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

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.