Screening and early diagnosis of osteoporosis through X-ray and ultrasound based techniques

Heritability of bone mineral density among Japanese women: A twin study

BACKGROUND

Osteoporosis is a major risk factor for fractures among the older population. Despite osteoporosis being a significant concern in super-aged societies such as Japan, the heritability of bone mineral density within the Japanese populace remains unexplored. Therefore, we conducted a twin study among Japanese women to determine the extent of genetic and environmental influences on bone mineral density.

METHODS

The bone area ratio of 298 healthy, female Japanese twins (comprising 149 pairs, 136 monozygotic and 13 dizygotic twin pairs) registered in the Osaka University Twin Registry was measured using quantitative ultrasound. Classical twin analysis was employed to ascertain the heritability of bone mineral density.

RESULTS

The heritability of bone mineral density for the entire cohort was 0.51 (95 % confidence interval 0.38-0.63). For the women younger than 50 years, the effects of menopause were adjusted, and the best-fit model was also found to be the additive genetics and unique environment (AE) model, with a heritability estimate of 0.53 (95 % confidence interval 0.34-0.72).

CONCLUSIONS

Compared with women from other countries, Japanese women appear to have a lower heritability of bone mineral density. Consequently, environmental factors may exert a larger influence on osteoporosis among Japanese women than among women of other races and ethnicities.

Development and evaluation of a machine learning model for osteoporosis risk prediction in Korean women

BACKGROUND

The aim of this study was to develop a machine learning (ML) model for classifying osteoporosis in Korean women based on a large-scale population cohort study. This study also aimed to assess ML model performance compared with traditional osteoporosis screening tools. Furthermore, this study aimed to examine the factors influencing the risk of osteoporosis through variable importance.

METHODS

Data was collected from 4199 women aged 40-69 years in the baseline survey of the Ansan and Ansung cohort of the Korean Genome and Epidemiology Study. Osteoporosis was set as the dependent variable to develop ML classification models. Independent variables included 122 factors related to osteoporosis risk, such as socio-demographic characteristics, anthropometric parameters, lifestyle factors, reproductive factors, nutrient intakes, diet quality indices, medical history, medication history, family history, biochemical parameters, and genetic factors. The six classification models were developed using ML techniques, including decision tree, random forest, multilayer perceptron, support vector machine, light gradient boosting machine, and extreme gradient boosting (XGBoost). The six ML classification models were compared with two traditional osteoporosis screening tools, including the osteoporosis risk assessment instrument (ORAI) and the osteoporosis self-assessment tool (OST). The ML model performances were evaluated and compared using the confusion matrix and area under the curve (AUC) metrics. Variable importance was assessed using the XGBoost technique to investigate osteoporosis risk factors.

RESULTS

The XGBoost model showed the highest performance out of the six ML classification models, with an accuracy of 0.705, precision of 0.664, recall of 0.830, and F1 score of 0.738. Moreover, the XGBoost model showed a higher performance on AUC than ORAI and OST. Variable importance scores were identified for 69 out of the 122 variables associated with osteoporosis risk factors. Age at menopause ranked first in variable importance. Variables of arthritis, physical activities, hypertension, education level, income level; alcohol intake, potassium intake, homeostatic model assessment for insulin resistance; energy intake, vitamin C intake, gout; and dietary inflammatory index ranked in the top 20 out of the 69 variables, using the XGBoost technique.

CONCLUSIONS

This study found that an XGBoost model can be utilized to classify osteoporosis in Korean women. Age at menopause is a significant factor in osteoporosis risk, followed by arthritis, physical activities, hypertension, and education level.

Osteoporosis Evaluation by Radiofrequency Echographic Multispectrometry (REMS) in Primary Healthcare

Background/Objectives: Radiofrequency echographic multispectrometry (REMS) technology has emerged as a promising alternative for osteoporosis diagnosis. This non-ionising, portable and accessible method enables early detection of osteoporosis in primary healthcare settings. The aim of this study was to assess the effectiveness of REMS in evaluating osteoporosis within primary healthcare. Methods: Bone mineral density was assessed in 86 participants trough 172 scans of the lumbar spine and femur, using REMS technology in two Portuguese primary healthcare units in Guarda. Results: In the lumbar spine evaluation, 51.2% of the participants had osteopenia and 31.4% osteoporosis; in the femur evaluation, 43.0% had osteopenia and 34.9% osteoporosis. The data indicated a significant prevalence of bone fragility. The bone mineral density estimated by radiofrequency echographic multispectrometry showed good agreement with the clinical diagnosis, suggesting that this technology is effective in the early detection of osteoporosis. Conclusions: Bone densitometry using REMS method, performed by a radiographer in primary healthcare settings, offers a viable and innovative alternative for the effective detection of osteoporosis and osteopenia.

The extended impact of the COVID-19 pandemic on medical imaging case volumes: a retrospective study

OBJECTIVE

This study aims to investigate the long-term effects of the COVID-19 pandemic on medical imaging case volumes.

METHODS

This retrospective study analyzed data from the Integrated Radiology Information System-Picture Archive and Communication System (RIS-PACS), including monthly medical imaging case volumes at a public hospital, spanning from January 2019 to December 2022. The study collected data on medical imaging examinations, comparing the pre COVID-19 period, which acted as a control group, with the periods following COVID-19, which were designated as cohort groups.

RESULTS

The total number of medical imaging procedures performed (n = 597,645) was found significantly different (F = 6.69, P < 0.001) between 2019 and 2022. Specifically, the bone mineral density/computed radiography (BMD/CR) modality experienced a significant decrease (P = 0.01) of the procedures performed in 2020 and 2021 compared to 2019. Conversely, the nuclear medicine/computed tomography (NM/CT) and computed tomography (CT) modalities demonstrated a significant increase of the procedures performed in 2021 (P = 0.04) and (P < 0.0001), respectively, and in 2022 (P = 0.0095) and (P < 0.0001), respectively, compared to the pre-pandemic year. The digital X-ray modality (DX) showed the highest volume (67.63%) of the performed procedures overall between 2019 and 2022. Meanwhile, magnetic resonance imaging (MR) and ultrasound (US) modalities experienced a slight drop in the number of procedures in 2020-4.47% for MR and 1.00% for US, which subsequently recovered by 22.15% and 19.74% in 2021, and 24.36% and 17.40% in 2022, respectively, compared to 2019.

CONCLUSION

The COVID-19 pandemic initially led to a drop in the number of medical imaging procedures performed in 2020, with the most noticeable drop occurring during the early waves of the pandemic. However, this trend revealed a gradual recovery in the subsequent years, 2021 and 2022, as healthcare systems adapted, and pandemic-related restrictions were modified.

Patterns and Correlates of Bone Mineral Density Parameters Measured Using Calcaneus Quantitative Ultrasound in Chinese Adults

Background: Monitoring bone mineral density (BMD) in adults is critical for early detection of osteoporosis and prevention of fracture, for which quantitative ultrasound (QUS) is a good non-invasive tool. We examined the associations of QUS measures, including stiffness index (SI) and T-score, with socio-demographic, lifestyle, and anthropometric correlates and risk of subsequent fracture. Methods: Calcaneal QUS was performed using GE's Lunar Achilles EXPII among 24,651 adults (mean age 59.5 years, 61.7% women) from the China Kadoorie Biobank study. Socio-demographic and lifestyle information was collected using an interviewer-administered electronic questionnaire, and anthropometrics were measured following standard protocols. Incidence of fracture and osteoporosis was recorded via linkage with nationwide health insurance database. Linear and Cox regression analyses were conducted, adjusting for potential confounders. Results: On average, men had higher SI (92.8 vs. 86.0) but lower T-score (-0.85 vs. -0.64) than women. In both men and women, advanced age and smoking were inversely associated with SI and T-score, while physical activity and tea drinking were positively so (p < 0.0001 for all). Except for height, all other anthropometric measures were significantly and positively associated with both BMD measures. With each SD lower SI, the risk of subsequent fracture was 26% (95% confidence interval: 10-44%) and 40% (25-57%) higher in men and women, and the corresponding associations of T-score were identical. Conclusions: Among Chinese adults, the SI and T-score provided by Achilles EXPII had similar patterns and predictive values for subsequent fracture, despite the T-score for men and women not being directly comparable because of gender-specific references used. Future studies are needed to confirm or refute the causality of relationship between lifestyle and anthropometric factors and BMD.

Optimizing autonomous artificial intelligence diagnostics for neuro-ocular health in space missions

Spaceflight-Associated Neuro-Ocular Syndrome (SANS) presents a critical risk in long-duration missions, with microgravity-induced changes that threaten astronaut vision and mission outcomes. Current SANS monitoring, limited to pre- and post-flight exams, lacks in-flight diagnostics, highlighting an urgent need for autonomous tools capable of real-time assessment. Grok, an AI platform by xAI, offers promising potential as an advanced diagnostic tool for space-based health monitoring. Originally developed for broader applications, Grok's high-resolution imaging capabilities could be adapted to detect early SANS indicators such as optic nerve edema and shifts in globe morphology, changes linked to fluid redistribution in space. However, realizing this vision requires algorithmic and hardware adjustments to address the unique physiological shifts astronauts experience. By advancing Grok's diagnostic capability, we strongly believe astronauts could manage SANS autonomously, bringing much-needed real-time, high-accuracy diagnostics to isolated, high-stakes environments-essential as humanity embarks on increasingly ambitious missions to Mars and beyond.

Parkinson's disease and posture: evaluation with biometrical holistic of human body device: a pilot study

Background

Patients with Parkinson's disease (PD) commonly develop severe spinal deformity. The etiology of Parkinson's spinal deformity is not completely understood and in most cases is likely due to multiple factors. These include dystonia and focal myopathy. Clinical, neurophysiological, and radiological data must be considered to monitor the pathology and the effects of rehabilitation. The aim of this pilot study is to evaluate spine alignment with a surface topography analysis of Parkinson's patients with Biometrical Holistic of Human Body (BHOHB®) and to compare results with their x-rays spine standard as already done for adolescent scoliosis.

Methods

32 adult patients affected by Parkinson disease, have been evaluated with BHOHB ®. The correspondence of the Cobb angles were evaluated using the BHOHB device and with standard spinal x-rays.

Results

A total of 32 patients were enrolled. The mean age was 67.45 years. In this pilot study the measurement of the correlations between the radiological and BHOHB® Cobb angles of the patients were excellent.

Conclusion

This preliminary result supports the use of BHOHB® as a device useful to monitor and measure posture in Parkinson's. This needs to be evaluated on a larger sample and over time. Keywords: Parkinson's disease, scoliosis, postural imbalance, neurophysiology.

Correlation of thyroid function and sensitivity to thyroid hormone with spinal bone mineral content, bone mineral density, and osteoporotic vertebral fracture: A cross-sectional study based on NHANES

Previous studies have demonstrated that thyroid hormone plays an important role in normal bone development, bone metabolism, and establishment of peak bone mass. However, the correlation of thyroid status with bone mineral content (BMC), bone mineral density (BMD), and osteoporotic vertebral fracture (OVF) is rarely discussed. The current study probes into the potential association between thyroid status and spinal BMC, BMD, and OVF from a novel perspective of thyroid function (TF) and sensitivity to thyroid hormone based on the National Health and Nutrition Examination Survey database. A total of 1844 participants were included in this study. The association of thyroid status with outcome variables, like spinal BMC, BMD, and OVF, was analyzed using thyroid function indices and sensitivity to thyroid hormone indices as influence factors. The correlation of them were assessed using univariate and multivariable weighted linear regression, weighted logistic regression models, restricted cubic spline model, and subgroup analyses. The results of this study showed that the association of free triiodothyronine (FT3)/free thyroxine (FT4) with BMC remained negatively associated after adjustment for all covariates. Higher thyroid peroxidase antibody (TPOAb) was significantly associated with an increased risk of developing OVF in both unadjusted and adjusted models. In addition, the results of the restricted cubic spline model were consistent with the weighted multivariate regression analysis after adjustment. The results of this cross-sectional study showed that higher FT3/FT4 and TPOAb were associated with decreased spinal BMC and the increased risk of OVF, indicating a complex link between thyroid status and bone health. Therefore, patients with hyperthyroidism, hypothyroidism, autoimmune thyroid disease, or abnormal peripheral thyroid sensitivity, especially who with elevated TPOAb or FT3/FT4, should focus on the prevention of vertebral osteopenia, osteoporosis, and OVF.

A new superfluity deep learning model for detecting knee osteoporosis and osteopenia in X-ray images

This study proposes a new deep-learning approach incorporating a superfluity mechanism to categorize knee X-ray images into osteoporosis, osteopenia, and normal classes. The superfluity mechanism suggests the use of two distinct types of blocks. The rationale is that, unlike a conventional serially stacked layer, the superfluity concept involves concatenating multiple layers, enabling features to flow into two branches rather than a single branch. Two knee datasets have been utilized for training, validating, and testing the proposed model. We use transfer learning with two pre-trained models, AlexNet and ResNet50, comparing the results with those of the proposed model. The results indicate that the performance of the pre-trained models, namely AlexNet and ResNet50, was inferior to that of the proposed Superfluity DL architecture. The Superfluity DL model demonstrated the highest accuracy (85.42% for dataset1 and 79.39% for dataset2) among all the pre-trained models.

Clinical Validation of a Deep Learning-Based Software for Lumbar Bone Mineral Density and T-Score Prediction from Chest X-ray Images

Screening for osteoporosis is crucial for early detection and prevention, yet it faces challenges due to the low accuracy of calcaneal quantitative ultrasound (QUS) and limited access to dual-energy X-ray absorptiometry (DXA) scans. Recent advances in AI offer a promising solution through opportunistic screening using existing medical images. This study aims to utilize deep learning techniques to develop a model that analyzes chest X-ray (CXR) images for osteoporosis screening. This study included the AI model development stage and the clinical validation stage. In the AI model development stage, the combined dataset of 5122 paired CXR images and DXA reports from the patients aged 20 to 98 years at a medical center was collected. The images were enhanced and filtered for hardware retention such as pedicle screws, bone cement, artificial intervertebral discs or severe deformity in target level of T12 and L1. The dataset was then separated into training, validating, and testing datasets for model training and performance validation. In the clinical validation stage, we collected 440 paired CXR images and DXA reports from both the TCVGH and Joy Clinic, including 304 pared data from TCVGH and 136 paired data from Joy Clinic. The pre-clinical test yielded an area under the curve (AUC) of 0.940, while the clinical validation showed an AUC of 0.946. Pearson's correlation coefficient was 0.88. The model demonstrated an overall accuracy, sensitivity, and specificity of 89.0%, 88.7%, and 89.4%, respectively. This study proposes an AI model for opportunistic osteoporosis screening through CXR, demonstrating good performance and suggesting its potential for broad adoption in preliminary screening among high-risk populations.

Application of radiomics model based on lumbar computed tomography in diagnosis of elderly osteoporosis

A metabolic bone disease characterized by decreased bone formation and increased bone resorption is osteoporosis. It can cause pain and fracture of patients. The elderly are prone to osteoporosis and are more vulnerable to osteoporosis. In this study, radiomics are extracted from computed tomography (CT) images to screen osteoporosis in the elderly. Collect the plain scan CT images of lumbar spine, cut the region of interest of the image and extract radiomics features, use Lasso regression to screen variables and adjust complexity, use python language to model random forests, support vector machines, K nearest neighbor, and finally use receiver operating characteristic curve to evaluate the performance of the model, including precision, recall, accuracy and area under the curve (AUC). For the model, 14 radiolomics features were selected. The diagnosis performance of random forest model and support vector machine is good, all around 0.9. The AUC of K nearest neighbor model in training set and test set is 0.828 and 0.796, respectively. We selected the plain scan CT images of the elderly lumbar spine to build radiomics features model, which has good diagnostic performance and can be used as a tool to assist the diagnosis of osteoporosis in the elderly.

Super-resolution techniques for biomedical applications and challenges

Super-resolution (SR) techniques have revolutionized the field of biomedical applications by detailing the structures at resolutions beyond the limits of imaging or measuring tools. These techniques have been applied in various biomedical applications, including microscopy, magnetic resonance imaging (MRI), computed tomography (CT), X-ray, electroencephalogram (EEG), ultrasound, etc. SR methods are categorized into two main types: traditional non-learning-based methods and modern learning-based approaches. In both applications, SR methodologies have been effectively utilized on biomedical images, enhancing the visualization of complex biological structures. Additionally, these methods have been employed on biomedical data, leading to improvements in computational precision and efficiency for biomedical simulations. The use of SR techniques has resulted in more detailed and accurate analyses in diagnostics and research, essential for early disease detection and treatment planning. However, challenges such as computational demands, data interpretation complexities, and the lack of unified high-quality data persist. The article emphasizes these issues, underscoring the need for ongoing development in SR technologies to further improve biomedical research and patient care outcomes.

Prospective Association Between the Dispensing of Antidepressants and of Medications to Treat Osteoporosis in Older Age

BACKGROUND

Osteoporosis is a common condition associated with fragility fractures, especially in older individuals and women. Antidepressants have emerged as a potential risk factor, but their association with bone fragility remains uncertain because the results of past studies are difficult to generalize. We aimed to investigate the association between antidepressant exposure and subsequent treatment for osteoporosis in a nationally representative sample of Australians.

METHODS

Cohort study using a 10% random sample of the Pharmaceutical Benefits Scheme (PBS) data for 2012, that included 566,707 individuals aged older than or equal to 50 years not dispensed osteoporosis medications. The effect of exposure to antidepressants during 2012 (prevalent or incident) or later (up to 2022) was examined using Cox regression models adjusted for age, sex, comorbidities and other psychotropic medications.

RESULTS

Over 10 years, 73,360 (12.94%) received osteoporosis medications; 16,216 (22.10%) had been dispensed antidepressants in 2012. The hazard of osteoporosis medication dispensing was higher among those exposed to antidepressants (HR = 1.16, 99% CI = 1.14-1.18; average duration of follow up: 8.0 ± 3.1 years, range: 1-10 years). The hazard of osteoporosis medication diminished with increasing age, and the effect of antidepressants was 37%-76% more pronounced among men in the 50s and 60s. Different classes of antidepressants had a similar risk profile.

CONCLUSION

The dispensing of antidepressants in older age is associated with higher hazard of subsequent dispensing of medications for osteoporosis, and this association is more marked for young older adults, particularly men. Clinicians should monitor the bone health of older individuals treated with antidepressants in order to decrease the morbidity associated with fragility fractures.

Theoretical and experimental study of attenuation in cancellous bone

Significance

Photoacoustic (PA) technology shows great potential for bone assessment. However, the PA signals in cancellous bone are complex due to its complex composition and porous structure, making such signals challenging to apply directly in bone analysis.

Aim

We introduce a photoacoustic differential attenuation spectrum (PA-DAS) method to separate the contribution of the acoustic propagation path to the PA signal from that of the source, and theoretically and experimentally investigate the propagation attenuation characteristics of cancellous bone.

Approach

We modified Biot's theory by accounting for the high frequency and viscosity. In parallel with the rabbit osteoporosis model, we build an experimental PA-DAS system featuring an eccentric excitation differential detection mechanism. Moreover, we extract a PA-DAS quantization parameter-slope-to quantify the attenuation of high- and low-frequency components.

Results

The results show that the porosity of cancellous bone can be evaluated by fast longitude wave attenuation at different frequencies and the PA-DAS slope of the osteoporotic group is significantly lower compared with the normal group (**p < 0.01 ).

Conclusions

Findings demonstrate that PA-DAS effectively differentiates osteoporotic bone from healthy bone, facilitating quantitative assessment of bone mineral density, and osteoporosis diagnosis.

Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography

Osteoporosis is a prevalent bone disease that causes fractures in fragile bones, leading to a decline in daily living activities. Dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) are highly accurate for diagnosing osteoporosis; however, these modalities require special equipment and scan protocols. To frequently monitor bone health, low-cost, low-dose, and ubiquitously available diagnostic methods are highly anticipated. In this study, we aim to perform bone mineral density (BMD) estimation from a plain X-ray image for opportunistic screening, which is potentially useful for early diagnosis. Existing methods have used multi-stage approaches consisting of extraction of the region of interest and simple regression to estimate BMD, which require a large amount of training data. Therefore, we propose an efficient method that learns decomposition into projections of bone-segmented QCT for BMD estimation under limited datasets. The proposed method achieved high accuracy in BMD estimation, where Pearson correlation coefficients of 0.880 and 0.920 were observed for DXA-measured BMD and QCT-measured BMD estimation tasks, respectively, and the root mean square of the coefficient of variation values were 3.27 to 3.79% for four measurements with different poses. Furthermore, we conducted extensive validation experiments, including multi-pose, uncalibrated-CT, and compression experiments toward actual application in routine clinical practice.

Study of Ultrasonic Guided Wave Propagation in Bone Composite Structures for Revealing Osteoporosis Diagnostic Indicators

Tubular bones are layered waveguide structures composed of soft tissue, cortical and porous bone tissue, and bone marrow. Ultrasound diagnostics of such biocomposites are based on the guided wave excitation and registration by piezoelectric transducers applied to the waveguide surface. Meanwhile, the upper sublayers shield the diseased interior, creating difficulties in extracting information about its weakening from the surface signals. To overcome these difficulties, we exploit the advantages of the Green's matrix-based approach and adopt the methods and algorithms developed for the guided wave structural health monitoring of industrial composites. Based on the computer models implementing this approach and experimental measurements performed on bone phantoms, we analyze the feasibility of using different wave characteristics to detect hidden diagnostic signs of developing osteoporosis. It is shown that, despite the poor excitability of the most useful modes associated with the diseased inner layers, the use of the improved matrix pencil method combined with objective functions based on the Green's matrix allows for effective monitoring of changes in the elastic moduli of the deeper sublayers. We also note the sensitivity and monotonic dependence of the resonance response frequencies on the degradation of elastic properties, making them a promising indicator for osteoporosis diagnostics.

MicroRNAs as potential biopredictors for premenopausal osteoporosis: a biochemical and molecular study

BACKGROUND

Circulating micro-RNAs have been proposed as a new type of biomarker in several diseases, particularly those related to bone health. They have shown great potential due to their feasibility and simplicity of measurement in all body fluids, especially urine, plasma, and serum.

AIM

This study aimed to evaluate the expression of a set of mRNAs, namely miR-21, miR-24, mir-100, miR-24a, miR-103-3p, and miR-142-3p. Their proposed roles in the progression of osteoporosis were identified using a real-time polymerase chain reaction (RT-PCR) analysis in premenopausal women. In addition, their correlations with osteocalcin (OC), bone-specific alkaline phosphatase (BAP), and deoxypyridinoline (DPD) bone markers were explored.

METHODS

A total of 85 healthy premenopausal women aged 25-50 years old were included in this study. Based on a DXA scan (Z-score) analysis and calcaneus broadband ultrasound attenuation scores (c-BUAs), measured via quantitative ultrasound (QUS), the subjects were classified into three groups: normal group (n = 25), osteopenia (n = 30), and osteoporosis (n = 30). Real-time-PCR and immunoassay analyses were performed to determine miRNA expression levels and serum OC, s-BAP, and DPD, respectively, as biomarkers of bone health.

RESULTS

Among the identified miRNAs, only miR-21, miR-24, and mir-100 were significantly upregulated and increased in the serum of patients with osteopenia and osteoporosis, and miR-24a, miR-103-3p, and miR-142-3p were downregulated and significantly decreased in osteoporosis. Both upregulated and downregulated miRNAs were significantly correlated with BMD, c-BUA, OC, s-BAP, and DPD.

CONCLUSION

A group of circulating miRNAs was shown to be closely correlated with the parameters BMD, c-BUA, OC, s-BAP, and DPD, which are traditionally used for bone-health measurements. They could be identified as non-invasive biomarkers in premenopausal patients with osteoporosis. More studies with large sample sizes are recommended to estimate the mechanistic role of miRNAs in osteoporosis pathogenesis and to provide evidence for the use of these miRNAs as a non-invasive method of diagnosing clinical osteoporosis, especially in premenopausal patients.

Effect of Lifestyle Modification Intervention Programme on Bone Mineral Density among Postmenopausal Women with Osteoporosis

Objectives

Osteoporosis is one of the major public health problems worldwide among postmenopausal osteoporotic women. Lifestyle modification interventions along with pharmacotherapy help to revert bone loss and prevent complications.

Methods

A randomised controlled trial was conducted at Kasturba Hospital, Manipal from January 2019 to December 2021 among postmenopausal women with osteoporosis. The postmenopausal women who attended the osteoporosis clinic and were within the age group of 45-65 years, could speak and understand English or Kannada and whose bone mineral density (BMD) score was between -1 and -3 were included in the study. The total sample size of the study was 120 with 60 in each of the experimental and control groups. After obtaining informed consent, a stratified block randomisation method was used to allocate the participants to intervention and control groups. The BMD was monitored by the portable ultrasound densitometer by a technician at the outpatient departments. The baseline information was collected by a structured demographic questionnaire. Intervention group participants received a lifestyle modification intervention program (LMIP) whereas the control group received standard regular care from the physician. Follow-up was done at three and six months.

Results

The results revealed that the increase in the BMD median score among the experimental group was from -2.2 (-2.5- -1.8) to -1.5 (-1.8- -0.65) whereas in the control group, it was from -2.3 (-2.6- -1.9) to -2.0 (-2.4- -1.5). The results of the Mann Whitey U test showed a statistical significance between the intervention and control groups in the post-test after six months (U = 505.5; P <0.05). Wilcoxon signed rank test showed a significant change in both the intervention and control groups from pre-test to post-test I (3 months) and post-test II (6 months; P <0.001).

Conclusion

The lifestyle modification intervention was found to be effective in improving the bone health status of postmenopausal women. Hence it is very important to integrate it into regular therapy.

To segment or not to segment: COVID-19 detection for chest X-rays

Artificial intelligence (AI) has been integrated into most technologies we use. One of the most promising applications in AI is medical imaging. Research demonstrates that AI has improved the performance of most medical imaging analysis systems. Consequently, AI has become a fundamental element of the state of the art with improved outcomes across a variety of medical imaging applications. Moreover, it is believed that computer vision (CV) algorithms are highly effective for image analysis. Recent advances in CV facilitate the recognition of patterns in medical images. In this manner, we investigate CV segmentation techniques for COVID-19 analysis. We use different segmentation techniques, such as k-means, U-net, and flood fill, to extract the lung region from CXRs. Afterwards, we compare the effectiveness of these three segmentation approaches when applied to CXRs. Then, we use machine learning (ML) and deep learning (DL) models to identify COVID-19 lesion molecules in both healthy and pathological lung x-rays. We evaluate our ML and DL findings in the context of CV techniques. Our results indicate that the segmentation-related CV techniques do not exhibit comparable performance to DL and ML techniques. The most optimal AI algorithm yields an accuracy range of 0.92-0.94, whereas the addition of CV algorithms leads to a reduction in accuracy to approximately the range of 0.81-0.88. In addition, we test the performance of DL models under real-world noise, such as salt and pepper noise, which negatively impacts the overall performance.

Can Ultrasonographic Measurement of Bone Cortical Thickness Predict Osteoporosis?

RATIONALE AND OBJECTIVES

In this study, we aimed to describe a more accessible and safe diagnostic tool for osteoporosis or osteopenia diagnosis. We utilized cortical thickness (CoT) measurement of various bones via ultrasonography and evaluated the method's accuracy relative to bone mineral density (BMD) results determined by dual-energy X-ray absorptiometry (DXA).

MATERIALS AND METHODS

A total of 200 volunteers (all female) who agreed to participate in the study and had undergone BMD measurement (femoral or vertebral) were included in the study. Patients with normal BMD result (≥-1.0 T-score) were defined as controls. CoT measurements were made from three bones (radius, tibia, and second metatarsal) for each patient via ultrasonography.

RESULTS

Radius CoT and tibial CoT measurements of both femoral and vertebral osteoporotic or osteopenic patients were significantly higher compared to controls. Second metatarsal CoT of femoral osteoporotic and osteopenic patients was also found to be significantly higher than the control group. We found tibia CoT and radius CoT to have high sensitivity and positive predictive value in identifying patients with abnormal femoral T-scores (<-1). In multivariable analyzes, radius CoT was found to be independently predictive in distinguishing patients with abnormal T-score (<-1) from controls in both the femur and vertebral BMD groups.

CONCLUSION

Radius CoT and tibia CoT values appear to have value in predicting patients with abnormal T-scores measured via DXA. This method may be a very simple technique that can be used for early detection of osteoporosis and osteopenia, but its results need to be supported by more comprehensive studies.

Evaluation of femoral head bone quality by Hounsfield units: A predictor of implant failure for intertrochanteric fractures after intramedullary nail fixation

Purpose

The aim of present study is to evaluate the femoral head bone quality by Hounsfield units and its relationship to the occurrence of implant failure for intertrochanteric fractures after intramedullary nail fixation.

Methods

This retrospective study assessed 160 intertrochanteric fractures treated with intramedullary fixation. Patients with and without implant failure were divided into failure and control groups, respectively. The demographic information, femoral head Hounsfield unit (HU) value, the reduction quality, status of posteromedial support and position of the screw/blade were collected and compared. The logistic regression analyses were performed to evaluate risk factors of implant failure in intertrochanteric fractures after intramedullary nail fixation.

Results

Of the patients, 15 (9.38%) suffered from implant failure after intramedullary fixation. The mean HU value of femoral head was much lower in the failure group than the control group (133.25 ± 34.10 vs. 166.12 ± 42.68, p = 0.004). And the univariate analyses showed that A3 fracture and poor reduction quality were associated with implant failure (p < 0.05). After adjustment for confounding variables, the multivariable logistic regression analyzes showed that femoral head HU value (odds ratio [OR], 0.972; 95% CI, 0.952-0.993; p = 0.008) and poor reduction quality (OR, 7.614; 95% CI, 1.390-41.717; p = 0.019) were independent influencing factors for implant failure.

Conclusion

The femoral head HU value was significantly correlated with the incidence of implant failure and can be used as an independent factor to predict implant failure for intertrochanteric fractures after intramedullary fixation.

Lumbar Bone Mineral Density Estimation From Chest X-Ray Images: Anatomy-Aware Attentive Multi-ROI Modeling

Osteoporosis is a common chronic metabolic bone disease often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e.g., via Dual-energy X-ray Absorptiometry (DXA). This paper proposes a method to predict BMD from Chest X-ray (CXR), one of the most commonly accessible and low-cost medical imaging examinations. The proposed method first automatically detects Regions of Interest (ROIs) of local CXR bone structures. Then a multi-ROI deep model with transformer encoder is developed to exploit both local and global information in the chest X-ray image for accurate BMD estimation. The proposed method is evaluated on 13719 CXR patient cases with ground truth BMD measured by the gold standard DXA. The model predicted BMD has a strong correlation with the ground truth (Pearson correlation coefficient 0.894 on lumbar 1). When applied in osteoporosis screening, it achieves a high classification performance (average AUC of 0.968). As the first effort of using CXR scans to predict the BMD, the proposed algorithm holds strong potential to promote early osteoporosis screening and public health.

A 2-Year Randomized Controlled Trial With Low-Dose B-Vitamin Supplementation Shows Benefits on Bone Mineral Density in Adults With Lower B12 Status

Folate, vitamins B12, B6, and riboflavin are required for one-carbon metabolism and may affect bone health, but no previous randomized trial has investigated all four nutrients in this context. We investigated the effect of low-dose B-vitamins for 2 years on bone mineral density (BMD) in a dual-centered, 2-year randomized controlled trial (RCT) in adults aged ≥50 years. Eligible participants not consuming B-vitamin supplements or fortified foods >4 times weekly were randomized to receive daily either combined folic acid (200 μg), vitamin B12 (10 μg), vitamin B6 (10 mg), and riboflavin (5 mg), or "active" placebo, whereby both the intervention and placebo groups received vitamin D (10 μg). BMD was assessed before and after intervention using dual-energy X-ray absorptiometry (DXA) scanning of the total hip, femoral neck, and lumbar spine (L1 to L4). Of 205 eligible participants randomized, 167 completed the trial in full. B-vitamin intervention resulted in increases in serum folate (p < 0.001), serum B12 (p < 0.001), and plasma pyridoxal-5-phosphate (p < 0.001) and decreases in functional biomarkers of B-vitamin status, erythrocyte glutathione reductase activation coefficient (p < 0.001), serum methylmalonic acid (MMA; p < 0.001), and serum total homocysteine (p < 0.001). B-vitamin intervention had no overall effect on BMD, which declined in both treatment groups by approximately 1% (ranging from -0.7% to -1.4%). However, in participants with lower baseline B12 status (serum B12 <246 pmol/L or MMA ≥0.22 μmol/L), B-vitamin intervention reduced the 2-year BMD decline versus placebo: adjusted mean (95% confidence interval [CI]) change of -0.003 (-0.008, 0.002) versus -0.015 (-0.021, -0.010) g/cm² at the total hip and -0.004 (-0.010, 0.001) versus -0.013 (-0.018, -0.007) g/cm² at the femoral neck. In conclusion, the findings indicate that although low-dose B-vitamin intervention for 2 years had no overall effect on BMD, improving B-vitamin status appears to have specific benefits for bone health in adults with lower B12 status. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Optical bone densitometry insensitive to skin thickness

Skin thickness, including the adipose layer, which varies from individual to individual, affects the bone density measurement using light. In this study, we proposed a method to measure skin thickness using light and to correct the bias caused by differences in skin thickness and verified the proposed method by experiments using a phantom. We measured simulated skin of different thicknesses and bovine trabecular bone of different bone mineral densities (BMDs) using an optical system consisting of lasers of 850 and 515 nm wavelengths, lenses, and slits. Although the slope of the light intensity distribution formed on the surface of the material when irradiated by the 850 nm laser is affected by the thickness of the skin phantom. The difference of the intensity distribution peaks (δy) between the 850 and 515 nm lasers was strongly correlated with the thickness of the skin phantom. The coefficient of determination between the measurements and the BMD was improved by correcting the 850 nm laser measurements with δy. This result suggests that the method is applicable to optical bone densitometry, which is insensitive to differences in skin thickness.

Current Status of the Diagnosis and Management of Osteoporosis

Osteoporosis has been defined as the silent disease of the 21st century, becoming a public health risk due to its severity, chronicity and progression and affecting mainly postmenopausal women and older adults. Osteoporosis is characterized by an imbalance between bone resorption and bone production. It is diagnosed through different methods such as bone densitometry and dual X-rays. The treatment of this pathology focuses on different aspects. On the one hand, pharmacological treatments are characterized by the use of anti-resorptive drugs, as well as emerging regenerative medicine treatments such as cell therapies and the use of bioactive hydrogels. On the other hand, non-pharmacological treatments are associated with lifestyle habits that should be incorporated, such as physical activity, diet and the cessation of harmful habits such as a high consumption of alcohol or smoking. This review seeks to provide an overview of the theoretical basis in relation to bone biology, the existing methods for diagnosis and the treatments of osteoporosis, including the development of new strategies.

Photoacoustic characterization of bone physico-chemical information

Osteoporosis usually alters the chemical composition and physical microstructure of bone. Currently, most clinical techniques for bone assessment are focused on the either bone microstructure or bone mineral density (BMD). In this study, a novel multi-wavelength photoacoustic time-frequency spectral analysis (MWPA-TFSA) method was introduced based on the optical absorption spectra and photoacoustic effects of biological macromolecules, which evaluates changes in bone chemical composition and microstructure. The results demonstrated that osteoporotic bones had decreased BMD, more lipids, and wider trabecular separation filled with larger marrow clusters, which were consistent with multiple gold-standard results, suggesting that the MWPA-TFSA method has the potential to provide a thorough bone physico-chemical information evaluation noninvasively and nonradiatively.

Assessment of Lumbar Vertebrae Morphology by Computed Tomography in Older Adults with Osteoporosis

BACKGROUND

Hounsfield units (HU) values derived from computerized tomography (CT) have been used in diagnosis for osteoporosis in the lumbar spine.

OBJECTIVE

This study aimed to identify anatomical dimensions of lumbar vertebrae on CT images, which were different between older normal, osteopenic and osteoporotic subjects.

METHODS

This prospective pilot study enrolled 79 older adults. Based on CT measurements of lumbar vertebrae in HU, participants were classified into three groups: normal (HU > 109), osteopenia (HU: 94-108), and osteoporosis (HU < 93). Altogether, 42 anatomical variables of lumbar vertebrae, L2, L3, L4, and L5, were measured in each participant by CT, including 24 parameters measurable by MRI or plain X-ray, and 18 parameters measurable by MRI only.

RESULTS

Among the morphological measurements also measurable by MRI and plain X-ray, the length upper curve, 50% and 75% of L5, length upper with cortex of L4, length center of cortex of L3, as well as width upper curve 75% of L2, were significantly different between the three groups (p= 0.008, 0.007, 0.035, 0.036, and 0.003 respectively). Among the morphological measurements also measurable by MRI, only width upper cortex 75% of L5 and width lower cortex 25% of L3 were significantly different between the three groups (p= 0.031 and 0.020, respectively).

CONCLUSION

Seven CT morphological measurements may be used as "reference standard" CT measurements for preliminarily diagnosing osteoporosis and osteopenia in older adults.

Regular Exercise and Weight-Control Behavior Are Protective Factors against Osteoporosis for General Population: A Propensity Score-Matched Analysis from Taiwan Biobank Participants

The rising prevalence of osteoporosis, which can lead to osteoporotic fractures, increases morbidity, mortality, and socioeconomic burden. Multiple factors influencing bone mass have already been identified. The aim of this study was to investigate whether exercise habits and weight-control behaviors can lower the incidence of osteoporosis in the general population. This retrospective study recruited all participants aged 35–70 years who underwent dual-energy X-ray absorptiometry (DXA) from Taiwan Biobank (TWB). The final analysis consisted of 3320 eligible participants divided into two groups; demographic characteristics, prevalence of clinical symptoms, comorbidities, and daily behavior were collected using a self-reported questionnaire. After propensity score matching with a 1:1 ratio, 1107 out of 2214 individuals were classified into the osteoporosis group. Age, body fat rate, body shape, diabetes mellitus, and social status were found to affect the incidence of osteoporosis. Subjects with a habit of regular exercise and weight-control behavior showed decreased odds of osteoporosis. (odds ratio: 0.709 and 0.753, 95% confidence interval: 0.599–0.839 and 0.636–0.890). In the general population, regular exercise or weight-control behavior lowers the incidence of osteoporosis.

Does exercise affect bone mineral density and content when added to a calorie-restricted diet? A systematic review and meta-analysis of controlled clinical trials

The effects of exercise in conjunction with weight-loss diets on bone health are mixed. Our objective was to systematically review and meta-analyze controlled clinical trials in adults investigating the addition of exercise to a weight-loss diet compared with a calorie-matched weight-loss diet without exercise on bone measures. Online databases including PubMed/MEDLINE, EMBASE, ISI (Web of Science), Scopus, and Google Scholar were searched up to April 2021 with no restriction. A random effects model was used to calculate the overall estimates. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. Fourteen eligible controlled clinical trials were included in the systematic review. The meta-analysis revealed that, compared to weight-loss diets alone, the addition of exercise did not improve total body bone mineral density (BMD) [weighted mean difference (WMD) = 0.002 g/cm2, P = 0.62, n = 8], lumbar BMD (WMD = 0.007 g/cm2, P = 0.44, n = 9), total hip BMD (WMD = 0.015 g/cm2, P = 0.14, n = 4) and total bone mineral content (BMC) (WMD =  - 11.97 g, P = 0.29, n = 7). Subgroup analysis revealed that resistance exercise in conjunction with hypocaloric diets positively affects total BMD compared to an energy restrictive diet alone (WMD = 0.01 g/cm2, P = 0.003, n = 3). Overall, it appears that only resistance exercise beneficially affects total BMD during a calorie-restricted diet in adults. Further well-controlled and long-term clinical trials are still needed to confirm these results. PROSPERO registration number: CRD42020173434.

A Risk Assessment Tool for Identifying Osteoporosis in Older Women with Type 2 Diabetes Mellitus

PURPOSE

To develop a simple and clinically useful assessment tool for osteoporosis in older women with type 2 diabetes mellitus (T2DM).

METHODS

A total of 601 women over 60 years of age with T2DM were enrolled in this study. The levels of serum sex hormones and bone metabolism markers were compared between the osteoporosis and non-osteoporosis groups. The least absolute shrinkage and selection operator regularization (LASSO) model was applied to generate a risk assessment tool. The risk score formula was evaluated using receiver operating characteristic analysis and the relationship between the risk score and the bone mineral density (BMD) and T-value were investigated.

RESULTS

Serum sex hormone-binding globulin (SHBG), cross-linked C-telopeptide of type 1 collagen (CTX), and osteocalcin (OC) were significantly higher in the osteoporosis group. After adjustment for age and body mass index (BMI), SHBG was found to be correlated with the T-value or BMD. Then, a risk score was specifically generated with age, BMI, SHBG, and CTX using the LASSO model. The risk score was significantly negatively correlated with the T-value and BMD of the lumbar spine, femoral neck, and total hip (all P<0.05).

CONCLUSION

A risk score using age, BMI, SHBG, and CTX performs well for identifying osteoporosis in older women with T2DM.

Ultrasonic Bone Assessment: Ability of Apparent Backscatter Techniques to Detect Changes in the Microstructure of Human Cancellous Bone

Ultrasonic backscatter techniques may offer a useful approach for detecting changes in bone caused by osteoporosis. The goal of this study was to investigate how bone mineral density (BMD) and the microstructure of human cancellous bone affect three ultrasonic backscatter parameters that have been identified as potentially useful for ultrasonic bone assessment purposes: the apparent integrated backscatter (AIB), the frequency slope of apparent backscatter (FSAB), and the frequency intercept of apparent backscatter (FIAB). Ultrasonic measurements were performed with a 3.5-MHz broadband transducer on 54 specimens of human cancellous bone prepared from the proximal femur. Microstructural parameters and BMD were measured using X-ray microcomputed tomography (micro-CT). Relationships between AIB, FSAB, FIAB, and the micro-CT parameters were investigated using univariate and multivariate statistical analysis techniques. Moderate-to-strong univariate correlations were observed between the backscatter parameters and microstructure and BMD in many cases. The partial correlation analysis indicated that the backscatter parameters are dependent on microstructure independently of BMD in some cases. Multiple stepwise linear regression analysis used to generate multivariate models found that microstructure was a significant predictor of the backscatter parameters in most cases.

Quantitative ultrasound assessment of the effect of parity on bone mineral density in females

Background

The effect of pregnancy and breastfeeding on a female’s bone mineral density (BMD) is controversial. This prospective study aims to investigate the effect of parity on BMD among pre-menopausal multiparous females using quantitative ultrasound as a screening method and females with no pregnancies (nulliparous) as a control group.

Methods

A portable ultrasound-based bone densitometer (DMS PEGASUS SMART, Mauguio, France) was used to indirectly assess the BMD in 51 multiparous (29–45 years) and 51 nulliparous Arabic females (18–35 years) by quantifying the broadband ultrasound attenuation (BUA) from their right calcaneus bone. BUA > 70 db/mhz = normal, BUA 65–69.9 db/mhz = below average, BUA 55–64.9 db/mhz = osteopenia and BUA < 55 db/mhz = osteoporosis.

Results

There was a significant difference in mean BUA between multiparous and nulliparous females (74.1 db/mhz vs. 69.3 db/mhz, p = 0.006). The prevalence of normal BMD was significantly higher in the nulliparous group than in the multiparous group (70.6% vs. 47.1%, p = 0.02). Osteoporosis was found in the multiparous group only (3/51). Among the multiparous females who breastfed (43/51), a total of 51.2% (22/43) had normal BMD, 25.6% (11/43) had BMD below average, 18.6% (8/43) had osteopenia and 4.7% (2/43) had osteoporosis. No significant differences in mean BUA (p = 0.2) were found between the group of females who breastfed for one year (13/43; BUA: 70.5 ± 9.4), the group of females who breastfed for 6–11 months (8/43; BUA: 70.6 ± 10.0) and those who breastfed for less than six months (22/43; BUA: 71.6 ± 9.4). A binary logistic regression model built for predicting BMD normality showed significance for the variable parity (p = 0.03), while the effect of the possible confounding variables BMI and age on BMD normality was found to be non- significant (p = 0.1 and p = 0.6, respectively).

Conclusion

Parity affects the BMD, as assessed by a portable ultrasound-based bone densitometer, of young and middle-aged females as compared to the BMD of nulliparous females.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12905-021-01516-w.

Prediction of osteoporosis from simple hip radiography using deep learning algorithm

Despite being the gold standard for diagnosis of osteoporosis, dual-energy X-ray absorptiometry (DXA) could not be widely used as a screening tool for osteoporosis. This study aimed to predict osteoporosis via simple hip radiography using deep learning algorithm. A total of 1001 datasets of proximal femur DXA with matched same-side cropped simple hip bone radiographic images of female patients aged ≥ 55 years were collected. Of these, 504 patients had osteoporosis (T-score ≤ − 2.5), and 497 patients did not have osteoporosis. The 1001 images were randomly divided into three sets: 800 images for the training, 100 images for the validation, and 101 images for the test. Based on VGG16 equipped with nonlocal neural network, we developed a deep neural network (DNN) model. We calculated the confusion matrix and evaluated the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We drew the receiver operating characteristic (ROC) curve. A gradient-based class activation map (Grad-CAM) overlapping the original image was also used to visualize the model performance. Additionally, we performed external validation using 117 datasets. Our final DNN model showed an overall accuracy of 81.2%, sensitivity of 91.1%, and specificity of 68.9%. The PPV was 78.5%, and the NPV was 86.1%. The area under the ROC curve value was 0.867, indicating a reasonable performance for screening osteoporosis by simple hip radiography. The external validation set confirmed a model performance with an overall accuracy of 71.8% and an AUC value of 0.700. All Grad-CAM results from both internal and external validation sets appropriately matched the proximal femur cortex and trabecular patterns of the radiographs. The DNN model could be considered as one of the useful screening tools for easy prediction of osteoporosis in the real-world clinical setting.

Racial and Ethnic Disparities in Bone Health and Outcomes in the United States

Osteoporosis is a bone disease classified by deterioration of bone microarchitecture and decreased bone strength, thereby increasing subsequent risk of fracture. In the United States, approximately 54 million adults aged 50 years and older have osteoporosis or are at risk due to low bone mass. Osteoporosis has long been viewed as a chronic health condition affecting primarily non-Hispanic white (NHW) women; however, emerging evidence indicates racial and ethnic disparities in bone outcomes and osteoporosis management. The primary objective of this review is to describe disparities in bone mineral density (BMD), prevalence of osteoporosis and fracture, as well as in screening and treatment of osteoporosis among non-Hispanic black (NHB), Hispanic, and Asian adults compared with NHW adults living on the US mainland. The following areas were reviewed: BMD, osteoporosis prevalence, fracture prevalence and incidence, postfracture outcomes, DXA screening, and osteoporosis treatments. Although there are limited studies on bone and fracture outcomes within Asian and Hispanic populations, findings suggest that there are differences in bone outcomes across NHW, NHB, Asian, and Hispanic populations. Further, NHB, Asian, and Hispanic populations may experience suboptimal osteoporosis management and postfracture care, although additional population-based studies are needed. There is also evidence that variation in BMD and osteoporosis exists within major racial and ethnic groups, highlighting the need for research in individual groups by origin or background. Although there is a clear need to prioritize future quantitative and qualitative research in these populations, initial strategies for addressing bone health disparities are discussed. © 2021 American Society for Bone and Mineral Research (ASBMR).

Optoacoustic imaging in endocrinology and metabolism

Imaging is an essential tool in research, diagnostics and the management of endocrine disorders. Ultrasonography, nuclear medicine techniques, MRI, CT and optical methods are already used for applications in endocrinology. Optoacoustic imaging, also termed photoacoustic imaging, is emerging as a method for visualizing endocrine physiology and disease at different scales of detail: microscopic, mesoscopic and macroscopic. Optoacoustic contrast arises from endogenous light absorbers, such as oxygenated and deoxygenated haemoglobin, lipids and water, or exogenous contrast agents, and reveals tissue vasculature, perfusion, oxygenation, metabolic activity and inflammation. The development of high-performance optoacoustic scanners for use in humans has given rise to a variety of clinical investigations, which complement the use of the technology in preclinical research. Here, we review key progress with optoacoustic imaging technology as it relates to applications in endocrinology; for example, to visualize thyroid morphology and function, and the microvasculature in diabetes mellitus or adipose tissue metabolism, with particular focus on multispectral optoacoustic tomography and raster-scan optoacoustic mesoscopy. We explain the merits of optoacoustic microscopy and focus on mid-infrared optoacoustic microscopy, which enables label-free imaging of metabolites in cells and tissues. We showcase current optoacoustic applications within endocrinology and discuss the potential of these technologies to advance research and clinical practice.

Wavelet transform-based photoacoustic time-frequency spectral analysis for bone assessment

In this study, we investigated the feasibility of using photoacoustic time-frequency spectral analysis (PA-TFSA) for evaluating the bone mineral density (BMD) and bone structure. Simulations and ex vivo experiments on bone samples with different BMDs and mean trabecular thickness (MTT) were conducted. All photoacoustic signals were processed using the wavelet transform-based PA-TFSA. The power-weighted mean frequency (PWMF) was evaluated to obtain the main frequency component at different times. The y-intercept, midband-fit, and slope of the linearly fitted curve of the PWMF over time were also quantified. The results show that the osteoporotic bone samples with lower BMD and thinner MTT have higher frequency components and lower acoustic frequency attenuation over time, thus higher y-intercept, midband-fit, and slope. The midband-fit and slope were found to be sensitive to the BMD; therefore, both parameters could be used to distinguish between osteoporotic and normal bones (p < 0.05).

Positive effects of low LDL-C and statins on bone mineral density: an integrated epidemiological observation analysis and Mendelian randomization study

BACKGROUND

Low-density lipoprotein cholesterol (LDL-C) is suggested to play a role in osteoporosis but its association with bone metabolism remains unclear. Effects of LDL-C-lowering drugs on bone are also controversial. We aim to determine whether LDL-C is linked causally to bone mineral density (BMD) and assess the effects of LDL-C-lowering drugs on BMD.

METHODS

Association between blood lipid levels and BMD was examined by epidemiological observation analyses in a US representative cohort NHANES III (n = 3638) and the Hong Kong Osteoporosis Study (HKOS; n = 1128). Two-sample Mendelian randomization (MR), employing genetic data from a large-scale genome-wide association study (GWAS) of blood lipids (n = 188 577), total body BMD (TB-BMD) (n = 66 628) and estimated BMD (eBMD) (n= 142 487), was performed to infer causality between LDL-C and BMD. Genetic proxies for LDL-C-lowering drugs were used to examine the drugs' effects on BMD.

RESULTS

In the NHANES III cohort, each standard deviation (SD) decrease in LDL-C was associated with a 0.045 SD increase in femoral neck BMD (95% CI: 0.009 - 0.081; P = 0.015). A similar increase in BMD was observed in the HKOS at femoral neck and lumbar spine. In MR analysis, a decrease in genetically predicted LDL-C was associated with an increase in TB-BMD {estimate per SD decrease, 0.038 [95% confidence interval (CI): 0.002 - 0.074]; P = 0.038} and eBMD [0.076 (0.042 - 0.111); P = 1.20x10-5]. Reduction in TB-BMD was causally associated with increased LDL-C [0.035 (0.033 - 0.066); P = 0.034]. Statins' LDL-C-lowering proxies were associated with increased TB-BMD [0.18 (0.044 - 0.316); P = 9.600x10-3] and eBMD [0.143 (0.062 - 0.223); P = 5.165x10-4].

CONCLUSIONS

Negative causal association exists between LDL-C level and BMD. Statins' LDL-C-lowering effect increases BMD, suggesting their protective effect on bone.

Expert Consensus on the Tapering of Oral Corticosteroids for the Treatment of Asthma. A Delphi Study

Rationale: There is a need to minimize oral corticosteroid (OCS) use in patients with asthma to prevent their costly and burdensome adverse effects. Current guidelines do not provide recommendations for OCS tapering in patients with asthma.Objectives: To develop expert consensus on OCS tapering among international experts.Methods: A modified Delphi method was used to develop expert consensus statements relating to OCS use, tapering, adverse effects, adrenal insufficiency, and patient-physician shared decision-making. Initial statements proposed by experts were categorized, filtered for repetition, and presented back to experts over three ranking rounds to obtain consensus (≥70% agreement).Measurements and Main Results: One hundred thirty-one international experts participated in the study, and 296 statements were ranked. Numerous recommendations and guidance regarding appropriate OCS use were established. Experts agreed that OCS tapering should be attempted in all patients with asthma receiving maintenance OCS therapy, with personalization of tapering rhythm and speed. The importance of recognizing individual adverse effects was also established; however, a unified approach to the assessment of adrenal insufficiency was not reached. Shared decision-making was considered an important goal during the tapering process.Conclusions: In this Delphi study, expert consensus statements were generated on OCS use, tapering, adverse-effect screening, and shared decision-making, which may be used to inform clinical practice. Areas of nonconsensus were identified, highlighting uncertainty among the experts around some aspects of OCS use in asthma, such as adrenal insufficiency, which underscores the need for further research in these domains.

Body Composition in Adolescents and Young Adults with Anorexia Nervosa: A Clinical Review

BACKGROUND

Anorexia nervosa is a serious health condition characterized by a significant low body weight and alteration in body composition components.

AIM

In the current paper, we aim to summarize the available literature concerning changes in body fat, lean, and bone masses, during anorexia nervosa and after complete weight restoration.

METHODS

Data were summarized using a narrative approach based on clinical expertise in the interpretation of the available evidence base in the literature.

RESULTS

The available data revealed three main findings. Firstly, anorexia nervosa causes a significant reduction in body fat mass, however it is completely restored after short-term weight normalization but with a central adiposity phenotype that does not seem to negatively influence treatment outcomes and appears to normalize after 1 year of normal weight maintenance. Secondly, anorexia nervosa causes a significant reduction in bone mineral density, but weight restoration is associated first (≈12 months) with stabilization of bone mineral density, followed by improvements (after ≈16 months); and finally, with complete normalization (after ≈30 months) after normal-weight maintenance. Thirdly, during anorexia nervosa loss of lean and skeletal body mass occurring in particular from the extremities rather than the central regions has been consistently reported, especially in patients with a Body Mass Index (BMI) ≤ 16.5 Kg/m2 however short-term weight restoration is associated with complete normalization.

CONCLUSION

Anorexia nervosa adversely affects body composition, however this medical complication seems to be reversible through the main treatment strategy of body weight restoration followed by normal weight maintenance, and this should be openly discussed with patients.

Radiofrequency Echographic Multi Spectrometry (REMS) for the diagnosis of osteoporosis in a European multicenter clinical context

BACKGROUND

Radiofrequency Echographic Multi Spectrometry (REMS) is a non-ionizing technology for the densitometric assessment of osteoporosis. It has already been validated in Italian women with respect to the current clinical reference technology, Dual-energy X-ray Absorptiometry (DXA).

PURPOSE

Aim of the current study was to assess the diagnostic accuracy of REMS technology with respect to DXA in a wider European clinical context.

METHODS

A total of 4307 female Caucasian patients aged between 30 and 90 years underwent DXA and REMS scans at femoral neck and/or lumbar spine (the site depending on the medical prescription). The acquired data underwent a rigorous quality check in order to exclude the erroneous DXA and REMS reports. The diagnostic agreement between the two technologies was assessed, also stratifying for patients' age groups. The ability to recognise previously fractured patients was also investigated.

RESULTS

Overall, 4245 lumbar spine scans and 4271 femoral neck scans were performed. The ability to discriminate patients with and without osteoporosis by femoral neck investigation resulted in sensitivity and specificity of 90.4% and 95.5%, respectively. For lumbar spine scans, a sensitivity of 90.9% and a specificity of 95.1% were obtained. The areas under the curve (AUCs) of the Receiver Operating Characteristic (ROC) curve evaluating the ability to discriminate groups of patients with previous osteoporotic fracture using DXA and REMS T-score values were 0.631 and 0.683 (p < 0.0001), respectively, for femoral neck scans, whereas 0.603 and 0.640 (p = 0.0002), respectively, for lumbar spine scans.

CONCLUSION

The diagnostic effectiveness of REMS technology at reference anatomical sites for the assessment of osteoporosis has been confirmed in a large series of female patients, spanning from younger and pre-menopausal to elderly women up to 90 years, in a multicenter European clinical context.

Brachial-ankle pulse wave velocity is associated with the risk of osteoporosis: a cross-sectional evidence from a Chinese community-based cohort

BACKGROUND

Association of arterial stiffness and osteoporosis has been well documented in elderly population. However, it is not clear whether they co-progress from the early stages through common mechanisms. The object of this study was to evaluate possible associations between arterial stiffness and osteoporosis by measuring brachial-ankle pulse wave velocity (baPWV) and the Osteoporosis Self-Assessment Tool for Asia (OSTA) index among a healthy population of Chinese aged 40 years and older. Whether baPWV can be used as a predictor of osteoporosis on OSTA was further assessed.

METHODS

This study was cross-sectional in design. Of 3984 adults aged 40 years and older in the Yunyan district of Guiyang (Guizhou, China) who underwent both OSTA and baPWV measurements within 1 month, 1407 were deemed eligible for inclusion (women, 1088; men, 319).

RESULTS

The mean baPWV was 1475 ± 302 cm/s (range,766-3459 cm/s). baPWV in 110 individuals with high risk of osteoporosis (OSTA index < - 4) was higher than that of individuals with non-high risk (1733 ± 461 cm/s vs. 1447 ± 304 cm/s, P < 0.001). OSTA index was negatively correlated with baPWV(ρ = - 0.296, P < 0.001) after adjusting for age, sex, body mass index, waist circumference, diastolic blood pressure, and creatinine clearance rate. baPWV was an independent predictor for the presence of high risk of osteoporosis (β = - 0.001, P < 0.001) and was consistent across age and sex subgroups, and the optimal baPWV cutoff value for predicting the presence of high risk of osteoporosis and fracture was 1693 cm/s. The AUC was 0.722 (95% confidence interval [CI], 0.667-0.777; P < 0.001, sensitivity of 52.8% and specificity of 83.6%).

CONCLUSIONS

We conclude that arterial stiffness measured by baPWV is well correlated with the severity of osteoporosis evaluated by OSTA. baPWV index may be a valuable tool for identifying individuals with risk of developing osteoporosis.

Novel coronavirus (COVID-19) diagnosis using computer vision and artificial intelligence techniques: a review

The universal transmission of pandemic COVID-19 (Coronavirus) causes an immediate need to commit in the fight across the whole human population. The emergencies for human health care are limited for this abrupt outbreak and abandoned environment. In this situation, inventive automation like computer vision (machine learning, deep learning, artificial intelligence), medical imaging (computed tomography, X-Ray) has developed an encouraging solution against COVID-19. In recent months, different techniques using image processing are done by various researchers. In this paper, a major review on image acquisition, segmentation, diagnosis, avoidance, and management are presented. An analytical comparison of the various proposed algorithm by researchers for coronavirus has been carried out. Also, challenges and motivation for research in the future to deal with coronavirus are indicated. The clinical impact and use of computer vision and deep learning were discussed and we hope that dermatologists may have better understanding of these areas from the study.

The Risk Factor of Worsening Low Back Pain in Older Adults Living in a Local Area of Japan: The GAINA Study

Background

Several factors, particularly osteoporosis, obesity, and a lack of exercise, contribute to low back pain (LBP). This observational longitudinal cohort study to identify the risk factors for worsening low back pain.

Methods

We sent a self-administered questionnaire and a consent form for this study to 1,450 subjects aged > 40 years in Hino, Japan. Baseline assessments of 273 individuals undergoing medical check-ups were conducted from 2014 to 2016. The subjects were divided into Group A (no change or improvement in LBP) and Group B (worsening LBP). LBP was assessed using a visual analog scale; body mass index (BMI), bone mineral density, skeletal muscle index (SMI), standing posture, and habitual exercise frequency were also evaluated. We defined, habitual exercise as nontherapeutic exercise (e.g. swimming, walking, physical exercise and work out).

Results

Overall, 81.2% subjects performed habitual exercise in Group A, a greater number of subjects than the 40.8% in Group B. BMI, SMI, and bone mineral density (BMD) were not significantly different between the two groups. Lack of exercise was a significant risk factor for worsening of LBP. On the other hand, the lack of osteoporosis treatment was significantly different between subjects with worsening LBP despite habitual exercise and those who did not perform habitual exercise.

Conclusion

Although habitual exercise is useful to prevent LBP, it may not necessarily be useful for those with a lack of osteoporosis treatment. Although exercise is typically posited to prevent LBP, it may not be effective in preventing LBP associated with osteoporosis.

Functional Photoacoustic and Ultrasonic Assessment of Osteoporosis: A Clinical Feasibility Study

Objective and Impact Statement. To study the feasibility of combined functional photoacoustic (PA) and quantitative ultrasound (US) for diagnosis of osteoporosis in vivo based on the detection of chemical and microarchitecture (BMA) information in calcaneus bone. Introduction. Clinically available X-ray or US technologies for the diagnosis of osteoporosis do not report important parameters such as chemical information and BMA. With unique advantages, including good sensitivity to molecular and metabolic properties, PA bone assessment techniques hold a great potential for clinical translation. Methods. By performing multiwavelength PA measurements, the chemical information in the human calcaneus bone, including mineral, lipid, oxygenated-hemoglobin, and deoxygenated-hemoglobin, were assessed. In parallel, by performing PA spectrum analysis, the BMA as an important bone physical property was quantified. An unpaired t -test and a two-way ANOVA test were conducted to compare the outcomes from the two subject groups. Results. Multiwavelength PA measurement is capable of assessing the relative contents of several chemical components in the trabecular bone in vivo, including both minerals and organic materials such as oxygenated-hemoglobin, deoxygenated-hemoglobin, and lipid, which are relevant to metabolic activities and bone health. In addition, PA measurements of BMA show good correlations (R 2 up to 0.65) with DEXA. Both the chemical and microarchitectural measurements from PA techniques can differentiate the two subject groups. Conclusion. The results from this initial clinical study suggest that PA techniques, by providing additional chemical and microarchitecture information relevant to bone health, may lead to accurate and early diagnosis, as well as sensitive monitoring of the treatment of osteoporosis.

COVID-19 Control by Computer Vision Approaches: A Survey

The COVID-19 pandemic has triggered an urgent call to contribute to the fight against an immense threat to the human population. Computer Vision, as a subfield of artificial intelligence, has enjoyed recent success in solving various complex problems in health care and has the potential to contribute to the fight of controlling COVID-19. In response to this call, computer vision researchers are putting their knowledge base at test to devise effective ways to counter COVID-19 challenge and serve the global community. New contributions are being shared with every passing day. It motivated us to review the recent work, collect information about available research resources, and an indication of future research directions. We want to make it possible for computer vision researchers to find existing and future research directions. This survey article presents a preliminary review of the literature on research community efforts against COVID-19 pandemic.

ZFBONE: An ImageJ toolset for semi-automatic analysis of zebrafish bone structures

The last decade has seen an increased interest in the discovery of compounds with bone anabolic activity to treat skeletal disorders such as osteoporosis and increase the well-being of patients. Due to the many technical advantages over classical rodent systems, zebrafish (Danio rerio) has been increasingly used in screening pipelines, in particular those aiming at identifying osteoactive compounds with pharmacological potential. Because compound osteoactivity is mostly determined in zebrafish through the morphometric analysis of bone structures, image analysis, rather than screening assay implementation, molecule availability and image acquisition, represents a bottleneck to the screening throughput. The absence of auto/semi-automatic tools for image analysis of fish bone structures is also a limitation to a broader usage of zebrafish screening pipelines. We present here ZFBONE (for ZebraFish BONE), an open-source, freely available, user-friendly, rapid and reliable toolset, aiming at accelerating image analysis by automating the morphometric assessment of zebrafish bone structures, but also at increasing data accuracy by reducing operator bias. Tools included in ZFBONE allow users to assess, from 2D images, morphometric parameters of several bone structures (e.g. operculum, caudal fin rays and scales) but also the extent and the intensity of bone-specific colorations. ZFBONE has been developed using the open-source ImageJ software, to make it available to the whole zebrafish research community, but also to have it easily modifiable according to user demands. ZFBONE can also be used toward the standardization of zebrafish screening protocols in academia and industry.

COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data

Detecting COVID-19 early may help in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how transfer learning from deep learning models can be used to perform COVID-19 detection using images from three most commonly used medical imaging modes X-Ray, Ultrasound, and CT scan. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent deep learning image classification models. We identify a suitable Convolutional Neural Network (CNN) model through initial comparative study of several popular CNN models. We then optimize the selected VGG19 model for the image modalities to show how the models can be used for the highly scarce and challenging COVID-19 datasets. We highlight the challenges (including dataset size and quality) in utilizing current publicly available COVID-19 datasets for developing useful deep learning models and how it adversely impacts the trainability of complex models. We also propose an image pre-processing stage to create a trustworthy image dataset for developing and testing the deep learning models. The new approach is aimed to reduce unwanted noise from the images so that deep learning models can focus on detecting diseases with specific features from them. Our results indicate that Ultrasound images provide superior detection accuracy compared to X-Ray and CT scans. The experimental results highlight that with limited data, most of the deeper networks struggle to train well and provides less consistency over the three imaging modes we are using. The selected VGG19 model, which is then extensively tuned with appropriate parameters, performs in considerable levels of COVID-19 detection against pneumonia or normal for all three lung image modes with the precision of up to 86% for X-Ray, 100% for Ultrasound and 84% for CT scans.

Texture Analysis and Genetic Algorithms for Osteoporosis Diagnosis

Early diagnosis of osteoporosis can efficiently predict fracture risk. There is a great demand to prevent this disease. The goal of this study was to distinguish osteoporotic cases from healthy controls on 2D bone radiograph images, using texture analysis and genetic algorithms (GAs). Gray Level Co-occurrence Matrix (GLCM), Run length Matrix (RLM) and Binarized Statistical Image Features (BSIF) were used for texture analysis. Features are numerous and parameter-dependent. The related experts can pick out the useful input features for the classifier. It however remains a difficult task and may be inefficient or even harmful as the data pattern is not clear. In this paper, GAs were used to optimize the two parameters of the co-occurrence matrix (distance parameter or pixel separation, orientation or direction) and the number of gray levels used in the preprocessing quantification step. GAs were also used to select the best combination of features extracted from GLCM and RLM matrices. Experiments were conducted on two populations composed of Osteoporotic Patients and Control Subjects. Results show that GAs combined with GLCM and BSIF features can improve the classification rates (ACC = 87.50%) obtained using GLCM (ACC = 77.8%) alone.

Radiofrequency echographic multi spectrometry for the prediction of incident fragility fractures: A 5-year follow-up study

PURPOSE

To investigate the effectiveness of the T-score values provided by Radiofrequency Echographic Multi Spectrometry (REMS) in the identification of patients at risk for incident osteoporotic fractures.

METHODS

A population of Caucasian women (30-90 years), enrolled from 2013 to 2016, underwent dual X-ray absorptiometry (DXA) and REMS scans at axial sites. The incidence of fragility fractures was assessed during a follow-up period up to 5 years. Afterwards, patients with and without incident fractures were stratified in two age-matched groups with a 1: 2 proportion (Group F' and Group NF', respectively). The performance of REMS T-score in discriminating between the two groups was quantitatively assessed and compared with DXA.

RESULTS

1516 patients were enrolled and 1370 completed the follow-up (mean ± SD: 3.7 ± 0.8 years; range: 1.9-5.0 years). Fracture incidence was 14.0%. Age-matched groups included 175 fractured patients and 350 non-fractured ones, respectively (median age 70.2 [interquartile range: 61.0-73.3] and 67.3 [65.4-69.8] years, p-value ns). The groups resulted also balanced for height, weight and BMI (p-values ns). As expected, the differences in REMS T-score (for vertebral site, -2.9 [-3.6 to -1.9] in Group F', -2.2 [-2.9 to -1.2] in Group NF') and DXA T-score (-2.8 [-3.3 to -1.9] in Group F', -2.2 [-2.9 to -1.4] in Group NF') were statistically significant (p-value <0.001). Analogous results were obtained for femoral neck. Considering the T-score cut-off of -2.5, REMS identified Group F' patients with a sensitivity of 65.1% and specificity of 57.7% of (OR = 2.6, 95%CI: 1.77-3.76, p < 0.001), whereas DXA showed a sensitivity of 57.1% and a specificity of 56.3% (OR = 1.7, 95%CI: 1.20-2.51, p-value = 0.0032). For femoral neck, REMS sensitivity and specificity were 40.2% and 79.9%, respectively, with an OR of 2.81 (95%CI: 1.80-4.39, p < 0.001). DXA, instead, showed a sensitivity and specificity of 42.3% and 79.3%, respectively, with an OR of 2.68 (95%CI: 1.71-4.21, p < 0.001).

CONCLUSIONS

REMS T-score resulted an effective predictor for the risk of incident fragility fractures in a population-based sample of female subjects, representing a promising parameter to enhance osteoporosis diagnosis in the clinical routine.