Detection Of Osteoporosis Research Articles

RISK ASSESSMENT FOR OSTEOPOROSIS IN POSTMENOPAUSAL WOMEN

PURPOSE: Risk factors for osteoporotic fractures include parental history of hip fractures, smoking, excessive alcohol consumption, low body weight and menopause. Postmenopausal women under 65 years with at least one risk factor should be evaluated with a risk assessment tool to decide if they need to undergo osteodensitometry. There is an evidence of a significantly greater sensitivity of the SCORE test for detection of osteoporosis in women with age between 50 to 64 years compared to the FRAX tool. METHODS: We conducted a survey to evaluate the risk of osteoporosis in 64 postmenopausal women using SCORE tool. The assessed women were with mean age of 50 years. A total score of more than 6 points is considered to determine a possible risk of osteoporosis. RESULTS: 50% of the women showed moderate risk and 25% of the women had very high risk for osteoporosis. 75% of all evaluated subjects were detected to need bone mineral density (BMD) assessment with a dual-energy X-ray absorptiometry (DXA). CONCLUSIONS: The results of the current study are evidence of the high sensitivity of the SCORE tool and of the significant prevalence of osteoporosis among Bulgarian postmenopausal women.

Circulating microRNAs in serum as novel biomarkers for osteoporosis: a case-control study.

Aims: Osteoporosis is underdiagnosed because of the lack of a convenient diagnostic method. Circulating microRNAs (miRNAs) emerge as novel biomarkers for disease diagnosis. Here, we conducted a case-control study that included a total of 448 serum samples collected from 182 healthy participants, 132 osteopenia participants, and 134 osteoporosis patients. Methods: Circulating miRNAs dysregulated during osteoporosis were screened and analyzed in three randomly determined sub-cohorts: the discovery cohort identified 22 candidate miRNAs; the training cohort tested the candidate miRNAs and constructed Index 1, comprising five miRNAs by logistic regression, and Index 2, comprising four miRNAs, was developed by linear combination. Results: Both indices were tested in the validation cohort and showed statistically significant results in distinguishing osteoporosis patients from healthy and osteopenic patients. Moreover, Index 1 also showed improved performance over traditional bone turnover biomarkers type I pro-collagen (tPINP) and type I collagen (β-CTx). Conclusion: In conclusion, circulating miRNAs are potential biomarkers for osteoporosis. The diagnostic panel of circulating miRNAs could be a complementary method for dual-energy X-ray absorptiometry (DXA) in mass screening and routine examination to enhance the osteoporosis detection rate.

A comprehensive study on feature types for osteoporosis classification in dental panoramic radiographs

Background and ObjectiveOsteoporosis is a disease characterized by a decrease in bone density. It is often associated with fractures and severe pain. Previous studies have shown a high correlation between the density of the bone in the hip and in the mandibular bone in the jaw. This suggests that dental radiographs might be useful for detecting osteoporosis. Use of dental radiographs for this purpose would simplify early detection of osteoporosis. However, dental radiographs are not normally examined by radiologists. This paper explores the use of 13 different feature extractors for detection of reduced bone density in dental radiographs. MethodsThe computed feature vectors are then processed with a Self-Organizing Map and Learning Vector Quantization as well as Support Vector Machines to produce a set of 26 predictive models. ResultsThe results show that the models based on Self-Organizing Map and Learning Vector Quantization using Gabor Filter, Edge Orientation Histogram, Haar Wavelet, and Steerable Filter feature extractors outperform the rest of the 22 models in detecting osteoporosis. The proposed Gabor-based algorithm achieved an accuracy of 92.6%, a sensitivity of 97.1%, and a specificity of 86.4%. ConclusionsThe oriented edges and textures in the upper and lower jaw regions are useful for distinguishing normal patients from patients with osteoporosis.

Osteoporosis Detection Using Combined Texture Features of Proximal Femur Radiographs

Osteoporosis Detection Using Combined Texture Features of Proximal Femur Radiographs

Opportunistic Computed Tomography and Spine Surgery: A Narrative Review.

Study Design:Narrative review.Objective:This article seeks to provide a narrative review regarding the ability of opportunistic information available from computed tomography (CT) scans to guide decisions in spine surgery related to patient bone quality.Methods:A review of the literature (limited to human and English language) was performed via PubMed and Google Scholar using the search terms; “osteoporosis” AND “opportunistic” AND “computed tomography” AND “spine surgery.” The titles and then abstracts of all identified citations were reviewed for inclusion by 2 of the authors (MS, BAF). Relevant articles were then studied in full text.Results:A review of the literature found 25 articles that were selected for inclusion in this narrative review. These articles were broadly divided into 4 subcategories: (1) opportunistic CT (oCT) and osteoporosis detection, (2) oCT data and the quality of screw fixation, (3) utilization of Hounsfield units to assess clinical and/or radiographic outcomes following spine fusion, and (4) virtual stress testing in spine surgery.Conclusion:The literature on oCT, as well as associated virtual stress-testing techniques, demonstrate the potential to enhance spine surgery outcomes by preoperatively identifying at-risk patients in need of bone health optimization and informing best techniques for performing spinal fusion surgery on patients with diminished bone quality. While our narrative summary of the limited literature to date suggests a promising future for oCT data, significant additional research and/or radiographic workflow standardization is needed to validate these methods for clinical use.

The health and economic burden of osteoporotic fractures in Singapore and the potential impact of increasing treatment rates through more pharmacological options.

This study aims to estimate the health and economic burden of osteoporosis in Singapore from 2017 to 2035, and to quantify the impact of increasing the treatment rate of osteoporosis. Population forecast data of women and men aged 50 and above in Singapore from 2017 to 2035 was used along with prevalence rates of osteoporosis to project the osteoporosis population over time. The population projections by sex and age group were used along with osteoporotic fracture incidence rates by fracture type (hip, vertebral, other), and average direct and indirect costs per case to forecast the number of fractures, the total direct health care costs, and the total indirect costs due to fractures in Singapore. Data on treatment rates and effects were used to model the health and economic impact of increasing treatment rate of osteoporosis, using different hypothetical levels. Between 2017 and 2035, the incidence of osteoporotic fractures is projected to increase from 15,267 to 24,104 (a 57.9% increase) F 10,717 to 17,225 (a 60.7% increase) and M 4550 to 6878 (a 51.2% increase). The total economic burden (including direct costs and indirect costs to society) associated with these fractures is estimated at S$183.5 million in 2017 and is forecasted to grow to S$289.6 million by 2035. However, increasing the treatment rate for osteoporosis could avert up to 29,096 fractures over the forecast period (2017-2035), generating cumulative total cost savings of up to S$330.6 million. Efforts to improve the detection, diagnosis, and treatment of osteoporosis are necessary to reduce the growing clinical, economic, and societal burden of fractures in Singapore.

Serum Proteomic Analysis Reveals Vitamin D-Binding Protein (VDBP) as a Potential Biomarker for Low Bone Mineral Density in Mexican Postmenopausal Women.

Osteoporosis is a skeletal disease mainly affecting women over 50 years old and it represents a serious public health problem because of the high socioeconomic burden. This disease is characterized by deterioration of bone microarchitecture, low bone mineral density (BMD), and increased risk of fragility fractures. This study aimed to identify serum useful proteins as biomarkers for the diagnosis and/or prognosis of osteoporosis and fracture risk. We collected 446 serum samples from postmenopausal women aged ≥45 years old. Based on the BMD measurement, we classified the participants into three groups: osteoporotic, osteopenic, and normal. In an initial discovery stage, we conducted a proteomic approach using two-dimensional differential gel electrophoresis (2D-DIGE). The peptides into the spots of interest were identified through matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF/TOF). Enzyme-linked immunosorbent assay (ELISA) was performed to validate the proteins of interest. We identified 27 spots of interest when comparing low BMD versus normal BMD postmenopausal women. Based on their relevance in bone metabolism, we analyzed three proteins: ceruloplasmin (CP), gelsolin (GSN), and vitamin D-binding protein (VDBP). Our results demonstrated that low serum VDBP levels correlate with low BMD (osteopenic and osteoporotic). Therefore, VDBP could be considered as a novel, potential, and non-invasive biomarker for the early detection of osteoporosis.

Assessment of BMD and Statistical Analysis for Osteoporosis Detection

Biomedical engineering is one of the promising disciplines in engineering that deals with technology advancement in human health. Osteoporosis is a common metabolic disease categorized by decreased bone mass and increased liability to fractures. Bone densitometry is a broad term comprising the art and science of measuring the bone mineral content (BMC) and bone mineral density (BMD) of particular skeletal sites or the whole body. There are various methods to measure bone mineral density which differs based on the differential absorption of ionizing radiation or the sound waves. The methods are SPA (Single Photon Absorptiometry), DPA (Dual Photon Absorptiometry), SEXA (Single Energy X ray Absorptiometry), DEXA (Dual Energy X ray Absorptiometry), QCT (Quantitative Computed Tomography), QUS (Quantitative Ultra Sound) and RA (Radiographic absorptiometry). The DEXA test can measure the whole body but usually the lower spine and hips. A major disadvantage of DEXA is that currently there is a lack of standardization in bone and soft tissue measurements. Furthermore, for a given manufacturer, results may vary by the model of the instrument, the mode of operation or the version of the software used to analyze the data. In addition to that, DEXA scan images are only for the confirmation of correct positioning of the patient and correct placement of the regions of interest (ROI). Motivated by the above issues, this paper can pave a way for analysis in the measurement of BMD, measurement of T-score, and Z-score from the DEXA scan images. This proposed methodology includes segmentation algorithms such as k means clustering & mean –shift algorithm and comparison of the accuracy of algorithms. Also in addition, a novel mathematical analysis is also proposed to measure the T–score values in DEXA images with a new parameter ‘S’ from BMD values in order to detect the osteoporosis condition accurately.

Association between true non-contrast and virtual non-contrast vertebral bone CT attenuation values determined using dual-layer spectral detector CT

PurposeTo investigate the association of vertebral CT attenuation between virtual non-contrast (VNC) and true non-contrast (TNC) images and to evaluate if VNC vertebral CT attenuation could be used for phantom-less osteoporosis detection in dual-layer spectral-detector CT (SDCT). Methods200 patients with non-contrast and portal-venous phase SDCT were retrospectively assigned to a test and a validation group of 100 patients each. CT attenuation of L1 vertebrae were measured on VNC and TNC. The test group was used to determine the difference between VNC and TNC CT attenuation and to calculate a statistical model for TNC CT attenuation prediction. The validation group was used to assess the capability of the model to predict TNC from VNC CT attenuation and its accuracy to identify osteoporosis. Osteoporosis was defined as a TNC CT attenuation of ≤110HU. ResultsIn both groups, CT attenuation was lower in VNC than in TNC (P < 0.001). VNC and TNC CT attenuation was correlated strongly (r = 0.958). Using the regression equation established in the test group (TNC = 23.677 + 1.540 × VNC), the predicted TNC CT attenuation did not differ from the real TNC CT attenuation in the validation group (P = 0.359). A VNC CT attenuation cut-off of 52HU yielded an AUC of 0.978 for osteoporosis detection. ConclusionsL1 CT attenuation is systematically underestimated in VNC compared with TNC images. However, TNC L1 CT attenuation can be predicted reliably from VNC. VNC may perform well in phantom-less osteoporosis detection.

Role of lumbar spine signal intensity measurement by MRI in the diagnosis of osteoporosis in post-menopausal women

BackgroundOsteoporosis is a worldwide health problem and a common cause of bone fractures; the most common type of osteoporosis is post-menopausal type. MRI has a role in the diagnosis of osteoporosis and can be used as a screening tool, so the purpose of our study was to define a quantitative MRI-based score (M-score) for the detection of lumbar spine osteoporosis and to evaluate the correlation between lumbar spine signal intensity measured by MRI and BMD (bone mineral density) in post-menopausal women. Our case-control study involved 100 cases (50 old post-menopausal females as a case group and 50 healthy females as a control group of matched age). Both groups were subjected to history taking, dual-energy X-ray absorptiometry (DEXA), and conventional lumbar MRI. DEXA was performed for the lumbar spine and all scores (T-score, Z-score, BMD) were calculated. Lumbar MRI was performed (sagittal T1WI and T2WI) from L1–L4 levels. SNRL1–L4 and M-score were calculated from T1W images.ResultsAll DEXA scores were significantly lower in post-menopausal females compared to the control group (P < 0.0001). Meanwhile, SNRL1–L4 and M-score were significantly higher among cases than controls (P < 0.0001). The diagnostic threshold of SNRL1–L4 and M-score for distinguishing osteoporotic from non-osteoporotic females was 104.5 for SNRL1–L4 with a sensitivity of 94%, specificity 60%, positive predictive values (PPV) 31%, and negative predictive values (NPV) 98%, and 3.5 for M-score with a sensitivity of 93.3%, specificity 83.5%, PPV 50%, and NPV 98.6%. SNRL1–L4 was negatively correlated with M-score (r = − 0.74, P < 0.0001), T-score (r = − 0.42, P < 0.0001), Z-score (r = − 0.32, P < 0.0001), and BMD (r = − 0.31, P < 0.0001). M-score was negatively correlated with T-score (r = − 0.48, P < 0.0001), Z-score (r = − 0.36, P < 0.0001), and BMD (r = − 0.37, P < 0.0001). M-score moderately agreed with T-score (κ = 0.5, P < 0.0001).ConclusionThe MRI-based score (M-score) of the lumbar spine is an accurate quantitative method for distinguishing osteoporotic from non-osteoporotic females. M-score and SNRL1–L4 are significantly correlated with osteoporotic indices (T-score and BMD).

Bayesian inference of a human bone and biomaterials using ultrasonic transmitted signals.

Ultrasonic techniques could be good candidates to aid the assessment of osteoporosis detection, due to their non-intrusiveness and low cost. While earlier studies made use of the measured ultrasonic phase velocity and attenuation inside the bone, very few have considered an inverse identification of both the intrinsic pore microstructure and the mechanical properties of the bone, based on Biot's model. The main purpose of this work is to present an in vitro methodology for bone identification, adopting a statistical Bayesian inference technique using ultrasonic transmitted signals, which allows the retrieval of the identified parameters and their uncertainty. In addition to the bone density, Young's modulus and Poisson's ratio, the bone pore microstructure parameters (porosity, tortuosity, and viscous length) are identified. These additional microstructural terms could improve the knowledge on the correlations between bone microstructure and bone diseases, since they provide more information on the trabecular structure. In general, the exact properties of the saturating fluid are unknown (bone marrow and blood in the case of bone study) so in this work, the fluid properties (water) are identified during the inference as a proof of concept.

Unknown osteoporosis in older patients admitted to post-acute rehabilitation.

Osteoporotic fractures are associated with increased morbidity, mortality, and increased health care use. As the number of older adults increases, identifying those at increased risk for osteoporotic fractures has become of utmost importance to providing them with preventive and therapeutic interventions. To determine the prevalence of unknown clinical and densitometric osteoporosis and to investigate the performance of different diagnostic strategies for osteoporosis in elderly patients admitted to rehabilitation. This is an observational study. Eligible participants were older adults admitted to rehabilitation in an academic hospital in Switzerland over an 11-month period. Patients with previously unknown osteoporosis underwent dual-energy X-ray absorptiometry (DXA), vertebral fracture assessment (VFA), and history review for past fractures. Complete assessment was available for 252 patients. Previously undiagnosed osteoporosis was identified in 62.3% of these patients, a proportion that was higher among women (71.5%) than men (44.8%). DXA proved most sensitive, followed by VFA and history review. Results differed across gender: DXA remained the most sensitive single test among women, but VFA proved most sensitive in men. The best test to combine with history review was DXA in women (detection increasing from 47.5 to 93.2%) and VFA in men (detection increasing from 35.9 to 84.6%). Prevalence of previously unknown osteoporosis appears very high in elderly patients admitted to post-acute rehabilitation. The combination of history review of previous fractures with DXA in women and with VFA in men appears the best two-step strategy to improving detection of osteoporosis in this population.

UTILITY OF CONE BEAM COMPUTED TOMOGRAPHY OF THE MANDIBLE IN DETECTION OF OSTEOPOROSIS IN POSTMENOPAUSAL WOMEN

INTRODUCTION: Osteoporosis is a systemic disease characterized by decreased bone mass and increased bone fragility. Dental radiographsare widely used in the adult population especially Cone Beam Computed Tomography (CBCT) that is used in oral and maxillofacial surgeryfor numerous clinical applications. Therefore, dental radiographs may offer an opportunity as a screening tool for the early detection ofosteoporosis and subsequent referral.OBJECTIVES: The aim of this study was to evaluate the diagnostic efficacy of mandibular CBCT images for the detection of osteoporosisin postmenopausal women and to correlate it to Dual energy x-ray absorptiometry (DEXA) scan results.MATERIALS AND METHODS: twenty four postmenopausal female patients were recruited in this study. Based on their DEXA results,they were classified into osteoporotic and control non-osteoporotic groups. CBCT examination was done as part of surgical treatment. Coronalimages were used to measure the computed tomography mandibular index (CTMI) and the computed tomography index (superior) (CTIS).Thecorrelation between these indices and bone mineral density (BMD) measured by DEXA were assessed.RESULTS: There were significant differences between the control and osteoporotic groups in the computed tomography mandibular indexsuperior CTI(S), and computed tomography mental index (CTMI: inferior cortical width).There was significant positive correlation betweenCTMI and T-score measured by DEXA.CONCLUSIONS: CTI(S) and CTMI on the CBCT images can be used to assess the osteoporotic women.

Using texture analysis of head CT images to differentiate osteoporosis from normal bone density

ObjectivesTo investigate the use of texture analysis for the detection of osteoporosis on noncontrast head CTs, and to explore optimal sampling regions within the craniofacial bones. MethodsIn this IRB-approved, retrospective study, the clivus, bilateral sphenoid triangles and mandibular condyles were manually segmented on each noncontrast head CT, and 41 textures features were extracted from 29 patients with normal bone density (NBD); and 29 patients with osteoporosis. Basic descriptive statistics including a false discovery rate correction were performed to evaluate for differences in texture features between the cohorts. ResulsSixteen texture features demonstrated significant differences (P < 0.01) between NBD and osteoporosis in the clivus including 4 histogram features, 2 gray-level co-occurrence matrix features, 8 gray-level run-length features and 2 Law’s features. Nineteen texture features including 9 histogram features, 1 GLCM features, 2 GLRL features, 5 Law’s features and 2 GLGM features demonstrated statistically significant differences in both sides of the sphenoid triangles. A total 24 texture features demonstrated statistically significant differences between normal BMD and osteoporosis in the left sphenoid and a total of 31 texture features in the left condyle. Furthermore, a total of 22 texture features including 6 histogram features, 3 GLCM features, 9 GLRL features, 2 Law’s features and 2 GLGM features demonstrated statistically significant differences in both sides of the mandibular condyles. ConclusionThe results of this investigation suggest that specific texture analysis features derived from regions of interest placed within multiple sites within the skull base and maxillofacial bones can distinguish between patients with normal bone mineral density compared to those with osteoporosis. This study demonstrates the potential utility of a texture analysis for identification of osteoporosis on head CT, which may help identify patients who have not undergone screening with traditional DXA.

Comparison of laboratory and imaging methods associated with bone metabolism in patients with or without renal failure under the age of 45 years with elevated parathyroid hormone levels

Objectives: Although bone biopsy is considered the gold standard for the definitive diagnosis of renal osteodystrophy; it is not suitable for routine clinical practice due to its invasive nature. The present study was aimed to evaluate and compare the bone mineral status using dual energy X-ray absorptiometry of patients with or without chronic kidney disease in young population with elevated parathyroid hormone levels. Methods: This was a single center, cross-sectional, retrospective study conducted in patients younger than 45 years of age. The study was performed in the outpatient clinic of a university hospital. Patients with elevated parathyroid hormone levels were included. Results: Among them, 29 had renal insufficiency, 158 had normal renal function. Measured bone mineral density with dual energy X-ray absorptiometry and laboratory values were collected from patient files. The primary end point was to assess the efficiacy of dual energy X-ray absorptiometry in patients with or without renal failure. Except Z score at Ward’s triangle, all of the T and Z scores at lomber, femur neck, trochanteric, and intertrochanteric areas were found significantly lower in patients with chronic kidney disease compared to those without (p &amp;lt; 0.001). Conclusion: Dual energy X-ray absorptiometry seemed to be a reliable method for detection of osteoporosis in premenopausal female and male patients younger than 45 years of age with or without renal failure with elevated parathyroid hormone levels.

A computer aided diagnosis system for measurement of mandibular cortical thickness on dental panoramic radiographs in prediction of women with low bone mineral density.

Osteoporosis detection at earlier stages can enhance the life span of an elderly individual. The aim of the study is to perform semi-automated measurement of mandibular cortical thickness (MCT) on a dental panoramic radiograph (DPR) and thereby to predict the risk of low BMD among the studied population. The study involved 76 women (mean age: 57.2 ± 12.6years). The DPR was obtained using KODAK 8000C system. The BMD of right total hip (T-BMD) was obtained using DPX Prodigy Dual-energy X-ray absorptiometry (DXA) Scanner. The DPR obtained were subjected to image processing techniques to perform MCT measurement. The region of interest was manually selected around the mental foramen and enhanced using a median filter. The Ostu segmentation was performed and connected component labelling operation was performed to determine the lower boundary by finding the contour with maximum area. Subsequently, the haar wavelet operation was carried out to find the magnitude and thereby select the upper delineating cortical boundary. The Pearson test results revealed (r = 0.96, p < 0.01) for the standard (manual) MCT measurement against the MCT measured using the proposed semi-automated scheme. ROC analysis revealed that MCT = 2.5mm could be an optimal threshold in spotting individuals at risk of low BMD. The results of the study revealed that the MCT measured on a DPR using the proposed approach could be helpful for identifying individuals at risk of low BMD.

Simulating Dual-Energy X-Ray Absorptiometry in CT Using Deep-Learning Segmentation Cascade

PurposeOsteoporosis is an underdiagnosed condition despite effective screening modalities. Dual-energy x-ray absorptiometry (DEXA) screening, although recommended in clinical guidelines, remains markedly underutilized. In contrast to DEXA, CT utilization is high and presents a valuable data source for opportunistic osteoporosis screening. The purpose of this study was to describe a method to simulate lumbar DEXA scores from routinely acquired CT studies using a machine-learning algorithm. MethodsBetween January 2010 and September 2014, 610 CT studies of the abdomen and pelvis were used to develop spinal column and L1 to L4 multiclass segmentation. DEXA simulation training and validation used 1,843 pairs of CT studies accompanied by DEXA results obtained within a 6-month interval from the same individual. Machine learning–based regression was used to determine correlation between calculated grade (on the basis of vertebrae L1-L4) and DEXA t score. ResultsAnalysis of the t score equivalent, generated by the algorithm, revealed true positives in 1,144 patients, false positives in 92 patients, true negatives in 245 patients, and false negatives in 212 patients, resulting in an accuracy of 82%. Sensitivity for the detection of osteoporosis or osteopenia was 84.4% (95% confidence interval, 82.3%-86.2%), and specificity was 72.7% (95% confidence interval, 67.7%-77.2%). ConclusionsThe presented algorithm can identify osteoporosis and osteopenia with a high degree of accuracy (82%) and a small proportion of false positives. Efforts to cull greater information using machine-learning algorithms from pre-existing data have the potential to have a marked impact on population health efforts such as bone mineral density screening for osteoporosis, in which gaps in screening currently exist.

A new biosensor for osteoporosis detection

Osteoporosis is a disease that is characterized by deterioration of bone tissue and increased risk of fracture as it leads to a decrease in bone mineral density, which is an important public health problem. Today, bone mineral density is measured by radiological techniques. Alternative techniques are needed because of the disadvantages such as excessive radiation intake, the cost of radiological techniques, and the necessity for specialist personnel for the devices. The quantitative determination of biochemical markers that play a role in bone mineralization may be a good alternative for the osteoporosis diagnosis and especially in the follow-up of treatment.In this study, a specific and sensitive immunological biosensor, which quantitatively determines the osteocalcin molecule, has been developed to be used in the early osteoporosis diagnosis and to evaluate the response to the drug treatment. Anti-osteocalcin antibody was immobilized onto gold electrode surface via covalent immobilization method by using 6-mercaptohexanol, 1,4-butanedioldiglycidyl ether, ethanolamine, and glutaraldehyde. Immobilization steps and biosensor characterization were specified by cyclic voltammetry and electrochemical impedance spectroscopy. The detection time and range of Ocn biosensor were determined as 45 min and 10–60 pg µL−1 Ocn concentration, respectively. The Ocn biosensor was successfully applied in artificial serum samples spiked with Ocn.

Computer-aided osteoporosis detection from DXA imaging

Background and objectiveOsteoporosis is a skeletal disease caused by a high rate of bone tissue loss, and it is a major cause of bone fracture. In contemporary society, osteoporosis is more common than cancer and stroke and results in a higher rate of morbidity and mortality in the human population. Osteoporosis can conclusively be diagnosed with dual energy X-ray absorptiometry (DXA). In this study, we propose a computer-aided osteoporosis detection (CAOD) technique that automatically measures bone mineral density (BMD) and generates an osteoporosis report from a DXA scan. MethodsThe CAOD model denoise and segments DXA images using a non-local mean filter, Machine learning pixel label random forest respectively, and locates regions of interest with higher accuracy. Pixel label random forest classifies a pixel either bone or soft tissue; then contours are extracted from binary image to locate regions of interest and calculate BMD from bone and soft tissues pixels. Mean standard deviation and correlation coefficients statistical analysis were used to evaluate the consistency and accuracy of BMD measurements. ResultsDuring a consistency test of BMD measurements using three consecutive scans from Computerized Imaging Reference Systems’ Bona Fide Phantom (CIRS-BFP) for the spine, the CAOD model showed an averaged standard deviation of 0.0029 while the standard deviation from manual measurements on the same data set by three different individuals was recorded as 0.1199. During another correlation study of BMD measurements evaluating real human scan images by the CAOD model versus manual measurement, the model scored a correlation coefficient of R2 = 0.9901 while the CIRS-BFP study scored a correlation coefficient of R2 = 0.9709. ConclusionsThe CAOD model increases the preciseness and accuracy of BMD measurements. This CAOD method will help clinicians, untrained DXA operators, and researchers (medical scientists, doctors, and bone researchers) use the DXA system with reliable accuracy and overcome workload challenges. It will also improve osteoporosis diagnosis from DXA systems and increase system performance and value.

Sclerostin, periostin and microRNA as potential markers of osteoporosis

In recent years, people have had high hopes for the possibility of using measurements of the so-called modern bone turnover markers in diagnosis, diagnostics of the course and the prediction of the occurrence of complications of bone metabolic diseases, especially osteoporosis. So far, several diagnostic markers of bone turnover have been identified, particular importance attached to PINP and CTx. However, many studies have highlighted that their use in the diagnosis of osteoporosis is rather limited due to their inter-individual and intra-individual variability, tissue non-specificity, the inability to evaluate individual bone tissue compartments, and the ability to assess metabolic activity of only osteoblasts and/or osteoclasts without osteocytes. The data presented in the thesis indicates the potential usefulness of sclerostin measurements, primarily associated with metabolic activity of osteocytes, periostin, as the indicator of metabolic activity of periosteum and microRNAs as the group of specific markers in the early detection of osteoporosis. All of the above-mentioned candidates could possibly become a predictor of osteoporotic fractures: sclerostin in patients with type 2 diabetes; periostin, especially in non-vertebral fractures in postmenopausal women and microRNA in early prediction of these fractures. However, many of the studies underline some of the limitations associated with their use in the diagnosis of osteoporosis. Ambiguous results of the presented studies may be primarily due to population selection, population size, type of fractures and duration of the observation itself. An objective judgement will be made when an adequate number of standardized and independent studies will allow for the exclusion or validation of each of them in clinical practice.