Detection Of Osteoporosis Research Articles

Radiomics for osteoporosis detection – current opportunities and prospects: a literature review

Background. The prevalence of osteoporotic fractures continues to increase as the population ages and undergoes a demographic transition, which is relevant for developed countries, including the Russian Federation. Radiomics may become a valuable tool for detecting osteoporosis in the future. Aim. This literature review aims to demonstrate the development and application of radiomic analysis in the diagnosis of oncological and non-oncological diseases, and for the diagnosis of osteoporosis. Materials and methods. The literature corresponding to the topic of the review was searched in the PubMed, Google Scholar and eLibrary over the past five years. Data on the prevalence and epidemiology of osteoporosis were taken from articles over the past fifteen years. The databases were searched for keywords: radiomic, osteoporosis, texture, MRI, CT, non-oncological radiomics, texture analysis, radiomic analysis. The review includes data from original articles. As a result, 247 articles were identified, of which 59 studies were included in the analysis. Conclusion. Currently, there are a limited number of studies exploring the possibilities of radiomic analysis for the detection of osteoporosis. Further research is needed on the radiomic analysis of CT and MRI images to detect osteoporosis, in comparison with recognized existing techniques – DRA, FRAX.

FREQUENCY OF OSTEOPOROSIS IN CHRONIC LIVER DISEASE PATIENTS

Chronic liver disease (CLD) is a significant health concern often associated with systemic complications, including osteoporosis. Early detection of osteoporosis in CLD patients is critical for preventing fractures and improving quality of life. Objective: To determine the frequency of osteoporosis in patients with chronic liver disease (CLD).Methods: This cross-sectional study was carried over six months, from May 2024 to November 2024, and included 80 patients with confirmed CLD. Participants aged 30 to 60 years were recruited from outpatient and inpatient settings. Dual-energy X-ray absorptiometry (DEXA) was used to measure bone mineral density (BMD), which was then classified into normal or osteoporosis categories based on T-scores. Demographic and clinical data were collected through structured evaluations. Results: Mean age was 46.15 ± 9.46 years. Among the cohort, 20 patients (25.0%) had osteoporosis. Osteoporosis was associated with increasing age (p = 0.001), female gender (p = 0.004), lower BMI (p = 0.0001) and Child-Pugh Class (p = 0.02).Conclusion: Osteoporosis is a prevalent complication in CLD, associated with older age, female gender, lower BMI, and advanced disease severity.

Osteoporosis Disease Detection using Optimized Elman Recurrent Neural Network based on Hybrid Bacterial Colony Optimization and Tabu Search Algorithm

Bone loss and fragility are indications of osteoporosis, a condition caused by calcium deficiency. The detection of osteoporosis is a significant and difficult diagnostic endeavor. Elman recurrent neural network (ERNN) is a well-known medical disease detection method due to its modeling sequential data and capturing temporal dependencies. ERNN training can be computationally costly and necessitates precise adjustment of hyperparameters. In this research, optimized ERNN is used to predict osteoporosis diseases to achieve high detection accuracy and to improve the global convergence rate. The new hybrid method is used to optimize the hyperparameters of ERNN based on the bacterial colony optimization (BCO) and tabu search (TS) algorithm, which is called IBCO-ERNN. The hybrid technique can efficiently explore the solution space by combining BCO's global exploration capabilities and TS's local exploitation capability, perhaps leading to better solutions to hyperparameter optimization problems. The hybrid BCO-TS strategy trains the ERNN model to prevent local optima and improve convergence rate. The experimental results demonstrated that the proposed IBCO-ERNN obtained high accuracy and fast convergence compared to other detection methods.

Osteoporosis Detection Using a Combination of Recursive Feature Elimination and Naive Bayes Classifier with Rule-Based Chatbot Testing

Osteoporosis Detection Using a Combination of Recursive Feature Elimination and Naive Bayes Classifier with Rule-Based Chatbot Testing

Bonevoyage: Navigating the depths of osteoporosis detection with a dual-core ensemble of cascaded ShuffleNet and neural networks

Background Osteoporosis (OP) is a condition that significantly decreases bone density and strength, often remaining undetected until the occurrence of a fracture. Timely identification of OP is essential for preventing fractures, reducing morbidity, and enhancing the quality of life. Objective This study aims to improve the accuracy, speed, and reliability of early-stage osteoporosis detection by integrating the compact architecture of Cascaded ShuffleNet with the pattern recognition prowess of Artificial Neural Networks (ANNs). Methods BoneVoyage leverages the efficiency of ShuffleNet and the analytical capabilities of ANNs to extract and analyze complex features from bone density scans. The framework was trained and validated on a comprehensive dataset containing thousands of bone density images, ensuring robustness across diverse cases. Results This model achieving an accuracy of 97.2%, with high sensitivity and specificity. These results significantly surpass those of existing OP detection methods, highlighting the effectiveness of the BoneVoyage framework in identifying subtle changes in bone density indicative of early-stage osteoporosis. Conclusions BoneVoyage represents a significant advancement in the early detection of osteoporosis, offering a reliable tool for healthcare providers to identify at-risk individuals prematurely. The early detection facilitated by BoneVoyage allows for the implementation of preventive measures and targeted treatments.

HarDNet-based deep learning model for osteoporosis screening and bone mineral density inference from hand radiographs

PurposeOsteoporosis, affecting over 200 million individuals, often remains unrecognized and untreated, increasing the risk of fractures in older adults. Osteoporosis is typically diagnosed with bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA). This study aims to develop DeepDXA-Hand, a deep learning model using the efficient CNN-based deep learning architecture, for opportunistic osteoporosis screening from hand radiographs. MethodsDeepDXA-Hand utilizes a CNN-based, HarDNet, approach to predict BMD non-invasively. A total of 10,351 hand radiographs and DXA pairs were used for model training and validation. The model's interpretability was enhanced using GradCAM for hotspot analysis to determine the model's attention areas. ResultsThe predicted and ground truth BMD were significantly correlated with a correlation coefficient of 0.745. For binary classification of osteoporosis, DeepDXA-Hand demonstrated a sensitivity of 0.73, specificity of 0.83, and accuracy of 0.80, indicating its clinical potential. The model mainly focused on the carpal bones, such as the capitate, trapezoid, hamate, triquetrum, and the head of the second metacarpal bone, suggesting these areas provide radiological features for inferring BMD. ConclusionDeepDXA-Hand shows potential for the early detection of osteoporosis with high sensitivity and specificity. Further studies should explore its utility in predicting fracture risks. Mini abstractOsteoporosis affects millions and often goes undetected and untreated. DeepDXA-Hand, a HarDNet-based deep learning model, predicted bone mineral density with a correlation of 0.745 and classified osteoporosis with 0.80 accuracy. This model enhances early detection and has significant clinical potential as osteoporosis opportunistic screening tool.

Screening ability of dental students to detect osteoporosis on dental panoramic radiographs

ObjectivesPostmenopausal women with osteoporosis are frequently underdiagnosed. In Japan, general dental practitioners have begun using the cortical shape of the mandible on dental panoramic radiographs (PRs) to identify and refer women at risk of osteoporosis to medical professionals. It remains unclear whether dental students, after relevant education, possess the ability to identify these at-risk individuals. This study evaluated the ability of dental students to screen for osteoporosis on PRs. MethodsA cohort of 113 fifth-year dental students participated in a lecture on osteoporosis screening using PRs. The students then categorized the mandibular inferior cortex on PRs from 30 postmenopausal women (11 with osteoporosis) as normal, mildly to moderately eroded, or severely eroded. Interobserver agreement between the students and an expert oral radiologist using two cortical groups (normal to moderately eroded and severely eroded) was assessed through kappa statistics. Screening ability for osteoporosis detection by identifying severely eroded cortices was calculated and compared with that of the Osteoporosis Self-assessment Tool for Asians (OSTA). ResultsTwenty-one (18.6%) students demonstrated moderate to substantial agreement with the expert. The mean sensitivity, specificity, and positive and negative predictive values for these students in identifying osteoporosis were 60.2%, 88.0%, 76.3%, and 80.0%, respectively. For the OSTA, sensitivity, specificity, and positive and negative predictive values were 27.3%, 68.4%, 33.3%, and 61.9%, respectively. ConclusionsEven with minimal education, approximately one-fourth of dental students may accurately identify postmenopausal women with osteoporosis on PRs, outperforming questionnaire-based screening tools.

INVESTIGATING OSTEOPENIA IN POSTMENOPAUSAL WOMEN USING DEXA SCANS

Aims and Objectives:This examines foremost goal is to evaluate bone mineral density (BMD) in women after menopause through Dual-energy X-ray Absorptiometry (DEXA) scans. It targets to find out how common osteopenia and osteoporosis are. The studies attempt to understand the link among BMD measurements at the femoral neck and lumbar spine. It additionally appears at how things like age, weight, peak, and frame mass index (BMI) have an impact on BMD. whats greater, the observe plans to examine the T-score spread to diagnose osteopenia and osteoporosis. , it desires to explore the hazard elements that lead to bone loss after menopause. Materials and Methods: This carried out this forward-looking cross-sectional study in the Radio-Imaging and Diagnosis Department at A Medical College Haryana. The study looked at 45 women after menopause ranging from 45 to 80 years old, which may or may not have had osteopenia. A Hologen Discovery DEXA gadget measured their BMD. T-rankings and Z-rankings helped institution members as ordinary, osteopenic, or osteoporotic following the sector fitness companys (WHO) device. The group gathered data on age, weight, top, and BMI, linking those to BMD consequences. They did not consist of patients with current breaks long-term illnesses, or those taking bone-associated capsules. Discussion: The research shows that osteopenia and osteoporosis occur often in women after menopause. The data points to a strong link between BMD in the femoral neck and lumbar spine, and both areas have positive connections with T-scores. Doctors found osteopenia in 32.56% of patients, while 34.88% had osteoporosis. These numbers stress the need to diagnose and provide preventive care for women who might lose bone due to less estrogen after menopause. Other studies back up the use of DEXA scans as a key way to spot early bone density loss, which can help lower the chance of breaks and make life better. Conclusion: DEXA scans are an critical diagnostic device for assessing bone mineral density in postmenopausal girls. They have a look that concludes that ordinary BMD screenings ought to be advocated for early detection of osteopenia and osteoporosis, taking into consideration a timely intervention which includes calcium and nutrition D supplementation, hormone replacement therapy, and life-style modifications. By identifying and dealing with these situations early, the danger of fractures and associated morbidity in postmenopausal girls may be significantly decreased.

Predictive Diagnostic Model for Early Osteoporosis Detection Using Deep Learning and Multimodal Imaging Data: A Systematic Review and Meta-Analysis

Osteoporosis is a common condition that weakens bones, making them more prone to fractures. Early detection is crucial for preventing fractures and improving patients’ quality of life. However, traditional methods, such as Dual-Energy X-ray Absorptiometry (DXA), often struggle to accurately predict fracture risk and may overlook minor changes in bone structure. This study focuses on developing a predictive model for early osteoporosis detection using deep learning algorithms combined with various imaging techniques, including MRI, CT, and High-Resolution Peripheral Quantitative Computed Tomography (HR-pQCT). A systematic review and meta-analysis of studies published between 2014 and 2024 were conducted, examining the use of deep learning models applied to multimodal imaging data. The meta-analysis highlighted differences in the accuracy and effectiveness of various models, and their performance was measured in terms of accuracy, sensitivity, and specificity, following PRISMA guidelines. The results showed that deep learning models outperformed traditional methods in early osteoporosis detection. The use of multiple imaging techniques provided a more detailed assessment of bone health, allowing the models to identify complex patterns that are difficult for human interpretation. These models demonstrated high accuracy and significant potential for improving clinical decision-making. By integrating deep learning with multimodal imaging, this approach offers a promising solution for enhancing the early detection of osteoporosis. The models tested in this study proved to be highly effective, yielding more accurate fracture risk predictions and enabling earlier interventions. This could lead to better patient outcomes and reduced healthcare costs.

AI-Powered Identification of Osteoporosis in Dental Panoramic Radiographs: Addressing Methodological Flaws in Current Research.

Background: Osteoporosis, a systemic skeletal disorder, is expected to affect 60% of women over 50. While dual-energy X-ray absorptiometry (DXA) scans are the current gold standard for diagnosis, they are typically used only after fractures occur, highlighting the need for early detection tools. Initial studies have shown panoramic radiographs (PRs) to be a potential medium, but these have methodological flaws. This study aims to address these shortcomings by developing a robust AI application for accurate osteoporosis identification in PRs. Methods: A total of 348 PRs were used for development, 58 PRs for validation, and 51 PRs for hold-out testing. Initially, the YOLOv8 object detection model was employed to predict the regions of interest. Subsequently, the predicted regions of interest were extracted from the PRs and processed by the EfficientNet classification model. Results: The model for osteoporosis detection on a PR achieved an overall sensitivity of 0.83 and an F1-score of 0.53. The area under the curve (AUC) was 0.76. The lowest detection sensitivity was for the cropped angulus region (0.66), while the highest sensitivity was for the cropped mental foramen region (0.80). Conclusion: This research presents a proof-of-concept algorithm showing the potential of deep learning to identify osteoporosis in dental radiographs. Furthermore, our thorough evaluation of existing algorithms revealed that many optimistic outcomes lack credibility when subjected to rigorous methodological scrutiny.

Enhanced Osteoporosis Detection Using Artificial Intelligence: A Deep Learning Approach to Panoramic Radiographs with an Emphasis on the Mental Foramen.

Osteoporosis, a skeletal disorder, is expected to affect 60% of women aged over 50 years. Dual-energy X-ray absorptiometry (DXA) scans, the current gold standard, are typically used post-fracture, highlighting the need for early detection tools. Panoramic radiographs (PRs), common in annual dental evaluations, have been explored for osteoporosis detection using deep learning, but methodological flaws have cast doubt on otherwise optimistic results. This study aims to develop a robust artificial intelligence (AI) application for accurate osteoporosis identification in PRs, contributing to early and reliable diagnostics. A total of 250 PRs from three groups (A: osteoporosis group, B: non-osteoporosis group matching A in age and gender, C: non-osteoporosis group differing from A in age and gender) were cropped to the mental foramen region. A pretrained convolutional neural network (CNN) classifier was used for training, testing, and validation with a random split of the dataset into subsets (A vs. B, A vs. C). Detection accuracy and area under the curve (AUC) were calculated. The method achieved an F1 score of 0.74 and an AUC of 0.8401 (A vs. B). For young patients (A vs. C), it performed with 98% accuracy and an AUC of 0.9812. This study presents a proof-of-concept algorithm, demonstrating the potential of deep learning to identify osteoporosis in dental radiographs. It also highlights the importance of methodological rigor, as not all optimistic results are credible.

Vertebral bone quality score as a new tool for osteoporosis diagnosis in patients undergoing lumbosacral fusion surgery: a single center cohort study.

Osteoporosis, a common bone disorder, increases fracture and spinal surgery complications risk. This study evaluates the potential of the Magnetic Resonance Imaging (MRI)-based Vertebral Bone Quality (VBQ) score as an alternative for assessing bone density in lumbosacral fusion surgery patients. In a prospective cohort study from April 2021 to November 2022, 134 patients with lumbar degenerative diseases underwent lumbar Dual-Energy X-ray Absorptiometry (DXA) and lumbosacral non-contrast T1-weighted MRI. VBQ scores were calculated and analyzed using IBM SPSS Statistics and MedCalc software. Osteopenia/Osteoporosis patients exhibited significantly higher VBQ scores than normal bone mineral density patients (3.37 ± 0.51 vs. 2.99 ± 0.44, P<0.001). VBQ correlated significantly with lumbar DXA T scores (r value= -0.415, p<0.001). Receiver Operating Characteristic (ROC) analysis showed VBQ AUC values of 0.730 (CI 95% 0.647-0.803, P<0.001) and 0.839 (CI 95% 0.765-0.897, P<0.001) for Osteopenia/Osteoporosis and osteoporosis diagnosis, respectively. The study suggests MRI-derived VBQ scores may benefit pre-lumbosacral fusion surgery bone density assessment, potentially improving patient care and aiding osteoporosis detection in spinal surgery candidates.

Screening of genes co-associated with osteoporosis and chronic HBV infection based on bioinformatics analysis and machine learning.

To identify HBV-related genes (HRGs) implicated in osteoporosis (OP) pathogenesis and develop a diagnostic model for early OP detection in chronic HBV infection (CBI) patients. Five public sequencing datasets were collected from the GEO database. Gene differential expression and LASSO analyses identified genes linked to OP and CBI. Machine learning algorithms (random forests, support vector machines, and gradient boosting machines) further filtered these genes. The best diagnostic model was chosen based on accuracy and Kappa values. A nomogram model based on HRGs was constructed and assessed for reliability. OP patients were divided into two chronic HBV-related clusters using non-negative matrix factorization. Differential gene expression analysis, Gene Ontology, and KEGG enrichment analyses explored the roles of these genes in OP progression, using ssGSEA and GSVA. Differences in immune cell infiltration between clusters and the correlation between HRGs and immune cells were examined using ssGSEA and the Pearson method. Differential gene expression analysis of CBI and combined OP dataset identified 822 and 776 differentially expressed genes, respectively, with 43 genes intersecting. Following LASSO analysis and various machine learning recursive feature elimination algorithms, 16 HRGs were identified. The support vector machine emerged as the best predictive model based on accuracy and Kappa values, with AUC values of 0.92, 0.83, 0.74, and 0.7 for the training set, validation set, GSE7429, and GSE7158, respectively. The nomogram model exhibited AUC values of 0.91, 0.79, and 0.68 in the training set, GSE7429, and GSE7158, respectively. Non-negative matrix factorization divided OP patients into two clusters, revealing statistically significant differences in 11 types of immune cell infiltration between clusters. Finally, intersecting the HRGs obtained from LASSO analysis with the HRGs identified three genes. This study successfully identified HRGs and developed an efficient diagnostic model based on HRGs, demonstrating high accuracy and strong predictive performance across multiple datasets. This research not only offers new insights into the complex relationship between OP and CBI but also establishes a foundation for the development of early diagnostic and personalized treatment strategies for chronic HBV-related OP.

Knee Osteoporosis Diagnosis Based on Deep Learning

Osteoporosis, a silent yet debilitating disease, presents a significant challenge due to its asymptomatic nature until fractures occur. Rapid bone loss outpaces regeneration, leading to pain, disability, and loss of independence. Early detection is pivotal for effective management and fracture risk reduction, yet current diagnostic methods are time-consuming. Despite its importance, research addressing early diagnosis remains limited. Deep learning, particularly convolutional neural networks (CNNs), has emerged as a potent tool in image analysis. This paper presents a novel approach utilizing transfer learning with CNNs for osteoporosis detection from X-ray images. The proposed approach not only achieves a high accuracy of osteoporosis diagnosis but also offers a revealed feature map that can guide medical professionals for osteoporosis diagnosis. The innovation lies in a dual strategy: (i) a model integrating transfer learning from CNN architectures such as AlexNet, VGG-16, ResNet-50, VGG-19, InceptionNet, XceptionNet, and a custom CNN, and (ii) a dataset collection augmentation mechanism to enhance learning accuracy. The study includes binary and multiclass classification of knee joint X-ray images into normal, osteopenia, and osteoporosis groups, utilizing a dataset of 1947 knee X-rays for training and testing. Performance comparisons against state-of-the-art models reveal the proposed VGG-19 model achieves the highest accuracy at 92.0% for multiclass and 97.5% for binary. These findings underscore the potential of deep learning with transfer learning in aiding early osteoporosis detection, thereby mitigating fracture risks.

Subject-level spinal osteoporotic fracture prediction combining deep learning vertebral outputs and limited demographic data.

Osteoporotic vertebral fractures are common and morbid. Automated opportunistic screening for incidental vertebral fractures from radiographs, the highest volume imaging modality, could improve osteoporosis detection and management. We consider how to form patient-level fracture predictions and summarization to guide management, using our previously developed vertebral fracture classifier on segmented radiographs from a prospective cohort study of US men (MrOS). We compare the performance of logistic regression (LR) and generalized additive models (GAM) with combinations of individual vertebral scores and basic demographic covariates. Subject-level LR and GAM models were created retrospectively using all fracture predictions or summary variables such as order statistics, adjacent vertebral interactions, and demographic covariates (age, race/ethnicity). The classifier outputs for 8663 vertebrae from 1176 thoracic and lumbar radiographs in 669 subjects were divided by subject to perform stratified fivefold cross-validation. Models were assessed using multiple metrics, including receiver operating characteristic (ROC) and precision-recall (PR) curves. The best model (AUC-ROC = 0.968) was a GAM using the top three maximum vertebral fracture scores and age. Using top-ranked scores only, rather than all vertebral scores, improved performance for both model classes. Adding age, but not ethnicity, to the GAMs improved performance slightly. Maximal vertebral fracture scores resulted in the highest-performing models. While combining multiple vertebral body predictions risks decreasing specificity, our results demonstrate that subject-level models maintain good predictive performance. Thresholding strategies can be used to control sensitivity and specificity as clinically appropriate.

Magnetic resonance imaging phantom-based S1 vertebral scores are indicators of fat-water-like osteoporotic changes in postmenopausal women: a pilot study.

A prospective study. To assess fat-water-like tissue changes on the 1st sacral vertebra using novel magnetic resonance imaging (MRI) phantombased F- and W-scores and evaluate their diagnostic performances in osteoporosis detection. Using an uncommonly advanced MRI technique, previous studies have found that fat-water changes were consistent with osteoporosis. The role of routine MRI sequences can be extended in this regard. The S1 vertebra is considered a crucial anatomical site in spine surgeries because it seldom suffers from fractures. Thus, S1 could indicate osteoporotic fat-water changes. Forty-two female volunteers (aged 62.3±6.3 years) underwent spine examination with both MRI (including a phantom) and dual-energy X-ray absorptiometry (DXA) following ethical approval. MRI phantom-based F- and W-scoreS1 were defined by normalizing S1 vertebral signal intensities (SIs) by coconut oil and water SIs of the phantom on T1- and T2-weighted imaging, respectively. Using receiver operating characteristic analysis, the diagnostic performances of the new scores for evaluating osteoporosis and vertebral fractures were investigated against standard areal bone mineral density measured with DXA (DXA-aBMD). The F-scoreS1 and W-scoreS1 were greater (4.11 and 2.43, respectively) in patients with osteoporosis than those without osteoporosis (3.25 and 1.92, respectively) and achieved areas under the curve (AUCs) of 0.82 and 0.76 (p<0.05), respectively, for osteoporosis detection. Similarly, the mean F-scoreS1 and W-scoreS1 were higher (4.11 and 2.63, respectively) in patients with vertebral fractures than in those without fractures (3.30 and 1.82, respectively) and had greater AUCs (0.90 for W-scoreS1 and 0.74 for F-scoreS1) than DXA-aBMD (AUC, 0.26; p<0.03). In addition, the F- and W-scoreS1 demonstrated a strong correlation (r=0.65, p<0.001). The new S1 vertebral-based MRI scores were developed to detect osteoporotic changes and demonstrated improvements over DXA-aBMD in differentiating patients with vertebral fractures.

Developing Cut-off Values for Low and Very Low Bone Mineral Density at the Thoracic Spine Using Quantitative Computed Tomography

Osteoporosis is under-diagnosed while detectable by measuring bone mineral density (BMD) using quantitative computer tomography (QCT). Opportunistic screening for low BMD has previously been suggested using lumbar QCT. However, thoracic QCT also possesses this potential to develop upper and lower cut-off values for low thoracic BMD, corresponding to the current cut-offs for lumbar BMD. In participants referred with chest pain, lumbar and thoracic BMD were measured using non-contrast lumbar- and cardiac CT scans. Lumbar BMD cut-off values for very low (< 80 mg/cm3), low (80–120 mg/cm3), and normal BMD (> 120 mg/cm3) were used to assess the corresponding thoracic values. A linear regression enabled identification of new diagnostic thoracic BMD cut-off values. The 177 participants (mean age 61 [range 31–74] years, 51% women) had a lumbar BMD of 121.6 mg/cm3 (95% CI 115.9–127.3) and a thoracic BMD of 137.0 mg/cm3 (95% CI: 131.5–142.5), p < 0.001. Categorization of lumbar BMD revealed 14%, 35%, and 45% in each BMD category. When applied for the thoracic BMD measurements, 25% of participants were reclassified into a lower group. Linear regression predicted a relationship of Thoracic BMD = 0.85 * Lumbar BMD + 33.5, yielding adjusted thoracic cut-off values of < 102 and > 136 mg/cm3. Significant differences in BMD between lumbar and thoracic regions were found, but a linear relationship enabled the development of thoracic upper and lower cut-off values for low BMD in the thoracic spine. As Thoracic CT scans are frequent, these findings will strengthen the utilization of CT images for opportunistic detection of osteoporosis.

Exploring the Role of Hormones and Cytokines in Osteoporosis Development.

The disease of osteoporosis is characterized by impaired bone structure and an increased risk of fractures. There is a significant impact of cytokines and hormones on bone homeostasis and the diagnosis of osteoporosis. As defined by the World Health Organization (WHO), osteoporosis is defined as having a bone mineral density (BMD) that is 2.5 standard deviations (SD) or more below the average for young and healthy women (T score < -2.5 SD). Cytokines and hormones, particularly in the remodeling of bone between osteoclasts and osteoblasts, control the differentiation and activation of bone cells through cytokine networks and signaling pathways like the nuclear factor kappa-B ligand (RANKL)/the receptor of RANKL (RANK)/osteoprotegerin (OPG) axis, while estrogen, parathyroid hormones, testosterone, and calcitonin influence bone density and play significant roles in the treatment of osteoporosis. This review aims to examine the roles of cytokines and hormones in the pathophysiology of osteoporosis, evaluating current diagnostic methods, and highlighting new technologies that could help for early detection and treatment of osteoporosis.

Comparative analysis of vision transformers and convolutional neural networks in osteoporosis detection from X-ray images

Within the scope of this investigation, we carried out experiments to investigate the potential of the Vision Transformer (ViT) in the field of medical image analysis. The diagnosis of osteoporosis through inspection of X-ray radio-images is a substantial classification problem that we were able to address with the assistance of Vision Transformer models. In order to provide a basis for comparison, we conducted a parallel analysis in which we sought to solve the same problem by employing traditional convolutional neural networks (CNNs), which are well-known and commonly used techniques for the solution of image categorization issues. The findings of our research led us to conclude that ViT is capable of achieving superior outcomes compared to CNN. Furthermore, provided that methods have access to a sufficient quantity of training data, the probability increases that both methods arrive at more appropriate solutions to critical issues.

Magnetic resonance imaging phantom-based S1 vertebral scores are indicators of fat-water-like osteoporotic changes in postmenopausal women: a pilot study.

A prospective study. To assess fat-water-like tissue changes on the 1st sacral vertebra using novel magnetic resonance imaging (MRI) phantombased F- and W-scores and evaluate their diagnostic performances in osteoporosis detection. Using an uncommonly advanced MRI technique, previous studies have found that fat-water changes were consistent with osteoporosis. The role of routine MRI sequences can be extended in this regard. The S1 vertebra is considered a crucial anatomical site in spine surgeries because it seldom suffers from fractures. Thus, S1 could indicate osteoporotic fat-water changes. Forty-two female volunteers (aged 62.3±6.3 years) underwent spine examination with both MRI (including a phantom) and dual-energy X-ray absorptiometry (DXA) following ethical approval. MRI phantom-based F- and W-scoreS1 were defined by normalizing S1 vertebral signal intensities (SIs) by coconut oil and water SIs of the phantom on T1- and T2-weighted imaging, respectively. Using receiver operating characteristic analysis, the diagnostic performances of the new scores for evaluating osteoporosis and vertebral fractures were investigated against standard areal bone mineral density measured with DXA (DXA-aBMD). The F-scoreS1 and W-scoreS1 were greater (4.11 and 2.43, respectively) in patients with osteoporosis than those without osteoporosis (3.25 and 1.92, respectively) and achieved areas under the curve (AUCs) of 0.82 and 0.76 (p<0.05), respectively, for osteoporosis detection. Similarly, the mean F-scoreS1 and W-scoreS1 were higher (4.11 and 2.63, respectively) in patients with vertebral fractures than in those without fractures (3.30 and 1.82, respectively) and had greater AUCs (0.90 for W-scoreS1 and 0.74 for F-scoreS1) than DXA-aBMD (AUC, 0.26; p<0.03). In addition, the F- and W-scoreS1 demonstrated a strong correlation (r=0.65, p<0.001). The new S1 vertebral-based MRI scores were developed to detect osteoporotic changes and demonstrated improvements over DXA-aBMD in differentiating patients with vertebral fractures.