private bone age prediction dataset from Zigong Fourth People's Hospital (474 hand radiographs, ranging from 12 to 218 months, 268 male). Ablation experiments were designed to demonstrate the necessity of incorporating age distribution and sex distribution.

RESULTS

The DASA-net model achieved a lower mean absolute deviation (MAD) of 3.52 months on the RSNA dataset, outperforming other methods such as BoneXpert, Deeplasia, BoNet, and other deep learning based methods. On the private dataset, the DASA-net model obtained a MAD of 3.82 months, which is also superior to other methods.

CONCLUSION

The proposed DASA-net model aided the model's learning of the distinctive characteristics of hand bones of various ages and both sexes by integrating age and sex distribution into style transfer.

Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion

Deep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation of osteoarthritis severity. The utilisation of DL is expected to increase, owing to its ability to present accurate diagnoses more efficiently than traditional methods in many scenarios. This reduces the time and cost of diagnosis for patients and orthopaedic surgeons. To our knowledge, no exclusive study has comprehensively reviewed all aspects of DL currently used in orthopaedic practice. This review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, and Web of Science between 2017 and 2023. The authors begin with the motivation for using DL in orthopaedics, including its ability to enhance diagnosis and treatment planning. The review then covers various applications of DL in orthopaedics, including fracture detection, detection of supraspinatus tears using MRI, osteoarthritis, prediction of types of arthroplasty implants, bone age assessment, and detection of joint-specific soft tissue disease. We also examine the challenges for implementing DL in orthopaedics, including the scarcity of data to train DL and the lack of interpretability, as well as possible solutions to these common pitfalls. Our work highlights the requirements to achieve trustworthiness in the outcomes generated by DL, including the need for accuracy, explainability, and fairness in the DL models. We pay particular attention to fusion techniques as one of the ways to increase trustworthiness, which have also been used to address the common multimodality in orthopaedics. Finally, we have reviewed the approval requirements set forth by the US Food and Drug Administration to enable the use of DL applications. As such, we aim to have this review function as a guide for researchers to develop a reliable DL application for orthopaedic tasks from scratch for use in the market.

Accuracy of automated forensic dental age estimation lab (F-DentEst Lab) on large Malaysian dataset

When a disaster occurs, the authority must prioritise two things. First, the search and rescue of lives, and second, the identification and management of deceased individuals. However, with thousands of dead bodies to be individually identified in mass disasters, forensic teams face challenges such as long working hours resulting in a delayed identification process and a public health concern caused by the decomposition of the body. Using dental panoramic imaging, teeth have been used in forensics as a physical marker to estimate the age of an individual. Traditionally, dental age estimation has been performed manually by experts. Although the procedure is fairly simple, the large number of victims and the limited amount of time available to complete the assessment during large-scale disasters make forensic work even more challenging. The emergence of artificial intelligence (AI) in the fields of medicine and dentistry has led to the suggestion of automating the current process as an alternative to the conventional method. This study aims to test the accuracy and performance of the developed deep convolutional neural network system for age estimation in large, out-of-sample Malaysian children dataset using digital dental panoramic imaging. Forensic Dental Estimation Lab (F-DentEst Lab) is a computer application developed to perform the dental age estimation digitally. The introduction of this system is to improve the conventional method of age estimation that significantly increase the efficiency of the age estimation process based on the AI approach. A total number of one-thousand-eight-hundred-and-ninety-two digital dental panoramic images were retrospectively collected to test the F-DentEst Lab. Data training, validation, and testing have been conducted in the early stage of the development of F-DentEst Lab, where the allocation involved 80 % training and the remaining 20 % for testing. The methodology was comprised of four major steps: image preprocessing, which adheres to the inclusion criteria for panoramic dental imaging, segmentation, and classification of mandibular premolars using the Dynamic Programming-Active Contour (DP-AC) method and Deep Convolutional Neural Network (DCNN), respectively, and statistical analysis. The suggested DCNN approach underestimated chronological age with a small ME of 0.03 and 0.05 for females and males, respectively.

Determination of growth and developmental stages in hand-wrist radiographs : Can fractal analysis in combination with artificial intelligence be used?

PURPOSE

The goal of this work was to assess the classification of maturation stage using artificial intelligence (AI) classifiers.

METHODS

Hand-wrist radiographs (HWRs) from 1067 individuals aged between 7 and 18 years were included. Fifteen regions of interest were selected for fractal dimension (FD) analysis. Five predictive models with different inputs were created (model 1: only FD; model 2: FD and Chapman sesamoid stage; model 3: FD, age, and sex; model 4: FD, Chapman sesamoid stage, age, and sex; model 5: Chapman sesamoid stage, age, and sex). The target diagnoses were accelerating growth velocity, very high growth velocity, and decreasing growth velocity. Four AI algorithms were applied: multilayer perceptron (MLP), support vector machine (SVM), gradient boosting machine (GBM) and C 5.0 decision tree classifier.

RESULTS

All AI algorithms except for C 5.0 yielded similar overall predictive accuracies for the five models. In order from lowest to highest, the predictive accuracies of the models were as follows: model 1 < model 3 < model 2 < model 5 < model 4. The highest overall F1 score, which was used instead of accuracy especially for models with unbalanced data, was obtained for models 1, 2, and 3 based on SVM, for model 4 based on MLP, and for model 5 based on C 5.0. Adding Chapman sesamoid stage, chronologic age, and sex as additional inputs to the FD values significantly increased the F1 score.

CONCLUSION

Applying FD analysis to HWRs is not sufficient to predict maturation stage in growing patients but can be considered a growth rate prediction method if combined with the Chapman sesamoid stage, age, and sex.

Statistical shape modeling of the geometric morphology of the canine femur, tibia, and patella

Bone morphometry varies among dogs of different sizes and breeds. Studying these differences may help understand the predisposition of certain breeds for specific orthopedic pathologies. This study aimed to develop a statistical shape model (SSM) of the femur, patella, and tibia of dogs without any clinical orthopeadic abnormalities to analyze and compare morphological variations based on body weight and breed. A total of 97 CT scans were collected from different facilities and divided based on breed and body weight. The 3D models of the bones were obtained and aligned to a coordinate system. The SSM was created using principal component analysis (PCA) to analyze shape variations. The study found that the first few modes of variation accounted for a significant percentage of the total variation, with size/scale being the most prominent factor. The results provide valuable insights into normal anatomical variations and can be used for future research in understanding pathological bone morphologies and developing 3D imaging algorithms in veterinary medicine.

Norwegian male U14 soccer players have superior running capacity compared to Icelandic players

The organisation and development strategies of youth soccer differ between Norway and Iceland. Whether this affect physical capacity is unknown. Thus, the first aim of the present study is to compare physical capacity between players from Iceland and Norway. Secondary aim is to assess associations between biological maturity and physical capacity in the Icelandic players since an association previously has been shown among the Norwegians. There were 48 U14 players from Iceland included and 103 players from Norway. Bone age (BA), measured with left-wrist x-ray, was used as an indicator of biological maturity. To measure physical capacity, 40 metre (m) linear sprint, standing long jump (SLJ), countermovement jump (CMJ), the Yo-Yo intermittent recovery test (IR1-test) and a maximal oxygen uptake test (VO2max) were used. Training load was assessed by questionnaire. The results showed that the Norwegian players ran faster (5.90 ± 0.38 vs. 6.37 ± 0.44 s, p < .001), had better intermittent endurance capacity (1,235 ± 461 vs. 960 ± 423 m, p < .001) and higher VO2max, (60.3 ± 6.5 vs. 54.8 ± 5.3 ml·kg-1·min-1, p < .001) than the Icelandic players. The players from Norway reported a higher number of weekly organised soccer training hours than the Icelandic. We also found significant correlations between BA and performance on 40 m linear sprint (r = -.566, p < .001), SLJ (r = .380, p = .008) and CMJ (r = .354, p = .014) among the Icelandic players. Moreover, no correlations were found between BA and VO2max or intermittent endurance capacity. In conclusion, the Norwegian players ran faster and had better VO2max and intermittent endurance capacity than the Icelandic players. Biological maturity level was associated with speed and jumping performance in U14 soccer players in Iceland, but not with VO2max or intermittent endurance capacity. Findings indicate that more research is needed to investigate the influence of different organisation and structure of youth soccer between the two countries on physical capacity.

Applicability and robustness of an artificial intelligence-based assessment for Greulich and Pyle bone age in a German cohort

PURPOSE

The determination of bone age (BA) based on the hand and wrist, using the 70-year-old Greulich and Pyle (G&P) atlas, remains a widely employed practice in various institutions today. However, a more recent approach utilizing artificial intelligence (AI) enables automated BA estimation based on the G&P atlas. Nevertheless, AI-based methods encounter limitations when dealing with images that deviate from the standard hand and wrist projections. Generally, the extent to which BA, as determined by the G&P atlas, corresponds to the chronological age (CA) of a contemporary German population remains a subject of continued discourse. This study aims to address two main objectives. Firstly, it seeks to investigate whether the G&P atlas, as applied by the AI software, is still relevant for healthy children in Germany today. Secondly, the study aims to assess the performance of the AI software in handling non-strict posterior-anterior (p. a.) projections of the hand and wrist.

MATERIALS AND METHODS

The AI software retrospectively estimated the BA in children who had undergone radiographs of a single hand using posterior-anterior and oblique planes. The primary purpose was to rule out any osseous injuries. The prediction error of BA in relation to CA was calculated for each plane and between the two planes.

RESULTS

A total of 1253 patients (aged 3 to 16 years, median age 10.8 years, 55.7 % male) were included in the study. The average error of BA in posterior-anterior projections compared to CA was 3.0 (± 13.7) months for boys and 1.7 (± 13.7) months for girls. Interestingly, the deviation from CA tended to be even slightly lower in oblique projections than in posterior-anterior projections. The mean error in the posterior-anterior projection plane was 2.5 (± 13.7) months, while in the oblique plane it was 1.8 (± 13.9) months (p = 0.01).

CONCLUSION

The AI software for BA generally corresponds to the age of the contemporary German population under study, although there is a noticeable prediction error, particularly in younger children. Notably, the software demonstrates robust performance in oblique projections.

KEY POINTS

· Bone age, as determined by artificial intelligence, aligns with the chronological age of the contemporary German cohort under study.. · As determined by artificial intelligence, bone age is remarkably robust, even when utilizing oblique X-ray projections..

CITATION FORMAT

· Pape J, Hirsch F, Deffaa O et al. Applicability and robustness of an artificial intelligence-based assessment for Greulich and Pyle bone age in a German cohort. Fortschr Röntgenstr 2024; 196: 600 - 606.

Deep learning-based identification of spine growth potential on EOS radiographs

OBJECTIVES

To develop an automatic computer-based method that can help clinicians in assessing spine growth potential based on EOS radiographs.

METHODS

We developed a deep learning-based (DL) algorithm that can mimic the human judgment process to automatically determine spine growth potential and the Risser sign based on full-length spine EOS radiographs. A total of 3383 EOS cases were collected and used for the training and test of the algorithm. Subsequently, the completed DL algorithm underwent clinical validation on an additional 440 cases and was compared to the evaluations of four clinicians.

RESULTS

Regarding the Risser sign, the weighted kappa value of our DL algorithm was 0.933, while that of the four clinicians ranged from 0.909 to 0.930. In the assessment of spine growth potential, the kappa value of our DL algorithm was 0.944, while the kappa values of the four clinicians were 0.916, 0.934, 0.911, and 0.920, respectively. Furthermore, our DL algorithm obtained a slightly higher accuracy (0.973) and Youden index (0.952) compared to the best values achieved by the four clinicians. In addition, the speed of our DL algorithm was 15.2 ± 0.3 s/40 cases, much faster than the inference speeds of the clinicians, ranging from 177.2 ± 28.0 s/40 cases to 241.2 ± 64.1 s/40 cases.

CONCLUSIONS

Our algorithm demonstrated comparable or even better performance compared to clinicians in assessing spine growth potential. This stable, efficient, and convenient algorithm seems to be a promising approach to assist doctors in clinical practice and deserves further study.

CLINICAL RELEVANCE STATEMENT

This method has the ability to quickly ascertain the spine growth potential based on EOS radiographs, and it holds promise to provide assistance to busy doctors in certain clinical scenarios.

KEY POINTS

• In the clinic, there is no available computer-based method that can automatically assess spine growth potential. • We developed a deep learning-based method that could automatically ascertain spine growth potential. • Compared with the results of the clinicians, our algorithm got comparable results.

Use of artificial intelligence in determination of bone age of the healthy individuals: A scoping review

BACKGROUND

Bone age assessment, as an indicator of biological age, is widely used in orthodontics and pediatric endocrinology. Owing to significant subject variations in the manual method of assessment, artificial intelligence (AI), machine learning (ML), and deep learning (DL) play a significant role in this aspect. A scoping review was conducted to search the existing literature on the role of AI, ML, and DL in skeletal age or bone age assessment in healthy individuals.

METHODS

A literature search was conducted in PubMed, Scopus, and Web of Science from January 2012 to December 2022 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Extension for Scoping Reviews (PRISMA-ScR) and Joanna Briggs Institute guidelines. Grey literature was searched using Google Scholar and OpenGrey. Hand-searching of the articles in all the reputed orthodontic journals and the references of the included articles were also searched for relevant articles for the present scoping review.

RESULTS

Nineteen articles that fulfilled the inclusion criteria were included. Ten studies used skeletal maturity indicators based on hand and wrist radiographs, two studies used magnetic resonance imaging and seven studies used cervical vertebrae maturity indicators based on lateral cephalograms to assess the skeletal age of the individuals. Most of these studies were published in non-orthodontic medical journals. BoneXpert automated software was the most commonly used software, followed by DL models, and ML models in the studies for assessment of bone age. The automated method was found to be as reliable as the manual method for assessment.

CONCLUSIONS

This scoping review validated the use of AI, ML, or DL in bone age assessment of individuals. A more uniform distribution of sufficient samples in different stages of maturation, use of three-dimensional inputs such as magnetic resonance imaging, and cone beam computed tomography is required for better training of the models to generalize the outputs for use in the target population.

AI applications in musculoskeletal imaging: a narrative review

This narrative review focuses on clinical applications of artificial intelligence (AI) in musculoskeletal imaging. A range of musculoskeletal disorders are discussed using a clinical-based approach, including trauma, bone age estimation, osteoarthritis, bone and soft-tissue tumors, and orthopedic implant-related pathology. Several AI algorithms have been applied to fracture detection and classification, which are potentially helpful tools for radiologists and clinicians. In bone age assessment, AI methods have been applied to assist radiologists by automatizing workflow, thus reducing workload and inter-observer variability. AI may potentially aid radiologists in identifying and grading abnormal findings of osteoarthritis as well as predicting the onset or progression of this disease. Either alone or combined with radiomics, AI algorithms may potentially improve diagnosis and outcome prediction of bone and soft-tissue tumors. Finally, information regarding appropriate positioning of orthopedic implants and related complications may be obtained using AI algorithms. In conclusion, rather than replacing radiologists, the use of AI should instead help them to optimize workflow, augment diagnostic performance, and keep up with ever-increasing workload.Relevance statement This narrative review provides an overview of AI applications in musculoskeletal imaging. As the number of AI technologies continues to increase, it will be crucial for radiologists to play a role in their selection and application as well as to fully understand their potential value in clinical practice. Key points • AI may potentially assist musculoskeletal radiologists in several interpretative tasks.• AI applications to trauma, age estimation, osteoarthritis, tumors, and orthopedic implants are discussed.• AI should help radiologists to optimize workflow and augment diagnostic performance.

Evaluation of height prediction models: from traditional methods to artificial intelligence

BACKGROUND

Traditional methods for predicting adult height (AHP) rely on manual readings of bone age (BA). However, the incorporation of artificial intelligence has recently improved the accuracy of BA readings and their incorporation into AHP models.

METHODS

This study aimed to identify the AHP model that fits the current average height for adults in Mexico. Using a cross-sectional design, the study included 1173 participants (5-18 yr). BA readings were done by two experts (manually) and with an automated method (BoneXpert®). AHP was carried out using both traditional and automated methods. The best AHP model was the one that was closest to the population mean.

RESULTS

All models overestimated the population mean (males: 0.7-6.7 cm, females: 0.9-3.7 cm). The AHP models with the smallest difference were BoneXpert for males and Bayley & Pinneau for females. However, the manual readings of BA showed significant interobserver variability (up to 43% of predictions between observers exceeded 5 cm using the Bayley & Pinneau method).

CONCLUSION

Traditional AHP models relying on manual BA readings have high interobserver variability. Therefore, BoneXpert is the most reliable option, reducing such variability and providing AHP models that remain close to the mean population height.

IMPACT

Traditional models for predicting adult height often result in overestimated height predictions. The manual reading of bone age is prone to interobserver variability, which can introduce significant biases in the prediction of adult height. The BoneXpert method minimizes the variability associated with traditional methods and demonstrates consistent results in relation to the average height of the population. This study is the first to assess adult height prediction models specifically in the current generations of Mexican children.

BoneXpert-derived bone health index reference curves constructed on healthy Indian children and adolescents

BACKGROUND

Artificial intelligence (AI)-based applications for the assessment of the paediatric musculoskeletal system like BoneXpert are not only useful to assess bone age (BA) but also to provide a bone health index (BHI) and a standard deviation score (SDS) for both. This allows comparison of the BHI with age- and sex-matched healthy Caucasian children.

OBJECTIVE

We conducted this study with the objective of generating BHI curves using BoneXpert in healthy Indian children with BA between 2 and 17 years.

METHOD

We retrospectively reviewed anthropometric parameters, BHI, and BHI SDS data of digitalized left-hand radiographs (joint photographic experts group [jpg] format) of a cohort of 788 paediatric patients from a previous study to which they were recruited to compare various methods of BA assessment. The recruited children represented all age groups for both sexes. The corrected BHI for jpg images was calculated using the formula corrected BHI=BHI*(stature/(avL*50))^0.33333 where stature is height of subject and avL is average length of metacarpal bones. The reference Indian BHI curves and centiles were generated using the Lambda-Mu-Sigma method.

RESULT

The mean BHI and BHI SDS of the study group were 4.02±0.57 and -1.73±1.09, respectively. The average increase in median BHI from each age group was between 2.5% and 3% in both sexes up to age of 14 years after which it increased to 4.5% to 5%. The mean BHI of Indian children was lower than that of Caucasian children with maximum differences noted in boys at 16 years (21.7%) and girls at 14 years (16%). We report 8.4% SD of BHI for our study sample. Reference percentile curves for BHI according to BA were derived separately for boys and girls.

CONCLUSION

Reference data has been provided for the screening of bone health status of Indian children and adolescents.

Deeplasia: deep learning for bone age assessment validated on skeletal dysplasias

BACKGROUND

Skeletal dysplasias collectively affect a large number of patients worldwide. Most of these disorders cause growth anomalies. Hence, evaluating skeletal maturity via the determination of bone age (BA) is a useful tool. Moreover, consecutive BA measurements are crucial for monitoring the growth of patients with such disorders, especially for timing hormonal treatment or orthopedic interventions. However, manual BA assessment is time-consuming and suffers from high intra- and inter-rater variability. This is further exacerbated by genetic disorders causing severe skeletal malformations. While numerous approaches to automate BA assessment have been proposed, few are validated for BA assessment on children with skeletal dysplasias.

OBJECTIVE

We present Deeplasia, an open-source prior-free deep-learning approach designed for BA assessment specifically validated on patients with skeletal dysplasias.

MATERIALS AND METHODS

We trained multiple convolutional neural network models under various conditions and selected three to build a precise model ensemble. We utilized the public BA dataset from the Radiological Society of North America (RSNA) consisting of training, validation, and test subsets containing 12,611, 1,425, and 200 hand and wrist radiographs, respectively. For testing the performance of our model ensemble on dysplastic hands, we retrospectively collected 568 radiographs from 189 patients with molecularly confirmed diagnoses of seven different genetic bone disorders including achondroplasia and hypochondroplasia. A subset of the dysplastic cohort (149 images) was used to estimate the test-retest precision of our model ensemble on longitudinal data.

RESULTS

The mean absolute difference of Deeplasia for the RSNA test set (based on the average of six different reference ratings) and dysplastic set (based on the average of two different reference ratings) were 3.87 and 5.84 months, respectively. The test-retest precision of Deeplasia on longitudinal data (2.74 months) is estimated to be similar to a human expert.

CONCLUSION

We demonstrated that Deeplasia is competent in assessing the age and monitoring the development of both normal and dysplastic bones.

Skeletal age estimation of living adolescents and young adults: A pilot study on conventional radiography versus magnetic resonance imaging and staging technique versus atlas method

OBJECTIVE

To compare conventional radiography (CR) and magnetic resonance imaging (MRI) of the left hand/wrist and both clavicles for forensic age estimation of adolescents and young adults.

MATERIALS AND METHODS

CR and MRI were prospectively conducted in 108 healthy Caucasian volunteers (52 males, 56 females) aged 16 to 21 years. Skeletal development was assessed by allocating stages (wrist, clavicles) and atlas standards (hand/wrist). Inter- and intra-observer agreements were quantified using linear weighted Cohen's kappa, and descriptive statistics regarding within-stage/standard age distributions were reported.

RESULTS

Inter- and intra-observer agreements for hand/wrist CR (staging technique: 0.840-0.871 and 0.877-0.897, respectively; atlas method: 0.636-0.947 and 0.853-0.987, respectively) and MRI (staging technique: 0.890-0.932 and 0.897-0.952, respectively; atlas method: 0.854-0.941 and 0.775-0.978, respectively) were rather similar. The CR atlas method was less reproducible than the staging technique. Inter- and intra-observer agreements for clavicle CR (0.590-0.643 and 0.656-0.770, respectively) were lower than those for MRI (0.844-0.852 and 0.866-0.931, respectively). Furthermore, although shifted, wrist CR and MRI within-stage age distribution spread were similar, as were those between staging techniques and atlas methods. The possibility to apply (profound) substages to clavicle MRI rendered a more gradual increase of age distributions with increasing stages, compared to CR.

CONCLUSIONS

For age estimation based on the left hand/wrist and both clavicles, reference data should be considered anatomical structure- and imaging modality-specific. Moreover, CR is adequate for hand/wrist evaluation and a wrist staging technique seems to be more useful than an atlas method. By contrast, MRI is of added value for clavicle evaluation.

Zoledronate Increases Bone Mineral Density in Nonambulant Children With Cerebral Palsy: A Randomized Controlled Trial

CONTEXT

Zoledronate appears to reduce fracture rates in children with cerebral palsy (CP), but no previous randomized, controlled trial has been performed to compare the effect of zoledronate to placebo in children with CP.

OBJECTIVE

To investigate the effect of zoledronate on bone mineral density (BMD) Z-scores in children with nonambulant CP in a randomized, controlled, double-blind trial.

METHODS

Nonambulant children with CP (5 to 16 years of age) were randomized 1:1 to receive 2 doses of zoledronate or placebo at a 6-month interval. BMD Z-score changes at the lumbar spine and the lateral distal femur (LDF) were calculated from dual-energy x-ray absorptiometry scans. Monitoring included weight, bone age, pubertal staging, knee-heel length, adverse events, biochemical markers, and questionnaires.

RESULTS

Twenty-four participants were randomized and all completed the study. Fourteen were assigned to zoledronate. The mean lumbar spine BMD Z-score increased 0.8 SD (95% CI: 0.4; 1.2) in the zoledronate group, which was significant when compared to 0.0 SD (95% CI: -0.3; 0.3) in the placebo group. Similarly, the LDF BMD Z-scores increased more in the zoledronate group. Severe acute phase symptoms affected 50% of the patients in the zoledronate group but were reported exclusively after the first dose. Growth parameters were similar in both groups.

CONCLUSION

Zoledronate for 12 months increased BMD Z-scores significantly without affecting growth, but first-dose side effects were common and considerable. Studies into lower first doses and long-term outcomes are needed.

Longitudinal assessment of bone health index as a measure of bone health in short-statured children before and during treatment with recombinant growth hormone

OBJECTIVES

The aim of our study was the longitudinal assessment of bone health index (BHI) in short-statured children during growth hormone (GH) treatment to estimate changes in their bone health.

METHODS

256 short-statured children (isolated GH deficiency (IGHD) n=121, multiple pituitary hormone deficiency (MPHD) n=49, intrauterine growth retardation (small for gestational age (SGA)) n=52, SHOX (short stature homeobox gene) deficiency n=9, Ullrich Turner syndrome (UTS) n=25) who started with GH between 2010 and 2018 were included. Annual bone ages (Greulich and Pyle, GP) and BHI were, retrospectively, analysed in consecutive radiographs of the left hand (BoneXpert software) from GH therapy start (T0) up to 10 years (T10) thereafter, with T max indicating the individual time point of the last available radiograph. The results are presented as the median (25 %/75 % interquartile ranges, IQR) and statistical analyses were performed using non-parametric tests as appropriate.

RESULTS

The BHI standard deviation scores (SDS) were reduced (-0.97, -1.8/-0.3) as bone ages were retarded (-1.6 years, -2.31/-0.97) in all patients before start of GH and were significantly lower in patients with growth hormone deficiency (GHD) (-1.04, -1.85/-0.56; n=170) compared to non-GHD patients (-0.79, -1.56/-0.01; n=86; p=0.022). BHI SDS increased to -0.17 (-1/0.58) after 1 year of GH (T1, 0.5-1.49, p<0.001) and to -0.20 (-1/-0.50, p<0.001) after 5.3 years (T max, 3.45/7.25).

CONCLUSIONS

BHI SDS are reduced in treatment-naive short-statured children regardless of their GH status, increase initially with GH treatment while plateauing thereafter, suggesting sustained improved bone health.

Oral sialic acid supplementation in NANS-CDG: Results of a single center, open-label, observational pilot study

NANS-CDG is a congenital disorder of glycosylation (CDG) caused by biallelic variants in NANS, encoding an essential enzyme in de novo sialic acid synthesis. It presents with intellectual developmental disorder (IDD), skeletal dysplasia, neurologic impairment, and gastrointestinal dysfunction. Some patients suffer progressive intellectual neurologic deterioration (PIND), emphasizing the need for a therapy. In a previous study, sialic acid supplementation in knockout nansa zebrafish partially rescued skeletal abnormalities. Here, we performed the first in-human pre- and postnatal sialic-acid study in NANS-CDG. In this open-label observational study, 5 patients with NANS-CDG (range 0-28 years) were treated with oral sialic acid for 15 months. The primary outcome was safety. Secondary outcomes were psychomotor/cognitive testing, height and weight, seizure control, bone health, gastrointestinal symptoms, and biochemical and hematological parameters. Sialic acid was well tolerated. In postnatally treated patients, there was no significant improvement. For the prenatally treated patient, psychomotor and neurologic development was better than two other genotypically identical patients (one treated postnatally, one untreated). The effect of sialic acid treatment may depend on the timing, with prenatal treatment potentially benefiting neurodevelopmental outcomes. Evidence is limited, however, and longer-term follow-up in a larger number of prenatally treated patients is required.

The promise and limitations of artificial intelligence in musculoskeletal imaging

With the recent developments in deep learning and the rapid growth of convolutional neural networks, artificial intelligence has shown promise as a tool that can transform several aspects of the musculoskeletal imaging cycle. Its applications can involve both interpretive and non-interpretive tasks such as the ordering of imaging, scheduling, protocoling, image acquisition, report generation and communication of findings. However, artificial intelligence tools still face a number of challenges that can hinder effective implementation into clinical practice. The purpose of this review is to explore both the successes and limitations of artificial intelligence applications throughout the muscuskeletal imaging cycle and to highlight how these applications can help enhance the service radiologists deliver to their patients, resulting in increased efficiency as well as improved patient and provider satisfaction.

Precocious Puberty Diagnoses Spike, COVID-19 Pandemic, and Body Mass Index: Findings From a 4-year Study

Context

Since the COVID-19 outbreak, the number of girls with suspected precocious puberty has increased.

Objective

To compare the incidence of idiopathic central precocious puberty (ICPP) during COVID-19 with that of the previous 4 years.

Methods

Anthropometric, biochemical, and radiological parameters were collected between January 2016 and June 2021 from 133 girls who met the Rapidly Progressive ICPP criteria (RP-ICPP).

Results

We found a higher incidence of RP-ICPP between March 2020 and June 2021 (group 2) compared with January 2016 through March 2020 (group 1) (53.5% vs 41.1%); 2021 showed the highest annual incidence (P < .05). Group 1 and group 2 differed in age at diagnosis (7.96 ± 0.71 vs 7.61 ± 0.94; P < .05), mean Tanner stage (2.86 ± 0.51 vs 2.64 ± 0; P < .05), and in the time between the appearance of thelarche and diagnosis (0.93 ± 0.75 vs 0.71 ± 0.62 years, P < .05). There was an increase in the number of girls aged <8 years in group 2 and a significantly higher number of girls aged >8 years was found in group 1 (42 in group 1 vs 20 in group 2, P < 0.05). Overall body mass index SD score showed higher values ​​in group 2 (1.01 ± 1.23 vs 0.69 ± 1.15; P = .18), which spent an average of 1.94 ± 1.81 hours per day using electronic devices; 88.5% of this group stopped any physical activity.

Conclusions

A spike in new diagnoses of idiopathic (1.79-fold higher) and RP-CPP coincided with the COVID-19 pandemic. The incidence of RP-ICPP was 1.3-fold higher during COVID-19 with a trend toward an increase in body mass index SD score. The expanding use of digital devices and the reduction of daily physical activity represent possible risk factors.

Comparison of Different Bone Age Methods and Chronological Age in Prediction of Remaining Growth Around the Knee

BACKGROUND

Bone age (BA) has been shown to be superior to chronological age (CA) when predicting remaining growth. However, it is not known whether the calculations are more accurate when BA is assessed by the Greulich and Pyle (GP) or the Sauvegrain (SG) methods. The aim of our study was to identify the method which gives an estimate closest to actual growth in the lower extremities.

METHODS

Leg length radiographs, hand radiographs, and elbow radiographs were simultaneously obtained during the adolescent growth spurt (10 to 16 years) in 52 children treated for LLD, with radiographic follow-up of segmental length (femur, tibia, and foot) until skeletal maturity, were randomly selected from a local institutional register. BA, according to GP and SG, were manually rated, and BA based on the GP method was additionally assessed by the automated BoneXpert (BX) method. The remaining growth was calculated based on the White-Menelaus method for both BA methods (GP, SG), the combination of the 2 methods, GP by BX, CA, and the combination of CA and GP by BX. Estimated growth was compared with the actual growth in the distal femur and proximal tibia from the time of BA determination until skeletal maturity.

RESULTS

For all included methods, the average calculated remaining growth was higher compared with the actual growth. The mean absolute difference between calculated remaining growth and actual growth in the femur and tibia was lowest using GP by BX [0.66 cm (SD 0.51 cm) and 0.43 cm (SD 0.34 cm)] and highest using CA [1.02 (SD 0.72) and 0.67 (SD 0.46)]. It was a significant association between calculated growth and the difference between actual and calculated growth for the SG method ( P =<0.001).

CONCLUSION

During the adolescent growth spurt, the GP method compared with the SG method and CA gives the most accurate estimate of remaining growth around the knee according to our results.

CLINICAL RELEVANCE

In calculations of remaining growth around the knee, BA assessment by the GP atlas or BX method should be used as the parameter of biological maturity.

Efficacy and Safety of Leuprolide Acetate 6-Month Depot for the Treatment of Central Precocious Puberty: A Phase 3 Study

Context

Treatment options for central precocious puberty (CPP) are important for individualization of therapy.

Objective

We evaluated the efficacy and safety of 6-month 45-mg leuprolide acetate (LA) depot with intramuscular administration.

Methods

LA depot was administered at weeks 0 and 24 to treatment-naïve (n = 27) or previously treated (n = 18) children with CPP in a phase 3, multicenter, single-arm, open-label study (NCT03695237). Week 24 peak-stimulated luteinizing hormone (LH) suppression (<4 mIU/mL) was the primary outcome. Secondary/other outcomes included basal sex hormone suppression (girls, estradiol <20 pg/mL; boys, testosterone <30 ng/dL), suppression of physical signs, height velocity, bone age, patient/parent-reported outcomes, and adverse events.

Results

All patients (age, 7.8 ± 1.27 years) received both scheduled study doses. At 24 weeks, 39/45 patients (86.7%) had LH suppressed. Six were counted as unsuppressed; 2 because of missing data, 3 with LH of 4.35-5.30 mIU/mL and 1 with LH of 21.07 mIU/mL. Through 48 weeks, LH, estradiol, and testosterone suppression was achieved in ≥86.7%, ≥97.4%, and 100%, respectively (as early as week 4 for LH and estradiol and week 12 for testosterone). Physical signs were suppressed at week 48 (girls, 90.2%; boys, 75.0%). Mean height velocity ranged 5.0 to 5.3 cm/year post-baseline in previously treated patients and declined from 10.1 to 6.5 cm/year at week 20 in treatment-naïve patients. Mean bone age advanced slower than chronological age. Patient/parent-reported outcomes remained stable. No new safety signals were identified. No adverse event led to treatment discontinuation.

Conclusion

Six-month intramuscular LA depot demonstrated 48-week efficacy with a safety profile consistent with other GnRH agonist formulations.

An Automated TW3-RUS Bone Age Assessment Method with Ordinal Regression-Based Determination of Skeletal Maturity

The assessment of bone age is important for evaluating child development, optimizing the treatment for endocrine diseases, etc. And the well-known Tanner-Whitehouse (TW) clinical method improves the quantitative description of skeletal development based on setting up a series of distinguishable stages for each bone individually. However, the assessment is affected by rater variability, which makes the assessment result not reliable enough in clinical practice. The main goal of this work is to achieve a reliable and accurate skeletal maturity determination by proposing an automated bone age assessment method called PEARLS, which is based on the TW3-RUS system (analysis of the radius, ulna, phalanges, and metacarpal bones). The proposed method comprises the point estimation of anchor (PEA) module for accurately localizing specific bones, the ranking learning (RL) module for producing a continuous stage representation of each bone by encoding the ordinal relationship between stage labels into the learning process, and the scoring (S) module for outputting the bone age directly based on two standard transform curves. The development of each module in PEARLS is based on different datasets. Finally, corresponding results are presented to evaluate the system performance in localizing specific bones, determining the skeletal maturity stage, and assessing the bone age. The mean average precision of point estimation is 86.29%, the average stage determination precision is 97.33% overall bones, and the average bone age assessment accuracy is 96.8% within 1 year for the female and male cohorts.

Automated Skeletal Bone Age Assessment with Two-Stage Convolutional Transformer Network Based on X-ray Images

Human skeletal development is continuous and staged, and different stages have various morphological characteristics. Therefore, bone age assessment (BAA) can accurately reflect the individual's growth and development level and maturity. Clinical BAA is time consuming, highly subjective, and lacks consistency. Deep learning has made considerable progress in BAA in recent years by effectively extracting deep features. Most studies use neural networks to extract global information from input images. However, clinical radiologists are highly concerned about the ossification degree in some specific regions of the hand bones. This paper proposes a two-stage convolutional transformer network to improve the accuracy of BAA. Combined with object detection and transformer, the first stage mimics the bone age reading process of the pediatrician, extracts the hand bone region of interest (ROI) in real time using YOLOv5, and proposes hand bone posture alignment. In addition, the previous information encoding of biological sex is integrated into the feature map to replace the position token in the transformer. The second stage extracts features within the ROI by window attention, interacts between different ROIs by shifting the window attention to extract hidden feature information, and penalizes the evaluation results using a hybrid loss function to ensure its stability and accuracy. The proposed method is evaluated on the data from the Pediatric Bone Age Challenge organized by the Radiological Society of North America (RSNA). The experimental results show that the proposed method achieves a mean absolute error (MAE) of 6.22 and 4.585 months on the validation and testing sets, respectively, and the cumulative accuracy within 6 and 12 months reach 71% and 96%, respectively, which is comparable to the state of the art, markedly reducing the clinical workload and realizing rapid, automatic, and high-precision assessment.

Multi-Branch Attention Learning for Bone Age Assessment with Ambiguous Label

Bone age assessment (BAA) is a typical clinical technique for diagnosing endocrine and metabolic diseases in children's development. Existing deep learning-based automatic BAA models are trained on the Radiological Society of North America dataset (RSNA) from Western populations. However, due to the difference in developmental process and BAA standards between Eastern and Western children, these models cannot be applied to bone age prediction in Eastern populations. To address this issue, this paper collects a bone age dataset based on the East Asian populations for model training. Nevertheless, it is laborious and difficult to obtain enough X-ray images with accurate labels. In this paper, we employ ambiguous labels from radiology reports and transform them into Gaussian distribution labels of different amplitudes. Furthermore, we propose multi-branch attention learning with ambiguous labels network (MAAL-Net). MAAL-Net consists of a hand object location module and an attention part extraction module to discover the informative regions of interest (ROIs) based only on image-level labels. Extensive experiments on both the RSNA dataset and the China Bone Age (CNBA) dataset demonstrate that our method achieves competitive results with the state-of-the-arts, and performs on par with experienced physicians in children's BAA tasks.

High performance for bone age estimation with an artificial intelligence solution

PURPOSE

The purpose of this study was to compare the performance of an artificial intelligence (AI) solution to that of a senior general radiologist for bone age assessment.

MATERIAL AND METHODS

Anteroposterior hand radiographs of eight boys and eight girls from each age interval between five and 17 year-old from four different radiology departments were retrospectively collected. Two board-certified pediatric radiologists with knowledge of the sex and chronological age of the patients independently estimated the Greulich and Pyle bone age to determine the standard of reference. A senior general radiologist not specialized in pediatric radiology (further referred to as "the reader") then determined the bone age with knowledge of the sex and chronological age. The results of the reader were then compared to those of the AI solution using mean absolute error (MAE) in age estimation.

RESULTS

The study dataset included a total of 206 patients (102 boys of mean chronological age of 10.9 ± 3.7 [SD] years, 104 girls of mean chronological age of 11 ± 3.7 [SD] years). For both sexes, the AI algorithm showed a significantly lower MAE than the reader (P < 0.007). In boys, the MAE was 0.488 years (95% confidence interval [CI]: 0.28-0.44; r² = 0.978) for the AI algorithm and 0.771 years (95% CI: 0.64-0.90; r² = 0.94) for the reader. In girls, the MAE was 0.494 years (95% CI: 0.41-0.56; r² = 0.973) for the AI algorithm and 0.673 years (95% CI: 0.54-0.81; r² = 0.934) for the reader.

CONCLUSION

The AI solution better estimates the Greulich and Pyle bone age than a general radiologist does.

Texture Analysis for the Bone Age Assessment from MRI Images of Adolescent Wrists in Boys

Currently, bone age is assessed by X-rays. It enables the evaluation of the child's development and is an important diagnostic factor. However, it is not sufficient to diagnose a specific disease because the diagnoses and prognoses may arise depending on how much the given case differs from the norms of bone age.

BACKGROUND

The use of magnetic resonance images (MRI) to assess the age of the patient would extend diagnostic possibilities. The bone age test could then become a routine screening test. Changing the method of determining the bone age would also prevent the patient from taking a dose of ionizing radiation, making the test less invasive.

METHODS

The regions of interest containing the wrist area and the epiphyses of the radius are marked on the magnetic resonance imaging of the non-dominant hand of boys aged 9 to 17 years. Textural features are computed for these regions, as it is assumed that the texture of the wrist image contains information about bone age.

RESULTS

The regression analysis revealed that there is a high correlation between the bone age of a patient and the MRI-derived textural features derived from MRI. For DICOM T1-weighted data, the best scores reached 0.94 R2, 0.46 RMSE, 0.21 MSE, and 0.33 MAE.

CONCLUSIONS

The experiments performed have shown that using the MRI images gives reliable results in the assessment of bone age while not exposing the patient to ionizing radiation.

Bone age assessment based on deep neural networks with annotation-free cascaded critical bone region extraction

Bone age assessment (BAA) from hand radiographs is crucial for diagnosing endocrinology disorders in adolescents and supplying therapeutic investigation. In practice, due to the conventional clinical assessment being a subjective estimation, the accuracy of BAA relies highly on the pediatrician's professionalism and experience. Recently, many deep learning methods have been proposed for the automatic estimation of bone age and had good results. However, these methods do not exploit sufficient discriminative information or require additional manual annotations of critical bone regions that are important biological identifiers in skeletal maturity, which may restrict the clinical application of these approaches. In this research, we propose a novel two-stage deep learning method for BAA without any manual region annotation, which consists of a cascaded critical bone region extraction network and a gender-assisted bone age estimation network. First, the cascaded critical bone region extraction network automatically and sequentially locates two discriminative bone regions via the visual heat maps. Second, in order to obtain an accurate BAA, the extracted critical bone regions are fed into the gender-assisted bone age estimation network. The results showed that the proposed method achieved a mean absolute error (MAE) of 5.45 months on the public dataset Radiological Society of North America (RSNA) and 3.34 months on our private dataset.

Applications of artificial intelligence in forensic sciences: Current potential benefits, limitations and perspectives

In recent years, new studies based on artificial intelligence (AI) have been conducted in the forensic field, posing new challenges and demonstrating the advantages and disadvantages of using AI methodologies to solve forensic well-known problems. Specifically, AI technology has tried to overcome the human subjective bias limitations of the traditional approach of the forensic sciences, which include sex prediction and age estimation from morphometric measurements in forensic anthropology or evaluating the third molar stage of development in forensic odontology. Likewise, AI has been studied as an assisting tool in forensic pathology for a quick and easy identification of the taxonomy of diatoms. The present systematic review follows the PRISMA 2020 statements and aims to explore an emerging topic that has been poorly analyzed in the forensic literature. Benefits, limitations, and forensic implications concerning AI are therefore highlighted, by providing an extensive critical review of its current applications on forensic sciences as well as its future directions. Results are divided into 5 subsections which included forensic anthropology, forensic odontology, forensic pathology, forensic genetics, and other forensic branches. The discussion offers a useful instrument to investigate the potential benefits of AI in the forensic fields as well as to point out the existing open questions and issues concerning its application on real-life scenarios. Procedural notes and technical aspects are also provided to the readers.

Bone age recognition based on mask R-CNN using xception regression model

Background and Objective: Bone age detection plays an important role in medical care, sports, judicial expertise and other fields. Traditional bone age identification and detection is according to manual interpretation of X-ray images of hand bone by doctors. This method is subjective and requires experience, and has certain errors. Computer-aided detection can effectually enhance the validity of medical diagnosis, especially with the fast development of machine learning and neural network, the method of bone age recognition using machine learning has gradually become the focus of research, which has the advantages of simple data pretreatment, good robustness and high recognition accuracy. Methods: In this paper, the hand bone segmentation network based on Mask R-CNN was proposed to segment the hand bone area, and the segmented hand bone region was directly input into the regression network for bone age evaluation. The regression network is using an enhancd network Xception of InceptionV3. After the output of Xception, the convolutional block attention module is connected to refine the feature mapping from channel and space to obtain more effective features. Results: According to the experimental results, the hand bone segmentation network model based on Mask R-CNN can segment the hand bone region and eliminate the interference of redundant background information. The average Dice coefficient on the verification set is 0.976. The mean absolute error of predicting bone age on our data set was only 4.97 months, which exceeded the accuracy of most other bone age assessment methods. Conclusion: Experiments show that the accuracy of bone age assessment can be enhancd by using the Mask R-CNN-based hand bone segmentation network and the Xception bone age regression network to form a model, which can be well applied to actual clinical bone age assessment.

A comprehensive validation study of the latest version of BoneXpert on a large cohort of Caucasian children and adolescents

INTRODUCTION

Automated bone age assessment has recently become increasingly popular. The aim of this study was to assess the agreement between automated and manual evaluation of bone age using the method according to Tanner-Whitehouse (TW3) and Greulich-Pyle (GP).

METHODS

We evaluated 1285 bone age scans from 1202 children (657 scans from 612 boys) by using both manual and automated (TW3 as well as GP) bone age assessment. BoneXpert software versions 2.4.5.1. (BX2) and 3.2.1. (BX3) (Visiana, Holte, Denmark) were compared with manual evaluation using root mean squared error (RMSE) analysis.

RESULTS

RMSE for BX2 was 0.57 and 0.55 years in boys and 0.72 and 0.59 years in girls, respectively for TW3 and GP. For BX3, RMSE was 0.51 and 0.68 years in boys and 0.49 and 0.52 years in girls, respectively for TW3 and GP. Sex- and age-specific analysis for BX2 identified the largest differences between manual and automated TW3 evaluation in girls between 6-7, 12-13, 13-14 and 14-15 years, with RMSE 0.88, 0.81, 0.92 and 0.84 years, respectively. The BX3 version showed better agreement with manual TW3 evaluation (RMSE 0.64, 0.45, 0.46 and 0.57).

CONCLUSION

The latest version of the BoneXpert software provides improved and clinically sufficient agreement with manual bone age evaluation in children of both sexes compared to the previous version and may be used for routine bone age evaluation in non-selected cases in pediatric endocrinology care.

Multi-scale multi-reception attention network for bone age assessment in X-ray images

Bone age assessment plays a significant role in estimating bone maturity. However, radiograph/X-ray images of hand bones contain a large amount of redundant information. Some detection or segmentation based methods have recently been proposed to solve this issue. These network structures are often of high complexity and might require extra annotations, which make them less applicable in practice. In this paper, we present a Multi-scale Multi-reception Attention Net (MMANet), which combines a novel Multi-scale Multi-reception Complement Attention (MMCA) network and a graph attention module with a ResNet backbone to enhance the feature representation of key regions and suppress the influence of background regions to achieve significant performance improvement. Experimental results show our MMANet is able to accurately detect key regions and achieves 3.88 mean absolute error (MAE) on the RSNA 2017 Paediatric Bone Age Challenge dataset. Our method, without explicit modelling of anatomical information, outperforms the current state-of-the-art method (MAE=3.91) by 0.03 (months) which requires extra annotations. Code is available at https://github.com/yzc1122333/BoneAgeAss.

Neuroimaging in 205 consecutive Children Diagnosed with Central Precocious Puberty in Denmark

INTRODUCTION

A brain magnetic resonance image (MRI) is considered part of routine evaluation in children diagnosed with central precocious puberty (CPP) to rule out intracranial pathology. We evaluated the occurrence of pathological findings on neuroimaging among children diagnosed with CPP.

METHODS

A retrospective study based on an evaluation of 1544 children referred with early signs of puberty from 2009-2019. Of these, 205 children (29 boys) with confirmed CPP had a brain MRI performed, and we report MRI results, pubertal stage, bone age, and hormonal analyses. All abnormal MRI results were re-evaluated by a trained neuroradiologist.

RESULTS

A new intracranial pathology was found by brain MRI in 6 out of 205 patients aged 1.5 to 6.1 years. The occurrence of intracranial pathology was 3/162 (1.8%) and 3/24 (12.5 %) in girls and boys respectively.

CONCLUSION

Organic causes of precocious puberty are more frequent in boys with CPP than in girls. No cases of organic CPP were seen above age 6.1 years of age. The age cut off value for routine brain MRI could be lowered to seven or perhaps even six years of age for girls, except in rapidly progressing puberty or presence of neurological symptoms.

IMPACT

In our study of children with central precocious puberty (CPP), intracranial pathology is a rare cause and occurs only in younger children. It supports the general trend, that younger children are at higher risk of having organic causes to CPP and supports the clinical practice, that only high-risk patients with CPP should undergo routine brain MRI.

Deep learning-based age estimation from chest X-rays indicates cardiovascular prognosis

BACKGROUND

In recent years, there has been considerable research on the use of artificial intelligence to estimate age and disease status from medical images. However, age estimation from chest X-ray (CXR) images has not been well studied and the clinical significance of estimated age has not been fully determined.

METHODS

To address this, we trained a deep neural network (DNN) model using more than 100,000 CXRs to estimate the patients' age solely from CXRs. We applied our DNN to CXRs of 1562 consecutive hospitalized heart failure patients, and 3586 patients admitted to the intensive care unit with cardiovascular disease.

RESULTS

The DNN's estimated age (X-ray age) showed a strong significant correlation with chronological age on the hold-out test data and independent test data. Elevated X-ray age is associated with worse clinical outcomes (heart failure readmission and all-cause death) for heart failure. Additionally, elevated X-ray age was associated with a worse prognosis in 3586 patients admitted to the intensive care unit with cardiovascular disease.

CONCLUSIONS

Our results suggest that X-ray age can serve as a useful indicator of cardiovascular abnormalities, which will help clinicians to predict, prevent and manage cardiovascular diseases.

Fully automated method for dental age estimation using the ACF detector and deep learning

Background

Dental age estimation plays an important role in identifying an unknown person. In forensic science, estimating age with high accuracy depends on the experience of the practitioner. Previous studies proposed classification of tooth development of the mandibular third molar by following Demirjian’s method, which is useful for dental age estimation. Although stage of tooth growth is very helpful in assessing age estimation, it must be performed manually. The drawback of this procedure is its need for skilled observers to carry out the tasks precisely and reproducibly because it is quite detailed. Therefore, this research aimed to apply computer-aid methods for reducing time and subjectivity in dental age estimation by using dental panoramic images based on Demirjian’s method. Dental panoramic images were collected from persons aged 15 to 23 years old. In accordance with Demirjian’s method, this study focused only on stages D to H of tooth development, which were discovered in the 15- to 23-year age range. The aggregate channel features detector was applied automatically to localize and crop only the lower left mandibular third molar in panoramic images. Then, the convolutional neural network model was applied to classify cropped images into D to H stages. Finally, the classified stages were used to estimate dental age.

Results

Experimental results showed that the proposed method in this study can localize the lower left mandibular third molar automatically with 99.5% accuracy, and training in the convolutional neural network model can achieve 83.25% classification accuracy using the transfer learning strategy with the Resnet50 network.

Conclusion

In this work, the aggregate channel features detector and convolutional neural network model were applied to localize a specific tooth in a panoramic image and identify the developmental stages automatically in order to estimate the age of the subjects. The proposed method can be applied in clinical practice as a tool that helps clinicians to reduce the time and subjectivity for dental age estimation.

The current and future roles of artificial intelligence in pediatric radiology

Artificial intelligence (AI) is a broad and complicated concept that has begun to affect many areas of medicine, perhaps none so much as radiology. While pediatric radiology has been less affected than other radiology subspecialties, there are some well-developed and some nascent applications within the field. This review focuses on the use of AI within pediatric radiology for image interpretation, with descriptive summaries of the literature to date. We highlight common features that enable successful application of the technology, along with some of the limitations that can inhibit the development of this field. We present some ideas for further research in this area and challenges that must be overcome, with an understanding that technology often advances in unpredictable ways.

Bone Age Reading by DXA Images should not Replace Bone Age Reading by X-ray Images

X-ray image of the hand is the most used technique to estimate bone age in children. For the analysis of bone mineral density using DXA in children, bone age may help to adjust such measurement in some cases. During image acquisition in DXA, an anteroposterior image of the hand may be acquired and used to evaluate bone age but few studies have evaluated the agreement between conventional X-ray and DXA images. The aim of the study was to determine bone age estimation agreement between conventional X-ray images and DXA in children and adolescents aged 5 to 16 years of age. We performed an analytical cross-sectional study of 711 healthy subjects. Subject´s bone age, both in conventional X-ray, and DXA images were read independently by two expert evaluators blinded for chronological age. Intraobserver and inter-observer reproducibility were evaluated using Intraclass Correlation Coefficient (ICC), and the agreement between bone age estimations made by both evaluators was analyzed using ICC and Bland-Altman analysis. General agreement between techniques measured through ICC was 0.99 with a mean difference of 6 months between techniques being older the ages obtained by DXA. The agreement limits were around ±2 years, which means that 95% of all differences between techniques were covered within this range. We found a high level of ICC agreement in bone age readings from X-ray and DXA images although we observed overestimation of bone age measurements in DXA. Differences between techniques were greater in women than in men, especially at the ages corresponding to puberty. Bone age measurement in DXA images appears not to be reliable; hence it should be suggested to perform conventional radiography of the hand to assess bone age taking into account that X-ray images have better resolution.

A comparison of bone age assessments using automated and manual methods in children of Indian ethnicity

BACKGROUND

Bone age is useful for pediatric endocrinologists in evaluating various disorders related to growth and puberty. Traditional methods of bone age assessment, namely Greulich and Pyle (GP) and Tanner-Whitehouse (TW), have intra- and interobserver variations. Use of computer-automated methods like BoneXpert might overcome these subjective variations.

OBJECTIVE

The aim of our study was to assess the validity of BoneXpert in comparison to manual GP and TW methods for assessing bone age in children of Asian Indian ethnicity.

MATERIALS AND METHODS

We extracted from a previous study the deidentified left hand radiographs of 920 healthy children aged 2-19 years. We compared bone age as determined by four well-trained manual raters using GP and TW methods with the BoneXpert ratings. We computed accuracy using root mean square error (RMSE) to assess how close the bone age estimated by BoneXpert was to the reference rating.

RESULTS

The standard deviations (SDs) of rating among the four manual raters were 0.52 years, 0.52 years and 0.47 years for GP, TW2 and TW3 methods, respectively. The RMSEs between the automated bone age estimates and the true ratings were 0.39 years, 0.41 years and 0.36 years, respectively, for the same methods. The RMSE values were significantly lower in girls than in boys (0.53, 0.5 and 0.47 vs. 0.39, 0.47 and 0.4) by all the methods; however, no such difference was noted in classification by body mass index. The best agreement between BoneXpert and manual rating was obtained by using 50% weight on carpals (GP50). The carpal bone age was retarded in Indian children, more so in boys.

CONCLUSION

BoneXpert was accurate and performed well in estimating bone age by both GP and TW methods in healthy Asian Indian children; the error was larger in boys. The GP50 establishes "backward compatibility" with manual rating.

Clinical, Endocrine and Neuroimaging Findings in Girls With Central Precocious Puberty

CONTEXT

The etiology of central precocious puberty (CPP) includes a spectrum of conditions. Girls younger than age 6 years with CPP should undergo cranial magnetic resonance imaging (MRI), but it remains controversial whether all girls who develop CPP between the ages of 6 and 8 years require neuroimaging examination.

OBJECTIVE

To investigate the frequency of brain MRI abnormalities in girls diagnosed with CPP and the relationship between maternal factors, their age at presentation, clinical signs and symptoms, hormonal profiles, and neuroimaging findings.

METHODS

Data were collected between January 2005 and September 2019 from 112 girls who showed clinical pubertal progression before 8 years of age who underwent brain MRI.

RESULTS

MRI was normal in 47 (42%) idiopathic (I) scans, 54 (48%) patients had hypothalamic-pituitary anomalies (HPA) and/or extra-HP anomalies (EHPA), and 11 (10%) had brain tumors or tumor-like conditions (BT/TL), including 3 with neurological signs. Associated preexisting disorders were documented in 16. Girls with BT/TL had a higher LH peak after GnRH test (P = 0.01) than I, and those older than age 6 years had a higher craniocaudal diameter of the pituitary gland (P = 0.01); their baseline FSH and LH (P = 0.004) and peak FSH (P = 0.01) and LH (P = 0.05) values were higher than I. Logistic regression showed maternal age at menarche (P = 0.02) and peak FSH (P = 0.02) as BT/TL risk factors.

CONCLUSIONS

MRI provides valuable information in girls with CPP by demonstrating that fewer than half have a normal brain MRI and that few can have significant intracranial lesions after the age of 6, despite the absence of suggestive neurological signs.