Association between sex hormones and bone age in boys aged 9-18 years from China.

This study aimed to analyse the association between sex hormones and bone age (BA) in boys aged 9-18 years, both individually and interactively, and further to explore whether nutritional status may influence this association. A retrospective analysis was performed among 1382 Chinese boys with physical measurements, sexual characteristics, BA radiographs and sex hormone indicators from February 2015 to February 2022. A total of 470 (34.0%) boys had advanced BA. BA was positively associated with estradiol, luteinizing hormone (LH), follicle-stimulating hormone (FSH) and testosterone in both advanced and normal BA groups after adjusting for age, genetic height and body mass index. Multiple logistic regression showed that after adjusting for covariates, estradiol (odds ratio [OR] = 1.66, 95% confidence interval [CI]: 1.14-2.12), LH (OR = 1.43, 95% CI: 1.04-1.96), and testosterone (OR = 1.58, 95% CI: 1.17-2.13) were significantly associated with the increased risk of advanced BA in boys, and the association was reinforced when these hormones were interactively explored. Stratified by nutritional status, the interaction between estradiol, LH, and testosterone showed a strong association with advanced BA in boys with normal weight.

Automated bone age assessment in a German pediatric cohort: agreement between an artificial intelligence software and the manual Greulich and Pyle method.

OBJECTIVES: This study aimed to evaluate the performance of artificial intelligence (AI) software in bone age (BA) assessment, according to the Greulich and Pyle (G&P) method in a German pediatric cohort. MATERIALS AND METHODS: Hand radiographs of 306 pediatric patients aged 1-18 years (153 boys, 153 girls, 18 patients per year of life)-including a subgroup of patients in the age group for which the software is declared (243 patients)-were analyzed retrospectively. Two pediatric radiologists and one endocrinologist made independent blinded BA reads. Subsequently, AI software estimated BA from the same images. Both agreements, accuracy, and interchangeability between AI and expert readers were assessed. RESULTS: The mean difference between the average of three expert readers and AI software was 0.39 months with a mean absolute difference (MAD) of 6.8 months (1.73 months for the mean difference and 6.0 months for MAD in the intended use subgroup). Performance in boys was slightly worse than in girls (MAD 6.3 months vs. 5.6 months). Regression analyses showed constant bias (slope of 1.01 with a 95% CI 0.99-1.02). The estimated equivalence index for interchangeability was - 14.3 (95% CI -27.6 to - 1.1). CONCLUSION: In terms of BA assessment, the new AI software was interchangeable with expert readers using the G&P method. CLINICAL RELEVANCE STATEMENT: The use of AI software enables every physician to provide expert reader quality in bone age assessment. KEY POINTS: • A novel artificial intelligence-based software for bone age estimation has not yet been clinically validated. • Artificial intelligence showed a good agreement and high accuracy with expert radiologists performing bone age assessment. • Artificial intelligence showed to be interchangeable with expert readers.

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.

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.

Forensic age estimation by MRI of the knee - comparison of two classifications for ossification stages in a German population.

AIM AND OBJECTIVES: In forensic age estimation e.g. for judicial proceedings surpassed age thresholds can be legally relevant. To examine age related differences in skeletal development the recommendations by the Study Group on Forensic Age Diagnostics (AGFAD) are based on ionizing radiation (among others orthopantomograms, plain x-rays of the hand). Vieth et al. and Ottow et al. proposed MRI-classifications for the epiphyseal-diaphyseal fusion of the knee joint to define different age groups in healthy volunteers. The aim of the present study was to directly compare these two classifications in a large German patient population. MATERIALS AND METHODS: MRI of the knee joint of 900 patients (405 female, 495 male) from 10 to 28 years of age were retrospectively analyzed. Acquired T1-weighted turbo spin-echo sequence (TSE) and T2-weighted sequence with fat suppression by turbo inversion recovery magnitude (TIRM) were analyzed for the two classifications. The different bony fusion stages of the two classifications were determined and the corresponding chronological ages assigned. Differences between the sexes were analyzed. Intra- and inter-observer agreements were determined using Cohen's kappa. RESULTS: With the classification of Ottow et al. it was possible to determine completion of the 18th and 21st year of life in both sexes. With the classification of Vieth et al. completion of the 18th year of life for female patients and the 14th and 21st year of life in both sexes could be determined. The intra- and inter-observer agreement levels were very good (κ > 0.82). CONCLUSION: In the large German patient cohort of this study it was possible to determine the 18th year of life with for both sexes with the classification of Ottow et al. and for female patients with the classification of Vieth et al. It was also possible to determine the 21st year of life for all bones with the classification of Ottow et al. and for the distal femur with the classification of Vieth et al.

Forensic bone age assessment of hand and wrist joint MRI images in Chinese han male adolescents based on deep convolutional neural networks.

In Chinese criminal law, the ages of 12, 14, 16, and 18 years old play a significant role in the determination of criminal responsibility. In this study, we developed an epiphyseal grading system based on magnetic resonance image (MRI) of the hand and wrist for the Chinese Han population and explored the feasibility of employing deep learning techniques for bone age assessment based on MRI of the hand and wrist. This study selected 282 Chinese Han Chinese males aged 6.0-21.0 years old. In the course of our study, we proposed a novel deep learning model for extracting and enhancing MRI hand and wrist bone features to enhance the prediction of target MRI hand and wrist bone age and achieve precise classification of the target MRI and regression of bone age. The evaluation metric for the classification model including precision, specificity, sensitivity, and accuracy, while the evaluation metrics chosen for the regression model are MAE. The epiphyseal grading was used as a supervised method, which effectively solved the problem of unbalanced sample distribution, and the two experts showed strong consistency in the epiphyseal plate grading process. In the classification results, the accuracy in distinguishing between adults and minors was 91.1%, and the lowest accuracy in the three minor classifications (12, 14, and 16 years of age) was 94.6%, 91.1% and 96.4%, respectively. The MAE of the regression results was 1.24 years. In conclusion, the deep learning model proposed enabled the age assessment of hand and wrist bones based on MRI.

Forensic age estimation at the University Center of Legal Medicine Lausanne-Geneva: a retrospective study over 12 years.

With the undeniable increase in asylum requests from unaccompanied alleged minors, age estimation of living individuals has become an essential part of the routine work in European forensic centers. This study aims to review the forensic age estimations performed in our center since 2010, to evaluate the state-of-the-art of this practice in Switzerland with the evolution of the methodology according to upcoming recommendations. Our institute's expert reports performed between 2010 and 2022 were retrospectively analyzed. We gathered the following parameters: demographic data, morphological characteristics, alleged age compared with the assessed minimum age, sexual maturation, dental and bone age. When available, we collected personal and family history, medical history, records of torture-related/self-inflicted injuries, and information about eating habits that might affect skeletal development. Data collection amounted to 656 cases. Forensic age estimations ordered by the Swiss Secretariat for Migration (SEM) represented 76.4% of cases, with 23.6% of them ordered by the Court/Public Prosecutor. Most alleged minors were male (94.5%) and came from Afghanistan (53.4%). Adjunction of CT scans of the sternoclavicular joints was necessary in 86.4% of cases. Only 25.2% of our reports concluded on most probable minority, with 55.6% of definite majors; in 19.2% of our cases, minority could not be excluded. This study aspires to further broaden our expertise regarding forensic age estimations. Given the increasing migratory flows, we can expect a notable increase in the frequency of these requests. Consequently, this study aims to promote a multidisciplinary approach and the international standardization of the methodology of these estimations.

Importance of Growth Factors and Bone Maturation Ratio in the Response to Growth Hormone Therapy.

OBJECTIVE: The aim was to identify the influence of insulin-like growth factor I (IGF-1), IGF-binding protein-3 (IGFBP-3), and bone age (BA)/chronological age (CA) ratio on the response to GH therapy after 1 and 2 years of treatment and upon reaching final height. METHODS: Longitudinal, retrospective, observational study of 139 patients treated for idiopathic growth hormone deficiency. Variables examined during follow-up: (1) genetic background; (2) perinatal history; (3) anthropometry; (4) height velocity, BA, BA/CA and height prognosis; (5) analytical results (IGF-1, IGFBP-3). Final response variables: adult height (AH), AH with respect to target height, AH with respect to initial height prognosis, AH with respect to height at the start of treatment, and AH with respect to height at onset of puberty. RESULTS: Lower pretreatment IGF-1 levels and a greater increase in IGF-1 at the end of treatment imply a better response (r = -0.405, P = .007 and r = 0.274, P = .014, respectively), as does a greater increase in IGFBP-3 after 2 years of treatment and at the end of treatment (r = 0.207, P = .035 and r = 0.259, P = .020, respectively). A lower BA/CA ratio pretreatment and at the onset of puberty results in a better response (r = -0.502, P = .000 and r = -0.548, P = .000, respectively), as does a lower increase in BA and BA/CA ratio after the 1 and 2 years of treatment (r = -0.337, P = .000 and r = -0.332, P = .000, respectively). CONCLUSION: Low pretreatment IGF-1, a greater BA delay with respect to CA pretreatment and at the onset of puberty, a greater increase in IGFBP-3 after 2 years of treatment, and a lower increase in BA and BA/CA ratio after 1 and 2 years of treatment imply a better long-term response.

Deep learning-based automated bone age estimation for Saudi patients on hand radiograph images: a retrospective study.

PURPOSE: In pediatric medicine, precise estimation of bone age is essential for skeletal maturity evaluation, growth disorder diagnosis, and therapeutic intervention planning. Conventional techniques for determining bone age depend on radiologists' subjective judgments, which may lead to non-negligible differences in the estimated bone age. This study proposes a deep learning-based model utilizing a fully connected convolutional neural network(CNN) to predict bone age from left-hand radiographs. METHODS: The data set used in this study, consisting of 473 patients, was retrospectively retrieved from the PACS (Picture Achieving and Communication System) of a single institution. We developed a fully connected CNN consisting of four convolutional blocks, three fully connected layers, and a single neuron as output. The model was trained and validated on 80% of the data using the mean-squared error as a cost function to minimize the difference between the predicted and reference bone age values through the Adam optimization algorithm. Data augmentation was applied to the training and validation sets yielded in doubling the data samples. The performance of the trained model was evaluated on a test data set (20%) using various metrics including, the mean absolute error (MAE), median absolute error (MedAE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). The code of the developed model for predicting the bone age in this study is available publicly on GitHub at https://github.com/afiosman/deep-learning-based-bone-age-estimation . RESULTS: Experimental results demonstrate the sound capabilities of our model in predicting the bone age on the left-hand radiographs as in the majority of the cases, the predicted bone ages and reference bone ages are nearly close to each other with a calculated MAE of 2.3 [1.9, 2.7; 0.95 confidence level] years, MedAE of 2.1 years, RMAE of 3.0 [1.5, 4.5; 0.95 confidence level] years, and MAPE of 0.29 (29%) on the test data set. CONCLUSION: These findings highlight the usability of estimating the bone age from left-hand radiographs, helping radiologists to verify their own results considering the margin of error on the model. The performance of our proposed model could be improved with additional refining and validation.

The impact of HIV infection on skeletal maturity in peripubertal children in Zimbabwe: a cross-sectional study.

INTRODUCTION: HIV infection and its treatment compromises skeletal development (growth and maturation). Skeletal maturity is assessed as bone age (BA) on hand and wrist radiographs. BA younger than chronological age (CA) indicates delayed development. We conducted a cross-sectional study to determine differences between BA and CA (i.e., skeletal maturity deviation [SMD]), and risk factors associated with SMD in peripubertal children with and without HIV established on antiretroviral therapy (ART) including use of tenofovir disoproxil fumarate (TDF). METHODS: Children with HIV taking ART for at least two years and a comparison group of HIV-negative children, aged 8-16 years and frequency-matched by age and sex, were recruited from HIV clinics and local schools in the same catchment area, in Harare, Zimbabwe. BA was assessed from non-dominant hand-wrist radiographs using the Tanner Whitehouse 3 method. Negative SMD values correspond to delayed development, i.e., BA younger than CA. Multivariable linear regression models determined factors associated with SMD overall, and in children with HIV. RESULTS: In total, 534 participants (54% males) were included; by design CA was similar in males and females, whether living with or without HIV. Mean (SD) SMD was more negative in CWH than in HIV-negative children in both males [-1.4(1.4) vs. -0.4(1.1) years] and females [-1.1(1.3) vs. -0.0(1.2) years]. HIV infection and weight-for-age Z-score<-2 were associated with more negative SMD in both males and females after adjusting for socio-economic status, orphanhood, pubertal stage, and calcium intake. Age at ART initiation was associated with SMD in both males and females with those starting ART later more delayed: starting ART aged 4-8 years 1.14 (-1.84, -0.43), or over 8 years 1.47 (-2.30, -0.65) (p-value for trend < 0.001). Similar non-significant trends were seen in males. TDF exposure TDF exposure whether < 4years or ≥ 4 years was not associated with delayed development. CONCLUSION: Perinatally-acquired HIV infection and being underweight were independently associated with delayed skeletal maturation in both males and females. Starting ART later was independently associated with skeletal maturation delay in CWH. Given the known effects of delayed development on later health, it is important to find interventions to ensure healthy weight gain through early years and in CWH to initiate ART as early as possible.

Longitudinal changes in body mass index, height, and weight in children with acute myeloid leukemia.

BACKGROUND: This study reported height prediction and longitudinal growth changes in Chinese pediatric patients with acute myeloid leukemia (AML) during and after treatment and their associations with outcomes. METHODS: Changes in 88 children with AML in percentages according to the growth percentile curve for Chinese boys/girls aged 2-18/0-2 years for body mass index (BMI), height, and weight from the time of diagnosis to 2 years off therapy were evaluated. The outcomes of AML were compared among patients with different BMI levels. RESULTS: The proportion of underweight children (weight < 5th percentile) increased significantly from the initial diagnosis to the end of consolidation treatment. The proportion of patients with low BMI (BMI < 5th percentile) was highest (23.08%) during the consolidation phase, and no children were underweight, but 20% were overweight (BMI > 75th percentile) after 2 years of drug withdrawal. Unhealthy BMI at the initial diagnosis and during intensive chemotherapy leads to poorer outcomes. For height, all patients were in the range of genetic height predicted based on their parents' height at final follow-up. CONCLUSIONS: Physicians should pay more attention to the changes in height and weight of children with AML at these crucial treatment stages and intervene in time.

Is there a skeletal age index that can predict accurate curve progression in adolescent idiopathic scoliosis? A systematic review.

BACKGROUND: The diagnosis of adolescent idiopathic scoliosis requires clinical and radiographic evaluation; the management options vary depending on the severity of the curve and potential for progression. Identifying predictors of scoliosis progression is crucial to avoid incorrect management; clinical and radiographic factors have been studied as potential predictors. The present study aims to review the literature on radiological indexes for the peak height velocity or curve acceleration phase to help clinicians manage treatment of patients with adolescent idiopathic scoliosis. METHODS: This systematic review was carried out in accordance with Preferential Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. The search was carried out including only peer-reviewed articles written in English that described the radiological indexes assessing skeletal maturity in patients with adolescent idiopathic scoliosis and evaluated their correlation with curve progression, expressed as peak height velocity and/or curve acceleartion phase. RESULTS: Thirteen studies were included and showed promising results in terms of reliable radiological indexes. Risser staging gives a general measure of skeletal maturity, but it cannot be used as a primary index for driving the treatment of patients with adolescent idiopathic scoliosis since more reliable indexes are available. CONCLUSION: Skeletal maturity quantification for adolescent idiopathic scoliosis has the potential to significantly modify disease management. However, idiopathic scoliosis is a complex and multifactorial disease: therefore, it is unlikely that a single index will ever be sufficient to predict its evolution. Therefore, as more adolescent idiopathic scoliosis progression-associated indexes are identified, a collective scientific effort should be made to develop a therapeutic strategy based on reliable and reproducible algorithms.

Phantom study of a self-shielded X-ray bone age assessment instrument against scattered radiation in children.

Hand-wrist radiography is the most common and accurate method for evaluating children's bone age. To reduce the scattered radiation of radiosensitive organs in bone age assessment, we designed a small X-ray instrument with radioprotection function by adding metal enclosure for X-ray shielding. We used a phantom operator to compare the scattered radiation doses received by sensitive organs under three different protection scenarios (proposed instrument, radiation personal protective equipment, no protection). The proposed instrument showed greater reduction in the mean dose of a single exposure compared with radiation personal protective equipment especially on the left side which was proximal to the X-ray machine (≥80.0% in eye and thyroid, ≥99.9% in breast and gonad). The proposed instrument provides a new pathway towards more convenient and efficient radioprotection.

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.

Does ethnicity influence bone health index in children? A pilot study.

BACKGROUND: Several pathological conditions can lead to variations in bone mineral content during growth. When assessing bone age, bone mineral content can be estimated without supplementary cost and irradiation. Manual assessment of bone quality using the Exton-Smith index (ESI) and automated assessment of the bone health index (BHI) provided by the BoneXpert® software are available but still not validated in different ethnic groups. OBJECTIVE: Our aim is to provide normative values of the ESI and BHI for healthy European Caucasian and first-generation children of North Africans living in Europe. MATERIALS AND METHODS: A sex- and aged-match population of 214 girls (107 European-Caucasian and 107 North African) and 220 boys (111 European-Caucasian and 109 North African) were retrospectively and consecutively included in the study. Normal radiographs of the left hand and wrist from healthy children were retrieved from those performed in a single institution from 2008 to 2017 to rule out a left-hand fracture. Radiographs were processed by BoneXpert® to obtain the BHI and BHI standard deviation score (SDS). One radiologist, blinded to BHI values, manually calculated ESI for each patient. The variability for both methods was assessed and compared using the standard deviation (SD) of the median (%) for each class of age and sex, and ESI and BHI trends were compared by sex and ethnic group. RESULTS: The final population comprised 434 children ages 3 to 15 years (214 girls). Overall, BHI was lower in North African children (mean = 4.23 for girls and 4.17 in boys) than in European Caucasians (mean = 4.50 for girls and 4.68 in boys) (P < 0.001). Regardless of ethnicity, 29 girls (13.6%) and 34 boys (15.5%) had BHI more than 2 SD from the mean. While correlated to BHI, ESI has a higher variability than BHI and is more pronounced from 8-12 years for both sexes (mean ESI in European Caucasian girls and boys 17.47 and 20.87, respectively) (P < 0.001). ESI showed more than 15% variability in European girls from 8-12 years and a plateau in North African boys from 12 years to 16 years. However, the BHI has less than 15% variability regardless of age and ethnic group. CONCLUSION: BHI may be a reliable tool to detect children with abnormal bone mineral content, with lower variability compared to ESI and with specific trends depending on sex and ethnicity.

Acceleration of skeletal maturation in Central Europe over the last two decades: insights from two cohorts of healthy children.

BACKGROUND: Deviations between the determination of bone age (BA) according to Greulich and Pyle (G&P) and chronological age (CA) are common in Caucasians. Assessing these discrepancies in a population over time requires analysis of large samples and low intra-observer variability in BA estimation, both can be achieved with artificial intelligence-based software. The latest software-based reference curve contrasting the BA determined by G&P to the CA of Central European children dates back over two decades. OBJECTIVE: To examine whether the reference curve from a historical cohort from the Netherlands (Rotterdam cohort) between BA determined by G&P and CA still applies to a current Central European cohort and derive a current reference curve. MATERIALS AND METHODS: This retrospective single-center study included 1,653 children and adolescents (aged 3-17 years) who had received a radiograph of the hand following trauma. The G&P BA estimated using artificial intelligence-based software was contrasted with the CA, and the deviations were compared with the Rotterdam cohort. RESULTS: Among the participants, the mean absolute error between BA and CA was 0.92 years for girls and 0.97 years for boys. For the ages of 8 years (boys) and 11 years (girls) and upward, the mean deviation was significantly greater in the current cohort than in the Rotterdam cohort. The reference curves of both cohorts also differed significantly from each other (P < 0.001 for both boys and girls). CONCLUSION: The BA of the current Central European population and that of the curve from the Rotterdam cohort from over two decades ago differ. Whether this effect can be attributed to accelerated bone maturation needs further evaluation.

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.

Evaluation and medical care of intersex and gender diverse youth.

While there is considerable overlap in the treatment of patients with intersex traits and differences in sex development (I/DSD) with transgender and gender diverse (TGD) youth, the initial medical evaluation varies significantly. I/DSD youth often present due to differences in genitalia development in infancy or pubertal development in adolescence, and this leads to comprehensive biochemical, radiologic, and genetic evaluation. TGD youth, however, tend to have typical development noted at birth and during puberty, but present with a gender identity that does not align with their sex assigned at birth and do not require evaluation for underlying pathology. For both I/DSD and TGD youth, the mainstays of treatment are to better align one's physical appearance to their gender identity. This review discusses the non-medical and medical interventions utilized in gender affirming care. A multidisciplinary team of mental health providers, pediatric medical providers, and surgeons is recommended for providing gender affirming care to both I/DSD youth and TGD youth and their families. Radiologists have an important role in initial evaluation of I/DSD youth and in ongoing monitoring of growth and bone mineral density during puberty induction in I/DSD and TGD youth.

Inter-rater variability and repeatability in the assessment of the Tanner-Whitehouse classification of hand radiographs for the estimation of bone age.

OBJECTIVE: To determine which bones and which grades had the highest inter-rater variability when employing the Tanner-Whitehouse (T-W) method. MATERIALS AND METHODS: Twenty-four radiologists were recruited and trained in the T-W classification of skeletal development. The consistency and skill of the radiologists in determining bone development status were assessed using 20 pediatric hand radiographs of children aged 1 to 18 years old. Four radiologists had a poor concordance rate and were excluded. The remaining 20 radiologists undertook a repeat reading of the radiographs, and their results were analyzed by comparing them with the mean assessment of two senior experts as the reference standard. Concordance rate, scoring, and Kendall's W were calculated to evaluate accuracy and consistency. RESULTS: Both the radius, ulna, and short finger (RUS) system (Kendall's W = 0.833) and the carpal (C) system (Kendall's W = 0.944) had excellent consistency, with the RUS system outperforming the C system in terms of scores. The repeatability analysis showed that the second rating test, performed after 2 months of further bone age assessment (BAA) practice, was more consistent and accurate than the first. The capitate had the lowest average concordance rate and scoring, as well as the lowest overall concordance rate for its D classification. Moreover, the G classifications of the seven carpal bones all had a concordance rate less than 0.6. The bones with lower Kendall's W were likewise those with lower scores and concordance rates. CONCLUSION: The D grade of the capitate showed the highest variation, and the use of the Tanner-Whitehouse 3rd edition (T-W3) to determine bone age (BA) was frequently inconsistent. A more comprehensive description with a focus on inaccuracy bones or ratings and a modification to the T-W3 approach would significantly advance BAA.

Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.

This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs.