Predictor Of Bone Mineral Density Research Articles

Artificial intelligence-enhanced opportunistic screening of osteoporosis in CT scan: a scoping Review.

This scoping review aimed to assess the current research on artificial intelligence (AI)--enhanced opportunistic screening approaches for stratifying osteoporosis and osteopenia risk by evaluating vertebral trabecular bone structure in CT scans. PubMed, Scopus, and Web of Science databases were systematically searched for studies published between 2018 and December 2023. Inclusion criteria encompassed articles focusing on AI techniques for classifying osteoporosis/osteopenia or determining bone mineral density using CT scans of vertebral bodies. Data extraction included study characteristics, methodologies, and key findings. Fourteen studies met the inclusion criteria. Three main approaches were identified: fully automated deep learning solutions, hybrid approaches combining deep learning and conventional machine learning, and non-automated solutions using manual segmentation followed by AI analysis. Studies demonstrated high accuracy in bone mineral density prediction (86-96%) and classification of normal versus osteoporotic subjects (AUC 0.927-0.984). However, significant heterogeneity was observed in methodologies, workflows, and ground truth selection. The review highlights AI's promising potential in enhancing opportunistic screening for osteoporosis using CT scans. While the field is still in its early stages, with most solutions at the proof-of-concept phase, the evidence supports increased efforts to incorporate AI into radiologic workflows. Addressing knowledge gaps, such as standardizing benchmarks and increasing external validation, will be crucial for advancing the clinical application of these AI-enhanced screening methods. Integration of such technologies could lead to improved early detection of osteoporotic conditions at a low economic cost.

Sonographic cortical bone thickness measurement: can it predict bone mineral density in the pediatric population?

To explore sonographic cortical bone thickness (CoT) as a potential indicator of bone mineral density (BMD) measured by dual-energy X-ray absorptiometry for screening and diagnosing pediatric osteoporosis. A prospective study included 41 osteopenic or osteoporotic patients and 52 healthy children. Radius cortical thickness (R-CoT), tibial cortical thickness (T-CoT), and second metatarsal cortical thickness (M-CoT) were measured by B-mode ultrasound; CoT values were compared between groups and the correlation between BMD and CoT was examined. There were no significant differences in R-CoT (P = 0.433), T-CoT (P = 0.057), and M-CoT (P = 0.978) values between the patient and control groups. No significant correlations were found between BMD T-scores and R-CoT (r = -0.073, P = 0.490), T-CoT (r = -0.154, P = 0.141), and M-CoT (r = 0.047, P = 0.657) values. Sonographic CoT values in children do not correlate with BMD values. Unlike in adults, sonographic CoT measurements do not appear to have a role in assessing BMD in the pediatric population.

Preoperative prediction for periprosthetic bone loss and individual evaluation of bisphosphonate effect after total hip arthroplasty using artificial intelligence.

This study was designed to develop a model for predicting bone mineral density (BMD) loss of the femur after total hip arthroplasty (THA) using artificial intelligence (AI), and to identify factors that influence the prediction. Additionally, we virtually examined the efficacy of administration of bisphosphonate for cases with severe BMD loss based on the predictive model. The study included 538 joints that underwent primary THA. The patients were divided into groups using unsupervised time series clustering for five-year BMD loss of Gruen zone 7 postoperatively, and a machine-learning model to predict the BMD loss was developed. Additionally, the predictor for BMD loss was extracted using SHapley Additive exPlanations (SHAP). The patient-specific efficacy of bisphosphonate, which is the most important categorical predictor for BMD loss, was examined by calculating the change in predictive probability when hypothetically switching between the inclusion and exclusion of bisphosphonate. Time series clustering allowed us to divide the patients into two groups, and the predictive factors were identified including patient- and operation-related factors. The area under the receiver operating characteristic (ROC) curve (AUC) for the BMD loss prediction averaged 0.734. Virtual administration of bisphosphonate showed on average 14% efficacy in preventing BMD loss of zone 7. Additionally, stem types and preoperative triglyceride (TG), creatinine (Cr), estimated glomerular filtration rate (eGFR), and creatine kinase (CK) showed significant association with the estimated patient-specific efficacy of bisphosphonate. Periprosthetic BMD loss after THA is predictable based on patient- and operation-related factors, and optimal prescription of bisphosphonate based on the prediction may prevent BMD loss.

Deep learning in the radiologic diagnosis of osteoporosis: a literature review.

Osteoporosis is a systemic bone disease characterized by low bone mass, damaged bone microstructure, increased bone fragility, and susceptibility to fractures. With the rapid development of artificial intelligence, a series of studies have reported deep learning applications in the screening and diagnosis of osteoporosis. The aim of this review was to summary the application of deep learning methods in the radiologic diagnosis of osteoporosis. We conducted a two-step literature search using the PubMed and Web of Science databases. In this review, we focused on routine radiologic methods, such as X-ray, computed tomography, and magnetic resonance imaging, used to opportunistically screen for osteoporosis. A total of 40 studies were included in this review. These studies were divided into three categories: osteoporosis screening (n = 20), bone mineral density prediction (n = 13), and osteoporotic fracture risk prediction and detection (n = 7). Deep learning has demonstrated a remarkable capacity for osteoporosis screening. However, clinical commercialization of a diagnostic model for osteoporosis remains a challenge.

External validation of a deep learning model for predicting bone mineral density on chest radiographs.

In this study, we performed external validation (EV) of a developed deep learning model for predicting bone mineral density (BMD) of femoral neck on chest radiographs to verify the usefulness of this model in clinical practice. This study included patients who visited any of the collaborating facilities from 2010 to 2020 and underwent chest radiography and dual-energy X-ray absorptiometry (DXA) at the femoral neck in the year before and after their visit. A total of 50,114 chest radiographs were obtained, and BMD was measured using DXA. We developed the model with 47,150 images from 17 facilities and performed EV with 2914 images from three other facilities (EV dataset). We trained the deep learning model via ensemble learning based on chest radiographs, age, and sex to predict BMD using regression. The outcomes were the correlation of the predicted BMD and measured BMD with diagnoses of osteoporosis and osteopenia using the T-score estimated from the predicted BMD. The mean BMD was 0.64±0.14 g/cm2 in the EV dataset. The BMD predicted by the model averaged 0.61±0.08 g/cm2, with a correlation coefficient of 0.68 (p<0.01) when compared with the BMD measured using DXA. The accuracy, sensitivity, and specificity of the model were 79.0%, 96.6%, and 34.1% for T-score < -1 and 79.7%, 77.1%, and 80.4% for T-score ≤ -2.5, respectively. Our model, which was externally validated using data obtained at facilities other than the development environment, predicted BMD of femoral neck on chest radiographs. The model performed well and showed potential for clinical use.

Integrating both common and rare variants to predict bone mineral density and fracture.

Integrating both common and rare variants to predict bone mineral density and fracture.

Predictors of bone mineral density in patients receiving glucocorticoid replacement for Addison's disease.

Patients receiving long-term glucocorticoid (GC) treatment are at risk of osteoporosis, while bone effects of substitution doses in Addison's disease (AD) remain equivocal. The project was aimed to evaluate serum bone turnover markers (BTMs): osteocalcin, type I procollagen N-terminal propeptide (PINP), collagen C-terminal telopeptide (CTX), sclerostin, DKK-1 protein, and alkaline phosphatase (ALP) in relation to bone mineral density (BMD) during GC replacement. Serum BTMs and hormones were assessed in 80 patients with AD (22 males, 25 pre- and 33 postmenopausal females) on hydrocortisone (HC) substitution for ≥3 years. Densitometry with dual-energy X-ray absorptiometry covered the lumbar spine (LS) and femoral neck (FN). Among BTMs, only PINP levels were altered in AD. BMD Z-scores remained negative except for FN in males. Considering T-scores, osteopenia was found in LS in 45.5% males, 24% young and 42.4% postmenopausal females, while osteoporosis in 9.0%, 4.0% and 21.1%, respectively. Lumbar BMD correlated positively with body mass (p = 0.0001) and serum DHEA-S (p = 9.899 × 10-6). Negative correlation was detected with HC dose/day/kg (p = 0.0320), cumulative HC dose (p = 0.0030), patient's age (p = 1.038 × 10-5), disease duration (p = 0.0004), ALP activity (p = 0.0041) and CTX level (p = 0.0105). However, only age, body mass, ALP, serum CTX, and sclerostin remained independent predictors of LS BMD. Standard HC substitution does not considerably accelerate BMD loss in AD patients and their serum BTMs: CTX, osteocalcin, sclerostin, DKK-1, and ALP activity remain within the reference ranges. Independent predictors of low lumbar spine BMD, especially ALP activity, serum CTX and sclerostin, might be monitored during GC substitution.

The Influence of Obesity on Bone Health in Post-COVID-19 Recovery- Single-Center Experience.

The ongoing repercussions of the COVID-19 pandemic include potentially deleterious impacts on bone health. This research aimed to ascertain the effects of COVID-19 on the bone health of obese and non-obese Indian individuals. We executed a case-control study enrolling individuals who recovered from COVID-19. Participants were stratified into obese and non-obese groups based on their BMI. Comprehensive assessments encompassed anthropometric evaluations, laboratory tests, and bone mineral density (BMD) measurements using dual-energy X-ray absorptiometry (DEXA). From April to July 2022, we enrolled obese (n = 27, mean BMI = 30.54 ± 4.51 kg/m 2) and non-obese (n = 23, mean BMI = 21.97 ± 2.20 kg/m 2) individuals. The cohort's average age was 36.08 ± 15.81 years, with a male-to-female ratio of 1.6:1. There was a difference in BMD, especially at the total hip, between the two groups. BMD at the spine (L1-L4), the neck of the femur, and ultra-distal radius were consistent across both groups. Weight exhibited a significant positive correlation with BMD at L1-L4 (r = 0.40, p = 0.003) and the left femur total (r = 0.27, p = 0.001). Haemoglobin levels were lower in the obese group compared to their non-obese counterparts (12.3 ± 2.0 vs 13.6 ± 1.9, p = 0.01). Multivariate analysis underscored weight as a crucial predictor for BMD at the spine (L1-L4, p = 0.003) and total hip (p = 0.001). Even with advanced age, obese post-COVID-19 individuals demonstrate a higher bone mineral density (BMD) at the hip than non-obese subjects.

Identifying Rare Genetic Determinants for Improved Polygenic Risk Prediction of Bone Mineral Density and Fracture Risk.

Osteoporosis and fractures severely impact the elderly population. Polygenic risk scores for bone mineral density have demonstrated potential clinical utility. However, the value of rare genetic determinants in risk prediction has not been assessed. With whole-exome sequencing data from 436,824 UK Biobank participants, we assigned White British ancestry individuals into a training data set (n = 317,434) and a test data set (n = 74,825). In the training data set, we developed a common variant-based polygenic risk score for heel ultrasound speed of sound (SOS). Next, we performed burden testing to identify genes harboring rare determinants of bone mineral density, targeting influential rare variants with predicted high deleteriousness. We constructed a genetic risk score, called ggSOS, to incorporate influential rare variants in significant gene burden masks into the common variant-based polygenic risk score. We assessed the predictive performance of ggSOS in the White British test data set, as well as in populations of non-White British European (n = 18,885), African (n = 7165), East Asian (n = 2236), South Asian (n = 9829), and other admixed (n = 1481) ancestries. Twelve genes in pivotal regulatory pathways of bone homeostasis harbored influential rare variants associated with SOS (p < 5.5 × 10-7 ), including AHNAK, BMP5, CYP19A1, FAM20A, FBXW5, KDM5B, KREMEN1, LGR4, LRP5, SMAD6, SOST, and WNT1. Among 4013 (5.4%) individuals in the test data set carrying these variants, a one standard deviation decrease in ggSOS was associated with 1.35-fold (95% confidence interval [CI] 1.16-1.57) increased hazard of major osteoporotic fracture. However, compared with a common variant-based polygenic risk score (C-index = 0.641), ggSOS had only marginally improved prediction accuracy in identifying at-risk individuals (C-index = 0.644), with overlapping confidence intervals. Similarly, ggSOS did not demonstrate substantially improved predictive performance in non-European ancestry populations. In summary, modeling the effects of rare genetic determinants may assist polygenic prediction of fracture risk among carriers of influential rare variants. Nonetheless, improved clinical utility is not guaranteed for population-level risk screening. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

MRI‑based vertebral bone quality score is a comprehensive index reflecting the quality of bone and paravertebral muscle

BACKGROUNDRecently, vertebral bone quality (VBQ) score has been shown to predict bone mineral density (BMD) and spine-related postoperative complications. However, in clinical work, we found that patients with higher VBQ scores also had more severe paravertebral muscle degeneration. PURPOSETo explore the ability of the VBQ score to evaluate BMD and paravertebral muscle quality. STUDY DESIGN/SETTINGRetrospective single-center cohort. PATIENT SAMPLEPatients in the spinal surgery department of our hospital. OUTCOME MEASURESBone mineral density and T-score were measured by dual-energy X-ray absorptiometry (DXA). The Picture Archiving and Communication Systems (PACS) measured the cross-sectional area (CSA) of the paravertebral muscles. Image J software was used to measure the degree of fat infiltration (DFF) of the paraspinal muscle. METHODSPatients who underwent lumbar MRI and DXA simultaneously within two weeks were enrolled. The VBQ score was calculated using T1-weighted lumbar MRI images. Firstly, BMD-related and muscle-related parameters of patients with different VBQ scores were compared. Then, the correlation coefficients between the VBQ score and the parameters of BMD and paravertebral muscle were calculated. Finally, multivariate linear analysis was used to compare the contribution of each variable to the VBQ score. RESULTSA total of 101 patients were eventually included in this study for analysis. When the VBQ score was greater than 3.0, the patients were mostly female, older, less likely to smoke, and had lower BMD. Interestingly, we found that patients with VBQ scores greater than 3.0 had smaller CSA of the paravertebral muscles (ES: 17.53±3.36 vs 19.13±3.97, p=.032; total: 29.59±5.27 vs 34.12±7.02, p<.001) and higher DFF (MF: 22.47±5.93 vs 19.64±5.28, p=.015; ES: 17.71±4.67 vs 15.74±4.62, p=.038; PM: 13.70±3.32 vs 11.33±3.02, p<.001; average: 17.96±3.78 vs 15.57±3.42, p=.001). The VBQ score was negatively correlated with the CSA (MF: r=−0.316, p=.001; ES: r =−0.388, p=.001; PM: r=0.388, p=.001) and positively correlated with the DFF (MF: r=0.344, p<.001; ES: r=0.439, p<.001; PM: =0.416, p<.001). In multivariate linear analysis, BMD, total CSA, and average DFF determined the value of the VBQ score, and the contribution of paravertebral muscle was higher than that of BMD (BMD: r=−0.203, p=.024; total CSA: r=−0.294, p=.003; average DFF: r=0.261, p=.011). CONCLUSIONSThis study is the first to find a positive association between the VBQ score and paravertebral muscle degeneration, and this association may be independent of BMD. VBQ can reflect the quality of bone and paravertebral muscle, which is its special advantage in clinical application.

Early-Onset Osteopenia and Osteoporosis in Young Women: An Emerging Epidemic?

Osteoporosis and osteopenia are conditions under which the bodies creation of new bone tissue does not sufficiently match the degradation rate of existing bone. Both conditions are characterized by low bone mineral density, deterioration of bone tissue, and disruption of bone microarchitecture. Osteopenia is often considered a precursor to osteoporosis, and is typically an important factor in predicting bone mineral density degradation progression rates throughout one’s life.

Serum high sensitivity C-reactive protein poorly predicts bone mineral density: A NHANES 2017-2020 analysis.

A reliable, widely available method to detect osteoporosis prior to fracture is needed. Serum levels of C-reactive protein may independently predict low bone mineral density (BMD) and high fracture risk. Existing empirical data focus on sexually and/or racially homogenous populations. This study tests the hypotheses that: C-reactive protein (1) negatively correlates with BMD and (2) fracture history, and (3) independently predicts BMD and fracture history in a diverse population. NHANES 2017-2020 pre-pandemic cycle data were analyzed in R studio. Strength and direction of relationships (-1 to +1) between variables were determined using Kendall's rank correlation coefficient (τ). Linear models were optimized to predict femoral neck or lumbar spine BMD. C-reactive protein positively correlated with femoral (τ = 0.09, p<0.0001) and spine BMD (τ = 0.10, p<0.0001). Individuals identifying as female demonstrated more robust, but still weak, correlations between C-reactive protein and femoral neck (τ = 0.15, p<0.0001; male, τ = 0.06, p = 0.051) and spine BMD (τ = 0.16, p<0.0001; male, τ = 0.06, p = 0.04). C-reactive protein positively correlated with fracture history (τ = 0.083, p = 0.0009). C-reactive protein significantly predicted femoral neck (R2 = 0.022, p = 0.0001) and spine BMD (R2 = 0.028, p<0.0001) and fracture history (R2 = 0.015, p<0.0001). Exploratory analyses identified weight was the single best predictor for femoral neck (R2 = 0.24, p<0.0001) and spine BMD (R2 = 0.21, p<0.0001). In sum, C-reactive protein statistically correlates with and predicts femoral neck and spine BMD, but the magnitude is too low to be biologically meaningful. While weight is a more robust predictor, individuals who are overweight or obese account for nearly half of all osteoporotic fractures, limiting the predictive power of this variable at identifying individuals at risk for osteoporosis. Identification of a robust predictor of fracture risk in a diverse population and across of range of body weights and compositions is needed.

A novel non-invasive method for predicting bone mineral density and fracture risk using demographic and anthropometric measures

Fractures are costly to treat and can significantly increase morbidity. Although dual-energy x-ray absorptiometry (DEXA) is used to screen at risk people with low bone mineral density (BMD), not all areas have access to one. We sought to create a readily accessible, inexpensive, high-throughput prediction tool for BMD that may identify people at risk of fracture for further evaluation. Anthropometric and demographic data were collected from 492 volunteers (♂275, ♀217; [44 ​± ​20] years; Body Mass Index (BMI) = [27.6 ​± ​6.0] kg/m2) in addition to total body bone mineral content (BMC, kg) and BMD measurements of the spine, pelvis, arms, legs and total body. Multiple-linear-regression with step-wise removal was used to develop a two-step prediction model for BMC followed by BMC. Model selection was determined by the highest adjusted R2, lowest error of estimate, and lowest level of variance inflation (α ​= ​0.05). Height (HTcm), age (years), sexm=1, f=0, %body fat (%fat), fat free mass (FFMkg), fat mass (FMkg), leg length (LLcm), shoulder width (SHWDTHcm), trunk length (TRNKLcm), and pelvis width (PWDTHcm) were observed to be significant predictors in the following two-step model (p ​< ​0.05). Step1: BMC (kg) = (0.006 3 × HT) ​+ ​(−0.002 4 × AGE) ​+ ​(0.171 2 × SEXm=1, f=0) ​+ ​(0.031 4 × FFM) ​+ ​(0.001 × FM) ​+ ​(0.008 9 × SHWDTH) ​+ ​(−0.014 5 × TRNKL) ​+ ​(−0.027 8 × PWDTH) - 0.507 3; R2 ​= ​0.819, SE ​± ​0.301. Step2: Total body BMD (g/cm2) = (−0.002 8 × HT) ​+ ​(−0.043 7 × SEXm=1, f=0) ​+ ​(0.000 8 × %FAT) ​+ ​(0.297 0 × BMC) ​+ ​(−0.002 3 × LL) ​+ ​(0.002 3 × SHWDTH) ​+ ​(−0.002 5 × TRNKL) ​+ ​(−0.011 3 × PWDTH) ​+ ​1.379; R2 ​= ​0.89, SE ​± ​0.054. Similar models were also developed to predict leg, arm, spine, and pelvis BMD (R2 ​= ​0.796–0.864, p ​< ​0.05). The equations developed here represent promising tools for identifying individuals with low BMD at risk of fracture who would benefit from further evaluation, especially in the resource or time restricted setting.

198. Bone modulus has value beyond that of bone mineral density and Houndsfield units for prediction of postoperative complications in spinal fusion patients

198. Bone modulus has value beyond that of bone mineral density and Houndsfield units for prediction of postoperative complications in spinal fusion patients

Prevalence of and risk factors for vertebral fracture and low bone mineral density among Peruvian women aging with HIV.

Studies have shown that patients with HIV are at increased risk for bone loss and fracture due to a combination of host, viral, and antiretroviral therapy (ART)-related factors. We aimed to explore the prevalence of vertebral fracture (VF) and low bone mineral density (BMD) among women aging with HIV in Peru and identify risk factors for osteoporosis and fracture in this population. We enrolled women living with and without HIV aged ≥40 years between 2019 and 2020. Participants completed a survey and obtained dual X-ray absorptiometry (DXA) test to assess BMD at the lumbar spine (LS), femoral neck (FN), and total hip (TH). A subset of patients also obtained lateral thoracolumbar X-rays. Presence of VF was determined using the Genant semiquantitative method. Regression analyses were used to model associations between key risk factors and BMD. 104 women living with HIV and 212 women living without HIV were enrolled with a mean age of 52.4±8.2 and 56.4±8.8 years (p < 0.001). Among postmenopausal women (257/316, 81.3%), 26.3% of women living with HIV and 25.9% of those without HIV had osteoporosis. Among the 88 women living with HIV and 178 women living without HIV who obtained thoracolumbar X-rays, 12.5% and 6.2%, respectively, had at least one VF. Based on DXA and the FRAX score, 22/104 women living with HIV met criteria for osteoporosis treatment according to national guidelines; however, none were on treatment. Propensity score matching revealed that women living with HIV had 0.032 g/cm2 lower FN BMD (p = 0.012) and 0.034 g/cm2 lower TH BMD (p = 0.041) compared to women without HIV. In this study, women living with HIV on long-standing ART had increased VF prevalence compared to the slightly older group of women without HIV. Age and BMI were independent predictors for BMD at the lumbar spine, hip, and femoral neck among women living with HIV, and there was a treatment gap among women who met criteria for osteoporosis treatment. Larger studies are needed in this region to identify individuals at risk for fracture and to inform prevention guidelines.

Body mass index as predictor of bone mineral density in postmenopausal women

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Feasibility of Bone Mineral Density and Bone Microarchitecture Assessment Using Deep Learning With a Convolutional Neural Network.

We evaluated the feasibility of using deep learning with a convolutional neural network for predicting bone mineral density (BMD) and bone microarchitecture from conventional computed tomography (CT) images acquired by multivendor scanners. We enrolled 402 patients who underwent noncontrast CT examinations, including L1-L4 vertebrae, and dual-energy x-ray absorptiometry (DXA) examination. Among these, 280 patients (3360 sagittal vertebral images), 70 patients (280 sagittal vertebral images), and 52 patients (208 sagittal vertebral images) were assigned to the training data set for deep learning model development, the validation, and the test data set, respectively. Bone mineral density and the trabecular bone score (TBS), an index of bone microarchitecture, were assessed by DXA. BMDDL and TBSDL were predicted by deep learning with a convolutional neural network (ResNet50). Pearson correlation tests assessed the correlation between BMDDL and BMD, and TBSDL and TBS. The diagnostic performance of BMDDL for osteopenia/osteoporosis and that of TBSDL for bone microarchitecture impairment were evaluated using receiver operating characteristic curve analysis. BMDDL and BMD correlated strongly (r = 0.81, P < 0.01), whereas TBSDL and TBS correlated moderately (r = 0.54, P < 0.01). The sensitivity and specificity of BMDDL for identifying osteopenia or osteoporosis were 93% and 90%, and 100% and 94%, respectively. The sensitivity and specificity of TBSDL for identifying patients with bone microarchitecture impairment were 73% for all values. The BMDDL and TBSDL derived from conventional CT images could identify patients who should undergo DXA, which could be a gatekeeper tool for detecting latent osteoporosis/osteopenia or bone microarchitecture impairment.

Bone Mineral Density Prediction from CT Image: A Novel Approach using ANN.

Though treatable, osteoporosis continues as a substantially underdiagnosed and undertreated condition. Bone mineral density (BMD) monitoring will definitely aid in the prediction and prevention of medical emergencies arising from osteoporosis. Although quantitative computed tomography (QCT) is one of the most widely accepted tools for measuring BMD, it lacks the contribution of bone architecture in predicting BMD, which is significant as aging progresses. This paper presents an innovative approach for the prediction of BMD incorporating bone architecture that involves no extra cost, time, and exposure to severe radiation. In this approach, the BMD is predicted using clinical CT scan images taken for other indications based on image processing and artificial neural network (ANN). The network used in this study is a standard backpropagation neural network having five input neurons with one hidden layer having 40 neurons with a tan-sigmoidal activation function. The Digital Imaging and Communications in Medicine (DICOM) image properties extracted from QCT of human skull and femur bone of rabbit that are closely associated with the BMD are used as input parameters of the ANN. The density value of the bone which is computed from the Hounsfield units of QCT scan image through phantom calibration is used as the target value for training the network. The ANN model predicts the density values using the image properties from the clinical CT of the same rabbit femur bone and is compared with the density value computed from QCT scan. The correlation coefficient between predicted BMD and QCT density valued to 0.883. The proposed network can assist clinicians in identifying early stage of osteoporosis and devise suitable strategies to improve BMD with no additional cost.

Circulating IGFBP-3 and Interleukin 6 as Predictors of Osteoporosis in Postmenopausal Women: A Cross-Sectional Study.

To explore the relationship between circulating IGFBP-3, IL-6, and bone mineral density and the potential diagnostic role of circulating IGFBP-3 and IL-6 in postmenopausal women with osteoporosis. Eighty-five postmenopausal women at Soochow University's First Affiliated Hospital, Osteoporosis and Menopause Clinics, were recruited. Forty-five of 85 women were diagnosed with osteoporosis. Circulating IL-6, PTH, 1,25(OH)2D3, osteocalcin (OST), IGF-1, IGFBP-3, and bone mineral density (BMD) of the lumbar spine (LS) and femoral neck (FN) were measured in 40 ordinary and 45 osteoporotic women. A simple regression analysis calculated the correlation between age, BMD, IL-6, and IGFBP-3. Multiple stepwise regression analyses were conducted to determine which variables were independently related to BMD. The potential role of IGFBP-3 and IL-6 in the diagnosis of postmenopausal osteoporosis was predicted using the area under the receiver operating characteristic curve (ROC, AUC). Age, years since menopause, and circulating IL-6, PTH, and IGFBP-3 were significantly higher in the osteoporosis group compared to the normal group. Osteoporotic women had substantially lower BMDs of the LS and FN than normal women. Age-related increases were found for IGFBP-3 and IL-6, whereas age-related decreases were observed for LS/FN BMD. IGFBP-3 and IL-6 were both negatively correlated with LS and FN BMD. Stepwise multiple regression analysis showed that IGFBP-3 and IL-6 were strong predictors of BMD in postmenopausal women. AUC cut-off values (IGFBP-3: 3.65, IL-6: 0.205) were best evaluated for the diagnosis of postmenopausal women with osteoporosis, and the AUC for circulating IGFBP-3 and IL-6 were 0.706 (95% CI 0.594-0.818) and 0.685 (95% CI 0.571-0.798), respectively. In this cross-sectional study of postmenopausal women, IGFBP-3 and IL-6 were negatively related to BMD. Circulating IGFBP-3 and IL-6 might be essential predictors of postmenopausal osteoporosis and can help predict osteoporotic fracture.

Nutrients, Body Composition and Exercise Are Related to Change in Bone Mineral Density in Premenopausal Women

This study determined relationships among total energy intake, nutrient intake, body composition, exercise group status, and annual rates of change (slopes) in bone mineral density in 66 Caucasian premenopausal women (mean age, 34.4 ± 2.7) taking calcium supplements. Body composition components measured by dual-energy X-ray absorptiometry included fat mass, soft tissue lean mass, and bone mineral density (g/cm2) of total body, spine (lumbar vertebrae 2–4), and three femur sites measured at baseline, 5, 12, and 18 mo. Nutrients were not significant variables in regression models predicting bone mineral density slopes (rates of change) at any femur site. The only significant variable in models predicting Ward's triangle bone mineral density slope was the initial fat mass and, for trochanter, exercise. Significant variables (P < 0.05) in models predicting total body bone mineral density slope included the initial fat mass and fat mass slope plus either vitamin A, carotene, fiber, magnesium, or phosphorus (R2 from 0.31 to 0.25) and fat mass slope plus sodium (R2 = 0.24). The significant variable in the model predicting L2–4 slope was energy intake (R2 = 0.17, P < 0.05). We conclude that nutrient intake, exercise, and body composition are related to bone mineral density rate of change and that relations among these variables vary by bone site.