Bones Of Hand Research Articles

Stratification of Hand Bone Fractures in the Kyrgyz Republic

The study aimed to stratify hand bone fractures in the Kyrgyz Republic based on patient data, including age, gender, and other clinical characteristics. Data from patients treated in 2019 were analyzed, with assessments made using key medical parameters such as fracture type, age, gender, blood group, and other laboratory indicators. A retrospective analysis of 200 hand fracture cases was conducted, classified by various clinical and demographic criteria. Conclusions were drawn based on statistical analysis of the data. Results: Significant differences in fracture incidence were observed between males and females, as well as across different age groups. The highest incidence was found in the working-age population, indicating a need for enhanced preventive measures in this demographic.

Structure of Medication Therapy in Patients with Complex Hand Bone Fractures in Kyrgyz Republic

This article analyzes the structure of medication therapy in patients with complex hand bone fractures in the Kyrgyz Republic. Complex hand fractures are common injuries requiring a multi-stage treatment approach, including surgery, immobilization, and adequate medication therapy. The article examines various therapeutic regimens used at different stages of treatment: the first-line therapy includes analgesics and anti-inflammatory drugs, the second-line therapy adds hormonal medications and tissue regeneration stimulants, and alternative methods incorporate biologically active agents and physiotherapy. Effectiveness was evaluated based on clinical indicators such as pain level, bone healing rate, complication frequency, and restoration of hand function. The article also discusses gender-specific differences in therapeutic approaches, necessitating the consideration of pharmacokinetics and pharmacodynamics in men and women. This study highlights the need for the development of standardized treatment protocols adapted to local healthcare conditions to improve treatment efficacy and reduce complications.

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.

Cone-beam CT volumetry: a new method for evaluating osteotomy healing - a clinical evaluation and MDCT comparison.

Fracture healing complications remain a major problem in trauma monitoring. An open wedge osteotomy of the distal radius provides a unique way of evaluating fracture healing. Since the introduction of cone-beam computed tomography (CBCT) at our institution, it has become the method of choice for assessing hand and wrist bones. To date, CT volumetry has been validated for multidetector CT (MDCT) but not for CBCT. To assess osteotomy healing using CBCT volumetry and to evaluate two different segmentation techniques. A total of 36 patients were surgically treated for malunited distal radius fractures with open-wedge osteotomy either leaving the void empty (open wedge empty [OWE]) or filled with bone graft substitutes (BGS). They were scanned using CBCT and MDCT postoperatively and after 3, 6, and 12 months. Segmentation was performed both manually and semi-automatically for volumetric measurement. Inter- and intra-observer reliability were assessed using intraclass correlation (ICC). The median osteotomy volume in the OWE group postoperatively was 0.87 cm3 (range=0.42-2.72). At 3 months, all but one of the OWE volumes had diminished to half or less of their initial volume. In the BGS group, the median postoperative volume was 1.30 cm3 (range=0.73-1.81) and at 12 months, 76% of the initial volume remained. Reliability between CBCT and MDCT volumetry expressed as ICC was ≥0.96. ICC for the two segmentation techniques was ≥0.99 and ICC for inter-observer reliability ≥0.97. CBCT volumetry is a reliable tool and comparable to MDCT to quantify bone healing of an osteotomy.

Recurrence and Risk Factors of Giant Cell Tumors in Hand Bones: A Systematic Review.

Giant cell tumor of bone (GCTB) is a locally aggressive tumor that may affect the bones of the hand and rarely causes pulmonary metastasis. It exhibits a variable recurrence rate after surgical interventions, which presents challenges in its management. This systematic review aims to delineate recurrence rates and identify risk factors for GCTB in the hand. We conducted a systematic literature search in April 2024, following PRISMA guidelines, on PubMed and TDNet for studies reporting postsurgical recurrence of GCTB in the hand. Cohort and case-control studies provided recurrence rates, whereas case reports and series were utilized to identify risk factors, compensating for the sparse data in the primary studies. We used descriptive statistics, χ2 tests, and logistic regression to analyze demographics, lesion characteristics, treatments, and outcomes. We reviewed 13 cohort and case-control studies involving 244 patients, finding an overall recurrence rate of 19.57%. Curettage was associated with higher recurrence rates compared with other surgical methods. After additional review of case reports, a limited range of motion in patients emerged as a significant protective factor against recurrence, suggesting potential benefits in surgical management and outcome prediction. The significant recurrence rate associated with curettage highlights the need for alternative surgical strategies in GCTB management of the hand. The protective role of limited ROM underscores the importance of thorough preoperative assessments to optimize surgical approaches and enhance patient outcomes.

Research on Mixed Reality Hand Interaction Technology in Flight Simulation Teaching

Abstract The integration of mixed reality simulation training in institutional education and teaching processes can yield significant quality benefits. Non-wearable interactive methods represent a more natural form of hand interaction within flight cockpits, covering various emerging fields such as mixed reality, computer vision, and human-computer interaction. This paper is guided by a “human-centered” approach to natural human-computer interaction and proposes a virtual hand mixed reality interaction scheme based on hand natural feature points. This study introduces a hand detection, segmentation, and recognition algorithm based on skin colour and key hand feature points. Initially, the algorithm uses the Otsu adaptive threshold algorithm in the YCbCr space for skin colour detection to accommodate varying image brightness levels. Subsequently, it employs a hand Keypoint model for rapid hand detection within skin-like areas while excluding interference from other regions. During the process of hand tracking, optimization prediction is performed using the particle filter algorithm combined with the artificial fish swarm algorithm to achieve tracking accuracy within six pixels in both horizontal and vertical directions. Gesture recognition involves extracting Fourier descriptive contour features of the hand bone structure through identification of feature points. Furthermore, this study combines the optimization capabilities of the artificial fish swarm algorithm to enhance support vector machine model parameter optimization for improved recognition rates. Testing involved 800 processed images as part of a test set for gesture recognition; only five commonly used gestures within flight cockpits were recognized and classified with an accuracy rate reaching up to 98%. Finally, this research presents virtual representations of common operational gestures including natural hand states, control stick manipulation, and throttle handling positions, button activations, and rotational movements via head-mounted displays or screens. The proposed mixed reality-based hand gesture interaction system addresses challenges related to complex background interference during hand detection in cockpit environments as well as issues arising from lighting disturbances. Additionally it resolves problems associated with insufficient particles leading to tracking failures while significantly enhancing registration effects during tracking processes. Moreover it mitigates issues related to Fourier descriptive features being influenced by background variations or changes in pose along with limitations pertaining to expressing singular forms of gestures thereby improving overall classification accuracy.

Design and Implementation of Gesture Music Composition System based on RealSense and Recurrent Neural Network

Aiming at the problems of slow human-computer interaction and low accuracy of hand gesture recognition (HGR) in music composition systems such as human-computer interaction, gesture recognition, and algorithmic composition, this paper designs an HGR music composition system based on real-sense technology and recurrent neural network. The system uses an Intel RealSense 3D camera to shoot the user's hand and extract the information on the hand joint points with Smart Wearable Biosensors (SWBSs). The joint points are used to construct a three-dimensional model of the hand bone, and the joint points' equivalent distance and joint deflection are calculated by the joint point information to match the corresponding Curwen HGR. The matching music data is input into the recurrent neural network. GRU is combined with the Markov chain algorithm to complete the composition and avoid the phenomenon of gradient disappearance or gradient explosion. The HGR recognition function and composition algorithm are simulated and tested to verify the method's feasibility. Cohort verification investigations and efficacy evaluations of wearable biosensors are essential to support their clinical acceptability. The research results show that the HGR music composition system based on real-sense technology and recurrent neural networks can effectively identify the tester's HGRs and assist the tester in completing the composition using intelligent biosensors. The composition mode is close to the professional composition process.

An intelligent bone age assessment model incorporating multilayer superimposed texture enhancement and the China-05 attention mechanism

In the process of intelligent bone age assessment for Chinese ethnicity, generalized convolutional network models are not well targeted in extracting specific features in medical skeletal images and lack specificity in training and predicting skeletal developmental features in different ethnicities. This study aims to propose a hybrid improved deep residual network model, ZH05-DL-ResNet50, focusing on intelligent bone age assessment for Chinese ethnicity. In the process of building the ZH05-DL-ResNet50 model. First, multiple overlapping texture enhancement layers are introduced through combinatorial optimization, which can better characterize the global features of hand bone radiographs while using less texture information, reducing the interference of redundant information and freeing up computational power; second, the China-05′s spatial focusing mechanism is designed so that the model can intelligently focus on the region of interest, efficiently locate and automatically learn key image information; Finally, the model can be used for intelligent bone age assessment of Chinese ethnicity by constructing a 50-layer residual depth network, the superposition enhancement layer and the focusing mechanism are fused to form the ZH05-DL-ResNet50 model, which mitigates the problem of gradient disappearance and explosion caused by the increase of network depth, and reduces the information loss and depletion of the multi-layer texture superposition features in the focus region of interest. The training was performed using the Chinese ethnographic dataset provided and created by Tsinghua Changgeng Hospital, where 10 % of the radiographs were used as the test set and the rest as the training and validation sets. The overall performance of the models was estimated by comparing the average accuracy and loss values of different models, and the overall performance between model training and model design was evaluated using the data distribution (Distribution) and histogram of bias weight distribution (Histogram). The results showed that the average accuracy of the bone age estimation model was 98.1 % and the average error on the test set was 0.312 years. Evaluation of the model visualization and accuracy comparison showed that the model performed well and was more appropriate and accurate than other bone age estimation models. Therefore, the first intelligent bone age assessment model for Chinese ethnicity, ZH05-DL-ResNet50, was created, which differs from the untargeted nature of other intelligent bone age assessment models, and is used to train and predict hand bone radiographs more appropriately and accurately for Chinese ethnicity.

A Hybrid Segmentation Algorithm for Rheumatoid Arthritis Diagnosis Using X-ray Images

Rheumatoid Arthritis (RA) is a chronic autoimmune illness that occurs in the joints, resulting in inflammation, pain, and stiffness. X-ray examination is one of the most common diagnostic procedures for RA, but manual X-ray image analysis has limitations because it is a time-consuming procedure and is prone to errors. A specific algorithm aims to a lay stable and accurate segmenting of carpal bones from hand bone images, which is vitally important for identifying rheumatoid arthritis. The algorithm demonstrates several stages, starting with Carpal bone Region of Interest (CROI) specification, dynamic thresholding, and Gray Level Co-occurrence Matrix (GLCM) application for texture analysis. To get the clear edges of the image, the component is first converted to the greyscale function and thresholding is carried out to separate the hand from the background. The pad region is identified to obtain the contours of it, and the CROI is defined by the bounding box of the largest contour. The threshold value used in the CROI method is given a dynamic feature that can separate the carpal bones from the surrounding tissue. Then the GLCM texture analysis is carried out, calculating the number of pixel neighbors, with the specific intensity and neighbor relations of the pixels. The resulting feature matrix is then employed to extract features such as contrast and energy, which are later used to categorize the images of the affected carpal bone into inflamed and normal. The proposed technique is tested on a rheumatoid arthritis image dataset, and the results show its contribution to diagnosis of the disease. The algorithm efficiently divides carpal bones and extracts the signature parameters that are critical for correct classification of the inflammation in the cartilage images.

Bone age estimation with HS-optimized Resnet and Yolo for child growth disorder

Bone Age Assessment (BAA) plays a significant part in assessing skeletal maturity for preventing different types of skeletal disorders in the human body. Predicting BAA helps clinicians make decisions about the patient’s biological maturity. An increase or decrease in bone age (BA) causes serious issues for pediatricians who need advanced equipment for BA consistency with the chronological age. Recent techniques fail to accurately estimate the region over interest (ROI) of hand-wrist images to predict BA under low time complexity accurately. Hence, this article brings novel deep learning (DL) techniques to assess the BA to overcome child growth disorders accurately. The proposed methodology undergoes three major stages: pre-processing, ROI detection, and BA detection. In the pre-processing stage, contrast-limited fuzzy adaptive histogram equalization (CLFAHE) is done to maintain the originality of the hand image. The size of the image is also increased by using data augmentation. After pre-processing, the required hand bone region with ROI detection approaches you only live once version 3 (YOLOv3). At the final stage, the BA is predicted using the residual network-152 (ResNet152) technique. Finally, the weight updation takes place in the ResNet152 model using harmony search (HS) optimization techniques. The dataset used in this research is collected from the RSNA bone age dataset. The performance measures such as mean absolute error (MAE), root mean square error (RMSE), and root mean squared percentage error (RMSPE) are analyzed and compared with other existing measures. In an experimental scenario, the MAE of 2.59, RMSE of 2.97, and RMSPE of 0.35 for the male gender is obtained.

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

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

Applying the V.E.R.A. method to entheses of the humerus: An assessment of repeatability and reproducibility

AbstractThe “Validated Entheses‐based Reconstruction of Activity” (V.E.R.A.) method has proven to be a reliable method for the quantification and analysis of entheses. However, this method has only been successfully applied to human hand bones and animal long bones. This study tests the general applicability of the V.E.R.A. method to the human humerus, focusing on evaluating its reliability and repeatability. The sample included 23 humeri of unknown origin and background. The V.E.R.A. method was applied to six entheses of the humerus, comprising the subscapularis, supraspinatus, infraspinatus, teres minor, common extensor origin, and common flexor origin. Lin's concordance correlation coefficient (CCC) and percentage of error statistics were used to measure intraobserver error. Interobserver error was assessed through analysis of variance (ANOVA) tests, the intraclass correlation coefficient (ICC), and Lin's CCC. Intraobserver rates were between 1.83% and 3.23%. Interobserver error values were excellent, with no significant differences found in the ANOVA tests; Lin's CCC values were all above 0.90, with only the common extensor origin being 0.87; and ICC values were all above 0.97. Our results showed that the V.E.R.A. method is highly reliable and reproducible for the six entheses of the human humerus selected for this study. These results demonstrate the vast potential for the V.E.R.A. method to be applied to other parts of the human skeleton.

Comparative anatomical characteristics of the distal parts of bear and human limbs

BACKGROUND: The comparative morphology of the human and animal skeletons has been incompletely described in the forensic medical and anthropological literature. Moreover, bones of the distal parts of bear limbs are anatomically similar to those of humans. Together with some features of the bear’s skeleton, poor preservation, absence of claws, and incomplete remains, difficulties and errors may occur during identification. AIM: To create an illustrative material describing morphological features important for the identification of each element of the distal parts of bear limbs in comparison with humans. MATERIALS AND METHODS: Preparations of the distal parts of the right thoracic and right pelvic extremities of the bear were made using osteological materials. The missing talon phalanges of a bear and bones of a human hand and foot were taken from the institute collections. The International Veterinary Anatomical Nomenclature was used to describe the anatomical features of bear bones, and the latest recommendations of International Anatomical Terminology were considered for human bones. RESULTS: Each bone of the bear’s hand and foot was described in comparison with a similar human bone. For greater versatility, descriptions were made in terms of the international zoological nomenclature. For all bones, except for distal sesamoid bones, high-quality photos are provided for aspects that are important for identification. Comparative anatomical analysis showed that the bones of the wrist differ to a greater extent, whereas all tarsal bones, which are part of the human foot, had analogs in the bear foot and had closer measurements. The articular surfaces of the heads of the metacarpals and metatarsals showed specific ridges articulating with the cutouts at the bases of the proximal phalanges of the fingers. In addition, the bear’s hand and foot contained numerous inset sesamoid bones and claw-like processes on the distal phalanges of the fingers. CONCLUSION: Comparative anatomical analysis showed similarities in the structures of the bones of the hand and foot of a brown bear and a human caused by foot walking. Owing to the morphological similarity, bone identification can be difficult. The set of features described in the article, which are specific to bear bones, in combination with illustrative material will help in identifying bones more accurately, even for individual bones.

Sex estimation using Magnetic Resonance Imaging measurements of hand and foot in Egyptian population

BackgroundThe identification of skeletal remains begins with a sex evaluation since other biological profile elements, such as size and age, are sex-specific. The pelvis is the favored element for sex assessment since it is very sexually dimorphic.Aim of the studyDelivering hand and foot bones sex estimate criteria for Egyptian population identification was the goal of this work.Subject and methodsEighty-two Egyptian adult subjects (41 males and 41 females) aged ≥ 21 years old were subjected to MRI scan on the right hand and foot to assess 9 measurements: hand length, hand width, four measurements of middle metacarpal bone (length, breadth, head breadth and base breadth), first metatarsal bone length, first metatarsal bone width and (first metatarsal bone length x first metatarsal bone width).ResultsAll the measured parameters in the right hand and foot were higher in males than females. First metatarsal bone length x first metatarsal bone width in the foot and middle metacarpal bone breadth in the hand were the most sexually dimorphic parameters, with accuracy of 86.6 percent and 84.1 percent, respectively. In stepwise discriminant analysis, three of nine measurements were selected: First metatarsal bone length x first metatarsal bone width, middle metacarpal bone breadth and hand width for the sex prediction equation in the Egyptian population, with a cross-validated sex classification accuracy of 90.2%.ConclusionSex can be assessed from hand and foot metric parameters measured by MRI with high accuracy.

Analysis of the relationship between solar greenhouse operation and occupational high incidence diseases

Objective: To understand the health status of solar greenhouse workers, to provide scientific basis for the development of occupational high incidence diseases prevention and control strategies. Methods: In July 2019, a random cluster sampling method was used to select 245 workers engaged in solar greenhouse vegetable cultivation in Daba Village, Jingyuan County, Baiyin City, Gansu Province as the solar greenhouse operation group. Matched by gender, age, marital status, body mass index (BMI), 282 people from adjacent Shaliang Village who did not engage in solar greenhouse operation were selected as the control group. Field investigation and health examination were carried out among the study subjects. The general situation, facial features examination results, ophthalmic examination results, bone and joint examination results and skin examination results were compared between the two groups. And the multiple logistic regression analysis was used to analyze the influencing factors of abnormal bone and joint examination (upper limbs, lower limbs, hands and spine bone joints) in study subjects. Results: There were statistically significant differences in smoking age and alcohol consumption between the two groups (P<0.001). Compared with the control group, the abnormal detection rates of nose examination, throat examination, slit lamp examination, conjunctival examination, lower limb bone joint examination, hand bone joint examination, spine examination, head and neck skin examination, trunk skin examination, upper limb skin examination, and lower limb skin examination in the solar greenhouse operation group were higher, and the differences were statistically significant (χ(2)=11.53, 7.94, 9.92, 27.93, 79.32, 81.42, 9.43, 6.79, 9.76, 4.34, 8.29, P<0.05). Multivariate logistic regression analysis showed that after adjusting for gender, age, marital status, education level, BMI, compared with the control group, solar greenhouse operation was a risk factor for abnormal bone and joint examination (OR=1.178, 95%CI: 1.151-2.143, P=0.001) . Conclusion: Solar greenhouse operation has a certain harmful effect on health of workers, and solar greenhouse workers have an increased risk of abnormal diseases of upper limbs, lower limbs, hands and spine bone joints.

A rare case report of Maffucci syndrome in a 16-year-old girl

Maffucci syndrome is an extremely rare, sporadic disorder that primarily affects bone and skin. It is characterized by multiple enchondromas, especially in the bones of hands and feet, with cutaneous hemangiomas. Treatment consists of orthopedic and surgical intervention to minimize deformities and for cosmetic purposes. Careful surveillance for both skeletal and non-skeletal malignancies is needed. We report here a case of Maffucci syndrome in a 16-year-old girl.

Cross-sectional and longitudinal analysis of bone age maturation during peri-pubertal growth in children with type I, III and IV osteogenesis imperfecta

Osteogenesis imperfecta (OI)is a rare genetically heterogeneous disorder caused by changes in the expression or processing of type I collagen. Clinical manifestations include bone fragility, decreased linear growth, and skeletal deformities that vary in severity. In typically growing children, skeletal maturation proceeds in a predictable pattern of changes in the size, shape, and mineralization on the hand and wrist bones that can be followed radiographically known at the bone age. Assessment of bone age can be clinically used to assess time remaining for linear growth, and the onset and duration of puberty, both of which can be useful in determining the timing of some surgeries or the interpretation of other imaging modalities such as bone densitometry. Additionally, deviations in the expected maturation process of the bone age may prompt or assist in the work up of a significant delay or advancement in a child's growth pattern. The primary aim of our study was to determine whether the bone age in children with a skeletal disorder such as OI follow the same pattern and rate of bone maturation compared to a control population. Using participants from the Natural History Study of the Brittle Bone Disorders Consortium, we analyzed 159 left hand and wrist radiographs (bone age) for a cross-sectional analysis and 55 bone ages repeated at approximately 24 months for a longitudinal analysis of skeletal maturation. Bone ages were read by a pediatric endocrinologist and by an automated analysis using a program called BoneXpert. Our results demonstrated that in children with mild-to-moderate OI (types I and IV), the skeletal maturation is comparable to chronological age-mated controls. For those with more severe forms of OI (type III), there is a delayed pattern of skeletal maturation of less than a year (10.5 months CI 5.1–16) P = 0.0012) at baseline and a delayed rate of maturation over the two-year follow up compared to type I (P = 0.06) and type III (P = 0.02). However, despite these parameters being statistically different, they may not be clinically significant. We conclude the bone age, with careful interpretation, can be used in the OI population in a way that is similar to the general pediatric population.

Radiological Assessment of Hand and Wrist Bones in Malnourished Children

Abstract Background Protein energy malnutrition (PEM) is still an important health problem that is responsible for childhood morbidity and mortality leading to permanent impairment of physical and mental growth. Objective This study is designed to assess the appearance of ossification centers of lower end of radius and ulna, carpals, metacarpals and phalanges in normal children and malnourished children then comparing between them, to detect early changes in bone growth in malnourished children. Subjects and Methods This case-control cross-sectional study included 45 malnourished children and 45 age and sex matched controls whose ages ranged between 6 months and 5 years. The patients were recruited from Pediatric Nutrition Clinic of Ain Shams University. All children (malnourished and control) were examined for anthropometric parameters (weight, height, body mass index (BMI) and mid upper arm circumference (MUAC)), investigated for serum calcium, phosphorus, alkaline phosphates and had a plain X-ray done on left hand and wrist (posteroanterior (P-A) view), the X-ray was analyzed by using A Digital Atlas of Skeletal Maturity. Results The plain X-ray hand and wrist showed delayed appearance of ossification centers in malnourished infants and children (97.8% had delayed bone age) when compared to controls (25.5% had delayed bone age) which was statistically highly significant with P-value &amp;lt; 0.01. Signs of vitamin D deficiency rickets together with serum calcium, phosphorus and alkaline phosphatase were statistically significantly lower in the cases when compared to the controls with P-value &amp;lt; 0.01. The statistical measurements of mean ± SD regarding weight of case group and control group was (9.05 ± 2.48, 13.54 ± 3.81) with P-value &amp;lt; 0.01 which is highly significant, regarding length or height was (79.51 ± 12.78, 88.94 ± 15.02) with P-value &amp;lt; 0.01 which is highly significant, regarding MUAC was (11.38 ± 1.91, 16.02 ± 1.26) with P-value &amp;lt; 0.01 which is highly significant. Conclusion X-ray hand and wrist for malnourished infants and children showed delayed appearance of ossification centers when compared to matched controls.

Adaptive Multi-Dimensional Weighted Network With Category-Aware Contrastive Learning for Fine-Grained Hand Bone Segmentation.

Accurately delineating and categorizing individual hand bones in 3D ultrasound (US) is a promising technology for precise digital diagnostic analysis. However, this is a challenging task due to the inherent imaging limitations of the US and the insignificant feature differences among numerous bones. In this study, we have proposed a novel deep learning-based solution for pediatric hand bone segmentation in the US. Our method is unique in that it allows for effective detailed feature mining through an adaptive multi-dimensional weighting attention mechanism. It innovatively implements a category-aware contrastive learning method to highlight inter-class semantic feature differences, thereby enhancing the category discrimination performance of the model. Extensive experiments on the challenging pediatric clinical hand 3D US datasets show the outstanding performance of the proposed method in segmenting thirty-eight bone structures, with the average Dice coefficient of 90.0%. The results outperform other state-of-the-art methods, demonstrating its effectiveness in fine-grained hand bone segmentation. Our method will be globally released as a plugin in the 3D Slicer, providing an innovative and reliable tool for relevant clinical applications.

Design of bifunctional composite bone age prediction network based on Swin-Transformer

Abstract Bone age can be predicted by taking hand X-rays. Prediction of bone age is a labor-intensive and time-consuming radiological clinical task. This paper combined a series of prevailing deep learning methods to address this problem. Stages1~3 of Swin Transformer served as backbone, with the multi-scale neck network and detection head of Yolox connected to it. These formed the first period’s hand bone joints detector. After finishing detector training, the pre-trained Stages1~3 were frozen for the second period’s developmental grades classification of corresponding bone joint. Additionally, a linear classification head was attached to Swin Transformer’s stage4, where it functioned as second period’s classifier for different developmental grades. Therefore, a dual-purpose composite network was created like this. It made the bone age prediction model have high integrated level, and the two periods could be applied fusion training. In addition, different attention mechanisms were introduced at different positions, loss functions and optimization methods were also redesigned to ensure improvement of network performance. In the hand bone joints detection period, compared to the original Yolox-X, there was a 5.27% increase in Ap@50 and a 40.12% increase in Ap@50:95. As for the developmental grade classification period, the validation accuracy surpassed that of EfficientNetV2-L by 5.18%, with one-third the training size.