Hand Radiographs Research Articles

Association between hyperuricaemia and hand osteoarthritis: data from the Xiangya Osteoarthritis Study

ObjectiveThe pathogenesis of hand osteoarthritis (OA) remains unknown. Hyperuricaemia, which is related to inflammation, may play a role in hand OA, but evidence is lacking. In a large population-based study,...

Enchondroma Detection from Hand Radiographs with an Interactive Deep Learning Segmentation Tool-A Feasibility Study.

Enchondromas are common benign bone tumors, usually presenting in the hand. They can cause symptoms such as swelling and pain but often go un-noticed. If the tumor expands, it can diminish the bone cortices and predispose the bone to fracture. Diagnosis is based on clinical investigation and radiographic imaging. Despite their typical appearance on radiographs, they can primarily be misdiagnosed or go totally unrecognized in the acute trauma setting. Earlier applications of deep learning models to image classification and pattern recognition suggest that this technique may also be utilized in detecting enchondroma in hand radiographs. We trained a deep learning model with 414 enchondroma radiographs to detect enchondroma from hand radiographs. A separate test set of 131 radiographs (47% with an enchondroma) was used to assess the performance of the trained deep learning model. Enchondroma annotation by three clinical experts served as our ground truth in assessing the deep learning model's performance. Our deep learning model detected 56 enchondromas from the 62 enchondroma radiographs. The area under receiver operator curve was 0.95. The F1 score for area statistical overlapping was 69.5%. Our deep learning model may be a useful tool for radiograph screening and raising suspicion of enchondroma.

The Modified Fels and Abbreviated Modified Fels Knee Skeletal-Maturity Systems in the Prediction of Leg-Length Discrepancy.

The Modified Fels (mFels) and Abbreviated Modified Fels (abFels) knee systems have been recently developed as options for grading skeletal maturity without the need for a separate hand radiograph. We sought to determine the interobserver reliability of these systems and to compare their prediction accuracy with that of the Greulich and Pyle (G-P) atlas in a cohort managed with epiphysiodesis for leg-length discrepancy (LLD). Three reviewers scored 20 knee radiographs using the mFels system, which includes 5 qualitative and 2 quantitative measures as well as a quantitative output. Short leg length (SL), long leg length (LL), and LLD prediction errors at maturity using the White-Menelaus (W-M) method and G-P, mFels, or abFels skeletal age were compared in a cohort of 60 patients managed with epiphysiodesis for LLD. Intraclass correlation coefficients for the 2 quantitative variables and the quantitative output of the mFels system using 20 knee radiographs ranged from 0.55 to 0.98, and kappa coefficients for the 5 qualitative variables ranged from 0.56 to 1, indicating a reliability range from moderate to excellent. In the epiphysiodesis cohort, G-P skeletal age was on average 0.25 year older than mFels and abFels skeletal ages, most notably in females. The majority of average prediction errors between G-P, mFels, and abFels were <0.5 cm, with the greatest error being for the SL prediction in females, which approached 1 cm. Skeletal-age estimates with the mFels and abFels systems were statistically comparable. The mFels skeletal-age system is a reproducible method of determining skeletal age. Prediction errors in mFels and abFels skeletal ages were clinically comparable with those in G-P skeletal ages in this epiphysiodesis cohort. Further work is warranted to optimize and validate the accuracy of mFels and abFels skeletal ages to predict LLD and the impact of epiphysiodesis, particularly in females. Both the mFels and abFels systems are promising means of estimating skeletal age, avoiding additional radiation and health-care expenditure. Prognostic Level II . See Instructions for Authors for a complete description of levels of evidence.

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

BackgroundSkeletal 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.ObjectiveWe present Deeplasia, an open-source prior-free deep-learning approach designed for BA assessment specifically validated on patients with skeletal dysplasias.Materials and methodsWe 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.ResultsThe 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.ConclusionWe demonstrated that Deeplasia is competent in assessing the age and monitoring the development of both normal and dysplastic bones.Graphical

Correlation of Serum Insulin-Like Growth Factor-1 and Hand Wrist Radiographs as Skeletal Maturity Indicators: an Ex-Vivo Study

Introduction: Conventional Hand and Wrist radiographs, have certain drawbacks of exposing orthodontic patients to unnecessary radiation hazards and subjectivity to errors. Serum insulin-like growth factor 1 (IGF-1) levels have been reported to increase until the pubertal peak in literature. The present study aims to assess the relationship between serum IGF-1 and skeletal maturity indicators Materials and Methods: Lateral cephalograms of 60 patients were assigned SMI stage according to Fishman’s classification system. The serum IGF-1 levels of the patients were also evaluated. Correlation between the serum IGF-1 levels, age of the patient, and their SMI stage was analyzed. Results: Pearson's coefficient of correlation revealed a non-significant weak positive correlation (p=0.69; &gt;0.05) between age and IGF-1 levels, and a non-significant weak negative correlation (p =0.52; &gt;0.05) between SMI stages and IGF-1 levels. Results of the ANOVA test indicated that there was no significant difference between mean IGF-1 levels across the different age groups and eleven SMI groups. However, there was a significant difference noted in the mean IGF-1 levels and the restructured SMI categories and groups. Conclusion: The moderate correlation between age and serum IGF-1 fluctuations during puberty underscores the hormone's pivotal role in adolescent growth. This positions serum IGF-1 as a potentially specific and reliable marker for assessing mandibular growth modifications, offering a radiation-free alternative to conventional radiographic methods

THU383 “My Hands Are Always Stiff”: A Common But Underrecognized Mimicker Of Inflammatory Arthritis.

Abstract Disclosure: I. Iqbal: None. A. Minalyan: None. M. Khan: None. Introduction: Inflammatory arthropathies are often identified in patients by their characteristic patterns, including symmetric small joint involvement, joint stiffness that can improve with physical activity, and others. However, it is crucial to address comorbidities and their impact on symptom presentation when assessing these patients. Here we present a case of a relatively common but underrecognized mimicker of inflammatory arthropathies. Case: A 63-year-old man with Type 1 Diabetes Mellitus (DM1) and seronegative rheumatoid arthritis (RA) was referred to a rheumatology clinic by his family physician to manage RA. He was previously treated with prednisone and methotrexate for six months for presumed seronegative rheumatoid arthritis without any relief. He reported chronic pain and stiffness in his joints: wrists, metacarpophalangeal (MCP), proximal and distal interphalangeal (PIP and DIP) joints, ankles, and feet. His symptoms were worse in the morning. However, on further questioning, he admitted that the stiffness did not improve throughout the day. He denied joint swelling or redness over the affected joints. On exam, his hand joints had significant stiffness. We obtained hand radiographs which did not show erosive changes or significant joint space narrowing. He had negative rheumatoid factor (RF), normal anti-cyclic citrullinated peptide (CCP) antibodies, and antinuclear antibody (ANA) levels, which ruled out the diagnosis of rheumatoid arthritis. A detailed physical exam demonstrated a positive “prayer sign” and “tabletop sign.” Further lab review showed that his hemoglobin A1c (HbA1C) had been ranging between 8% to 10% over several years. Eventually, the patient was diagnosed with limited joint mobility due to the poorly controlled DM1. We discontinued his methotrexate and referred him to an endocrinologist to manage the uncontrolled DM1. He was referred to physical therapy for hand stretching exercises. After six months of treatment, his symptoms improved but did not resolve completely. Conclusion: It is essential to identify the impact of diabetes on the musculoskeletal system. Limited joint mobility, previously known as diabetic cheiroarthropathy (DCA), is a common but frequently missed manifestation of poorly controlled diabetes. It is characterized by a limited ability to flex or extend the MCP and IP joints. It may or may not be painful and can progress to fixed flexion contractures of the finger joints. Early recognition is essential for correct diagnosis. Treatment is with tighter glycemic control and physical therapy. Presentation: Thursday, June 15, 2023

Radiographic joint space width in individuals with hand osteoarthritis: Are their “healthy” joints really healthy?

We aimed to investigate the systemic nature of hand osteoarthritis (OA). We hypothesized that people who suffer from hand OA will display narrower radiographic joint space width (JSW) - not only in joints with apparent radiographic OA but also in their unaffected "healthy" joints. We examined 3394 participants from the Osteoarthritis Initiative (OAI) with available dominant hand radiographs at baseline. Cases were defined as having interphalangeal OA (IPOA) based on a Kellgren and Lawrence (KL) score of ≥2 in two or more finger joints, whereas controls did not have IPOA. We used custom software to make JSW measurements of the metacarpophalangeal (MCP), proximal interphalangeal (PIP), and distal interphalangeal (DIP) joints in fingers 2-5 per hand. In joint-level analyses, we included only KL score = 0, allowing us to compare all joints without IPOA in cases and controls. We used generalized estimating equation (GEE) models to compare JSW between both groups, adjusted for age, gender, metacarpal length, and joint type. Finger joints without radiographic OA had significantly narrower JSW in the IPOA group compared to finger joints in the control group (p<0.001). The differences were significant across all joint types and for both total JSW measurements as well as for central and lateral sub-regions within each joint group (p<0.001). Unaffected finger joints in people with IPOA had narrower joint space than joints of healthy controls. This implies a systemic nature of hand OA, in which people may have a predisposition for general cartilage deterioration.

The Effect of an Intensive Hand Exercise Program on Muscle Strength and Hand Functions in Patients with Rheumatoid Arthritis

Aim: This study investigated the effectiveness of classic and intensive exercise programs in patients with rheumatoid arthritis (RA) and their relationship with disease activity and patient functional well-being.&#x0D; Material and Methods: Sixty patients aged 18 to 65, diagnosed with RA in our clinic, were randomly divided into two groups of 30 each. The first group received an intensive exercise program, while the second group received a classic exercise program. Disease activity was evaluated using erythrocyte sedimentation rate, C-reactive protein, DAS28Sedim, and DAS28Crp. Daily life activities were assessed using the HAQ score and Duruöz hand index. Joint mobility measurements were taken with a goniometer, and muscle strength measurements were taken using a manual dynamometer and pinch meter. Hand and wrist radiographs were taken and evaluated according to Steinbroker stages before exercise therapy.&#x0D; Results: Improvement was observed in both groups' Visual Analog Scale (VAS) and Health Assessment Questionnaire (HAQ) scores, with a significant improvement in VAS scores favoring the intensive exercise group.&#x0D; Conclusions: The study concluded that exercise therapy had positive effects on patients' hand functions, and the intensive exercise program was more effective.

Hand joints without radiographic osteoarthritis maintain their joint space width over 4 years despite what's happening elsewhere in the hand.

We aimed to determine whether hand OA is characterized by systemic cartilage loss by assessing if radiographically normal joints had greater joint space width (JSW) loss over 4 years in hands with incident or prevalent OA elsewhere in the hand compared with hands without OA. We used semi-automated software to measure JSW in the distal and proximal IP joints of 3368 participants in the Osteoarthritis Initiative who had baseline and 48-month hand radiographs. A reader scored 16 hand joints (including the thumb base) for Kellgren-Lawrence (KL) grade. A joint had OA if scored as KL ≥2. We identified three groups based on longitudinal hand OA status: no hand OA (KL <2 in all 16 joints) at the baseline and 48-month visits, incident hand OA (KL <2 in all 16 joints at baseline and then one or more joints with KL ≥2 at 48 months) and prevalent hand OA (one or more joints with KL ≥2 at baseline and 48 months). We then assessed if JSW in radiographically normal joints (KL 0) differed across these three groups. We calculated unpooled effect sizes to help interpret the differences between groups. We observed small differences in JSW loss that are unlikely to be clinically important in radiographically normal joints between those without hand OA (n = 1054) and those with incident (n = 102) or prevalent hand OA (n = 2212) (effect size range -0.01-0.24). These findings were robust when examining JSW loss dichotomized based on meaningful change and in other secondary analyses. Hand OA is not a systemic disease of cartilage.

Validation of a deep learning algorithm for bone age estimation among patients in the city of São Paulo, Brazil.

To validate a deep learning (DL) model for bone age estimation in individuals in the city of São Paulo, comparing it with the Greulich and Pyle method. This was a cross-sectional study of hand and wrist radiographs obtained for the determination of bone age. The manual analysis was performed by an experienced radiologist. The model used was based on a convolutional neural network that placed third in the 2017 Radiological Society of North America challenge. The mean absolute error (MAE) and the root-mean-square error (RMSE) were calculated for the model versus the radiologist, with comparisons by sex, race, and age. The sample comprised 714 examinations. There was a correlation between the two methods, with a coefficient of determination of 0.94. The MAE of the predictions was 7.68 months, and the RMSE was 10.27 months. There were no statistically significant differences between sexes or among races (p > 0.05). The algorithm overestimated bone age in younger individuals (p = 0.001). Our DL algorithm demonstrated potential for estimating bone age in individuals in the city of São Paulo, regardless of sex and race. However, improvements are needed, particularly in relation to its use in younger patients.

Brownish limb tumor as a presentation of primary hyperparathyroidism

43-year-old male, with tumor in the anterior region of the right lower limb (figure 1) and biopsy compatible with brown tumor. He presented total serum calcium 12.3 mg/dl and 13 mg/dl (NR: 8.3-10.3), parathormone (PTH) 1057 pg/ml (NR 15-65). With the diagnosis of primary hyperparathyroidism, a 99mTc-MIBI parathyroid scintigraphy with SPECT was requested, which showed hyperfunctioning parathyroid tissue in the projection of the left inferior parathyroid gland. Hand radiographs showed a lytic lesion in punch in the distal phalanx of the first finger of the left hand (figure 2); in the clavicle radiograph: lytic lesion of 10 mm x 8 mm in the right clavicle, no alterations were evidenced in the skull radiograph.

Sex estimation from Thai hand radiographs using convolutional neural networks

Manual analysis of hand radiographs for sex estimation is prone to biases and errors. This study addresses the need for automated methods by exploring the potential of Convolutional Neural Networks (CNNs) to accurately identify individual sex from Thai hand radiographs, overcoming limitations in data availability and variable quality. To improve dataset quality, we applied contrast limited adaptive histogram equalization (CLAHE) and Gaussian blur filter techniques to Thai hand radiographs from 385 male and 788 female individuals. We split these images into training, validation, and test sets. We also applied image augmentation techniques to increase the number of radiographs in the training dataset. Seven CNN models were trained, validated, and evaluated on 100 unseen male and female radiographs each. Among these models, the InceptionResNetV2 architecture demonstrated superior performance, achieving an accuracy of 87.50 % and an F1-Score of 86.91 %. Notably, this model utilized information from the 2nd to the 5th metacarpal bone and proximal phalanges in males, and from the 2nd metacarpal bone in females. Our findings provide a solid foundation for sex estimation from Thai hand radiographs, highlighting the power of CNNs in mitigating challenges associated with data quantity and quality. By automating the sex estimation process using CNNs, forensic analysis can benefit from enhanced accuracy and objectivity, enabling faster and more reliable sex assessment. We envisage that future research will build upon these findings to further improve the performance of sex estimation, contributing to advancements in forensic analysis and facilitating more effective utilization of Thai hand radiographs for sex estimation.

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

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. 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. 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). BHISDS 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). 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.

Deep learning discrimination of rheumatoid arthritis from osteoarthritis on hand radiography.

To develop a deep learning model to distinguish rheumatoid arthritis (RA) from osteoarthritis (OA) using hand radiographs and to evaluate the effects of changing pretraining and training parameters on model performance. A convolutional neural network was retrospectively trained on 9714 hand radiograph exams from 8387 patients obtained from 2017 to 2021 at seven hospitals within an integrated healthcare network. Performance was assessed using an independent test set of 250 exams from 146 patients. Binary discriminatory capacity (no arthritis versus arthritis; RA versus not RA) and three-way classification (no arthritis versus OA versus RA) were evaluated. The effects of additional pretraining using musculoskeletal radiographs, using all views as opposed to only the posteroanterior view, and varying image resolution on model performance were also investigated. Area under the receiver operating characteristic curve (AUC) and Cohen's kappa coefficient were used to evaluate diagnostic performance. For no arthritis versus arthritis, the model achieved an AUC of 0.975 (95% CI: 0.957, 0.989). For RA versus not RA, the model achieved an AUC of 0.955 (95% CI: 0.919, 0.983). For three-way classification, the model achieved a kappa of 0.806 (95% CI: 0.742, 0.866) and accuracy of 87.2% (95% CI: 83.2%, 91.2%) on the test set. Increasing image resolution increased performance up to 1024 × 1024 pixels. Additional pretraining on musculoskeletal radiographs and using all views did not significantly affect performance. A deep learning model can be used to distinguish no arthritis, OA, and RA on hand radiographs with high performance.

Remitting seronegative symmetrical synovitis with pitting edema syndrome postsecond COVID-19 vaccination: a case report.

A 52-year-old woman presented with hand edema, arthralgia, and morning stiffness, after receiving the second dose of m RNA COVID-19 vaccination. Examination revealed edema in the dorsum of both hands and symmetrical tenderness of the shoulders, elbows, wrists, knees, and ankles. Blood test results were normal except for elevated levels of C-reactive protein. The immune profile and the tuberculin test were also negative. A hand radiograph showed soft tissue edema. Ultrasonography of the extremities has shown tenosynovitis. Remitting seronegative symmetrical synovitis with pitting edema syndrome was diagnosed according to the diagnostic criteria. She was treated with 30mg/day prednisolone, with a complete disappearance of edema and arthralgia after 10 days, and the C-reactive protein level was decreased. Vaccines are still the most effective and protective method against COVID-19 infection, but it may trigger an immunological response. Remitting seronegative symmetrical synovitis with pitting edema (or sometimes RS 3 PE) is a rare nonunderstood syndrome identified by symmetric polyarthritis, synovitis, acute pitting edema of hands and/or feet, and a negative serum rheumatoid factor, with an excellent prognosis to treatment with steroid. Usually occurred in the old aged population, young patients' cases were mentioned. As the authors did not find any data about our subject, this is the first case of this syndrome development after the second dose of COVID-19 vaccination. The occurrence of acute symmetrical seronegative polysynovitis with extremities edema, in the elderly, should guide toward the diagnosis of this syndrome.

Improvement of Bone Age Assessment Using a Deep Learning Model in Young Children: Significance of Carpal Bone Analysis

Background: Deep learning methods used for bone age assessment (BAA) mostly employ the whole hand or regional convolutional neural networks without carpal bones; therefore, their application is insufficient in young children. Objectives: This study aimed to improve the accuracy of BAA in young children by integrating a carpal bone analysis and to achieve a similar BAA accuracy for all age groups. Patients and Methods: A hybrid Greulich-Pyle (GP) and modified Tanner-Whitehouse deep learning model for BAA was trained by integrating an additional carpal bone analysis of an open dataset. A total of 453 hand radiographs from a single institution were selected for external validation. To create the reference standard, three human experts conducted a BAA, based on the GP Atlas, and then, interobserver agreement was evaluated. The model performance was estimated by comparing the mean absolute difference (MAD) and the root mean square error (RMSE) between the two BAA models, including one with a carpal bone analysis (M1) and one without a carpal bone analysis (M2), and the reference standard. The MAD of each model was compared between sex and age groups with respect to four major developmental stages, that is, pre-puberty, early and mid-puberty, late puberty, and post-puberty. Results: The M1 model showed a higher accuracy with a lower MAD (0.366; 95% confidence interval [CI]: 0.337 - 0.395) compared to the M2 model (0.388; 95% CI: 0.358 - 0.418) for all age groups, with a significant difference (P &lt; 0.001). The RMSE values versus the reference standard were 0.483 and 0.505 years for the M1 and M2 models, respectively. According to sex and developmental stage distributions, the M1 model had a greater predictive ability compared to the M2 model for pre-pubertal patients, regardless of sex (P = 0.008 for males and P = 0.022 for females). Conclusion: Based on the present findings, the integration of a carpal bone analysis into the BAA model improved its accuracy, especially in young children.

THE MODULARITY OF THE FINGERS AS A SOURCE OF MORPHOLOGICAL VARIATIONS OF THE HUMAN HAND'S SHAPE DEPENDING ON SEX

According to the latest achievements in animal morphology and paleo-morphology, the phenomena of modularity and integration are quite closely interconnected, while morphological integration describes the extent to which various structural features are related to each other in the process of morphogenesis, while modularity reflects the rate of evolutionary transformation, which determines the degree of this conjugation. One of the classical objects of study of modularity and integration in morphology is regularly segmented skeletal structures, in particular, the hand. In humans, the hand is characterized mainly by the ratio of the lengths of the index and ring fingers, while their position and the value that determines the structure of the hand as a whole in the space of its shape has not yet been studied. In this paper, the influence of the modules of the fingers on the shape of the hand in the framework of their morphological integration was determined, which became the main goal of the study. The method of geometric morphometry of digital images of radiographs of the right hands of 100 people was used, on which the coordinates of 16 landmarks of the phalanges of the II-V fingers were determined, followed by a study of the shape, covariance and modularity. The results showed that changes in the shape of the hand are due to the position of the II-V fingers in space, with the ulnar type of the hand (2d&lt;4d) there is a displacement of the II-III fingers in space in the distal direction and IV-V fingers in the proximal direction, with the radial type of the hand (2d&gt;4d) reverse transformations in space are observed. The position of the second finger in space largely affects the shape of the hand as a whole. An analysis of the modularity of the fingers indicates a significant integration between them, compared with the integration of other modules of the hand. The high values of covariance revealed as a result of the study in pairwise comparison of the modules of the fingers and lower values of covariance in the multiple comparison between the modules of the hand indicate a different degree of local influence of morphogenetic factors on the structural features and shape of the hand in people of different sexes.

Which method is more effective in predicting adult height in pubertal girls treated with gonadotropin-releasing hormone agonist?

The aim of the present study was to determine the efficiency of three different predictive models [Bayley-Pinneau (BP), Roche-Wainer-Thissen (RWT), and Tanner-Whitehouse 2 (TW2)] by comparing their predictions with near-adult height data of girls receiving gonadotropin-releasing hormone agonist (GnRHa) therapy. Clinical findings were retrospectively analyzed. Bone ages obtained before treatment were evaluated from left hand and wrist radiographs by three researchers. Predicted adult height (PAH) was calculated using the BP, RWT, and TW2 methods for each patient at the beginning of therapy. The median age at diagnosis of the 48 patients included in the study was 8.8 (8.9-9.3) years. There was no significant difference between the mean bone ages evaluated separately with the Greulich-Pyle atlas and the TW3-RUS method (p=0.34). Among the PAH methods, only PAH measured by the BP method was very close to and no different from near adult height (NAH) [159.8±6.3 vs. 158.8±9.3 cm. p=0.3; (-0.5±1.1) vs. (-0.7±1.6) standard deviation score, p=0.1]. Accordingly, the BP method was found to be the most accurate prediction tool in girls with puberty treated with GnRHa. The BP method is more effective at predicting adult height than the RWT and TW2 methods in female patients who will receive GnRHa treatment.

Adaptive Critical Region Extraction Net via relationship modeling for bone age assessment

Bone age assessment is an efficient clinical practice to diagnose children’s endocrine, genetic, and growth disorders. Guided by clinical diagnostic methods, local critical regions cropped from original hand radiography images have significant biological identification meaning in skeletal maturity analysis. However, existing methods crop local regions without maintaining the relationship between cropped regions and the global image, which results in discarding essential information. To address this problem, we propose an Adaptive Critical Region Extraction Net (ACRE-Net) for bone age assessment, which can automatically locate local critical regions from global images and calibrate features by relationship modeling. Unlike existing two-stage methods, which crop global images in the first stage and analyze cropped parts in the second stage separately, our ACRE-Net maintains the relationships between global images and cropped regions and further integrates such relationship information into relationship-guided features. With the relationship-guided features, we propose a novel cross-graph feature calibration module which is consisted of a bigraph structure to supplementary and calibrate features extracted from cropped parts by also modeling the relationship between cropped parts. Meanwhile, we offer a novel localization strategy in the patch-based region localization module, which aggregates and leverages multi-scale attention maps to locate patches of interest as critical regions to be cropped. In practical application, our ACRE-Net can free clinicians from time-consuming manual annotations on local critical regions and automatically assess bone age through radiography images. Extensive experiments illustrate that our ACRE-Net achieves state-of-the-art performance with Mean Absolute Error (MAE) 3.78 months on the public RSNA2017 dataset.

Prevalence and Associated Factors for Primary Osteoarthritis of the Scaphotrapeziotrapezoid, Radiocarpal, and Distal Radioulnar Joints in the Japanese General Elderly Population

The incidence and etiology of primary osteoarthritis (OA) of the scaphotrapeziotrapezoid joint (STTJ), radiocarpal joint (RCJ), and distal radioulnar joint (DRUJ) remains unknown. The purpose of this study was to evaluate the prevalence and factors associated with primary wrist OA in a cross-sectional study of a basic resident registry. A total of 1,297 residents between the ages of 50 and 89 years were randomly sampled from the registry of a Japanese town. A questionnaire was administered to all subjects, and each of them underwent radiographs of the bilateral hands, wrists, and elbows. STTJ, RCJ, and DRUJ radiographic osteoarthritis (ROA) were evaluated according to a previously described method. Associated factors for STTJ and DRUJ ROA were recorded. Associations between the incidence of ROA of the DRUJ, ulnar variance, and severity of elbow ROA were investigated. A total of 676 wrists (162 men and 176 women; mean age of 69.0 years) were investigated. The prevalence of STTJ, RCJ, and DRUJ ROA was 5.3%, 1.5%, and 21.2%, respectively. Factors associated with STTJ ROA were thumb carpometacarpal joint ROA, female sex, and increasing age. Factors associated with DRUJ ROA were elbow ROA, use of vibrating tools, increasing age, and positive ulnar variance. Prevalence of DRUJ ROA was 54.4% in wrists with severe-grade elbow ROA. Ulnar variance of the wrist in severe-grade elbow ROA was significantly larger than that in mild-grade or nonelbow ROA. The prevalence of ROA was highest in the DRUJ, followed by the STTJ, and lowest in the RCJ. The occurrence of ROA of the STTJ and DRUJ was affected by the presence of ROA of the adjacent joint. Primary DRUJ ROA occurs at a moderate frequency, similar to primary ROA of other extremity joints; however, primary STTJ and RCJ ROA is rare.