Classification Of Density Research Articles

Percepção da sociedade sobre a qualidade de vida e o controle populacional de cães não domiciliados

In most Brazilian municipalities there is an overpopulation of stray dogs, which causes problems tothe urban order, the environment and the public health, in addition to mistreatment to these dogs.In such context we foresee the need of developing actions targeting the population control of theseanimals. This essay aims at knowing the perception of social actors, such as managers of entitiesresponsible for control actions, managers of NGOs working with animal protection and population ingeneral with respect to the life quality and population control of stray dogs. Questionnaires were usedon samples of individuals of these three groups and the data thereof were analyzed with descriptivestatistics techniques and frequency comparison. The results allowed us to conclude that the society’sperception of population control and life quality of these animals bear important differences under theviewpoint of the three evaluated groups; however, they also bear significant similarities, especiallywith respect to the perception of the responsibility for the development of population control actions,the acceptance of using public funds intended to public health in control actions, the classification of such population density as large and the poor life quality of these animals. Keywords: population control; social perception; stray dog.

Polarimetric Classification of C-Band SAR Data for forest Density Characterization

Polarimetric classification is one of the most significant applications of synthetic aperture radar (SAR) remote sensing. Sensitivity of C-band SAR in discerning the variation in canopy roughness and limited penetration capability through forest canopy have been well studied at a given frequency, polarization and incidence angle. However, the scope of C-band SAR in characterizing and monitoring forest density has not been adequately understood with polarimetric techniques. The objectives of the present study were to understand the scattering behaviour of different landcover classes and evaluate the feasibility of polarimetric SAR data classification methods in forest canopy density slicing using C-band SAR data. The RADARSAT- 2 image with fine quad-pol obtained on 27 October 2011 over Madhav National Park, Madhya Pradesh, India and its surroundings was used for the analysis. Forest patches exhibit α-angle around 45°, which means the dominant scattering mechanism is volume; entropy of one or a value close to it denotes distributed targets and low anisotropy values than all other land units, which shows a dominant first scattering mechanism. This study comparatively analysed Wishart supervized classifier and Support Vector Machine (SVM) classifier for classification of the forest canopy density along with other associated land-cover classes for a better understanding of the class separability. All forest density classes showed comparatively good separability in Wishart supervized classification (73.8-84.7%) and in SVM classifier (82.3-84.8%). The results demonstrate the effectiveness of SVM classifier (88.7%) over Wishart supervized classifier (87.8%) with kappa coefficient of 0.86 and 0.85 respectively. The experimental results obtained with polarimetric C-band SAR data over dry deciduous forest area imply that SAR data have a significant potential for estimating stand density in operational forestry.

Assessment of Wind Energy Potential for Small Scale Power Generation at Thatta, Sindh, Pakistan

In this paper, the wind characteristics and wind Power potential for south coast of Thatta, Sindh province, Pakistan are presented. The variation of monthly wind speed at the height of 10m, 20m, 30m, 40m, and 50m are presented. The power density for these heights are calculated employing coefficient of performance Cp as 0.40 and 0.45. Season wise classification of wind speed and power density at these heights indicates a fairly reasonable prospect of wind energy utilization for small scale power generation.

Automated quantification of 18F-flutemetamol PET activity for categorizing scans as negative or positive for brain amyloid: concordance with visual image reads.

Clinical trials of the PET amyloid imaging agent (18)F-flutemetamol have used visual assessment to classify PET scans as negative or positive for brain amyloid. However, quantification provides additional information about regional and global tracer uptake and may have utility for image assessment over time and across different centers. Using postmortem brain neuritic plaque density data as a truth standard to derive a standardized uptake value ratio (SUVR) threshold, we assessed a fully automated quantification method comparing visual and quantitative scan categorizations. We also compared the histopathology-derived SUVR threshold with one derived from healthy controls. Data from 345 consenting subjects enrolled in 8 prior clinical trials of (18)F-flutemetamol injection were used. We grouped subjects into 3 cohorts: an autopsy cohort (n = 68) comprising terminally ill patients with postmortem confirmation of brain amyloid status; a test cohort (n = 172) comprising 33 patients with clinically probable Alzheimer disease, 80 patients with mild cognitive impairment, and 59 healthy volunteers; and a healthy cohort of 105 volunteers, used to define a reference range for SUVR. Visual image categorizations for comparison were from a previous study. A fully automated PET-only quantification method was used to compute regional neocortical SUVRs that were combined into a single composite SUVR. An SUVR threshold for classifying scans as positive or negative was derived by ranking the PET scans from the autopsy cohort based on their composite SUVR and comparing data with the standard of truth based on postmortem brain amyloid status for subjects in the autopsy cohort. The derived threshold was used to categorize the 172 scans in the test cohort as negative or positive, and results were compared with categorization using visual assessment. Different reference and composite region definitions were assessed. Threshold levels were also compared with corresponding thresholds derived from the healthy group. Automated quantification (using pons as the reference region) demonstrated 91% sensitivity and 88% specificity and gave 3 false-positive and 4 false-negative scans. All 3 false-positive cases were either borderline-normal by standard of truth or had moderate to heavy cortical diffuse plaque burden. In the test cohort, the concordance between quantitative and visual read categorization ranged from 97.1% to 99.4% depending on the selection of reference and composite regions. The threshold derived from the healthy group was close to the histopathology-derived threshold. Categorization of (18)F-flutemetamol amyloid imaging data using an automated PET-only quantification method showed good agreement with histopathologic classification of neuritic plaque density and a strong concordance with visual read results.

Content Based Mammogram Retrieval based on Breast Tissue Characterization using Statistical Features

The aim of the study is to retrieve the similar mammographic images based on the type of breast tissue density of the given query image. Statistical descriptors were extracted from the candidate blocks of the breast parenchyma. The mean of extracted features are fed into the SVM classifier for classification of the tissue density into any of the three classes namely dense, glandular and fatty and the classification accuracy obtained is 91.54%. After classification the mammogram images along with its feature vector are stored into three separate databases based on tissue type. Then K-means clustering algorithm is used to divide each database into 2 clusters. For content based retrieval of the mammograms based on the given query image, first the query image is classified into any of the three tissue class. Then the feature vector of the query image is compared with the two cluster centroids of the corresponding class, so as to confine the search within the closest cluster. Top 5 similar images are retrieved from its corresponding class database. Euclidean distance based k-NN is used for mammogram retrieval and this study obtained the highest precision rate ranging between 98 and 99%.

CFS-SMO based classification of breast density using multiple texture models.

It is highly acknowledged in the medical profession that density of breast tissue is a major cause for the growth of breast cancer. Increased breast density was found to be linked with an increased risk of breast cancer growth, as high density makes it difficult for radiologists to see an abnormality which leads to false negative results. Therefore, there is need for the development of highly efficient techniques for breast tissue classification based on density. This paper presents a hybrid scheme for classification of fatty and dense mammograms using correlation-based feature selection (CFS) and sequential minimal optimization (SMO). In this work, texture analysis is done on a region of interest selected from the mammogram. Various texture models have been used to quantify the texture of parenchymal patterns of breast. To reduce the dimensionality and to identify the features which differentiate between breast tissue densities, CFS is used. Finally, classification is performed using SMO. The performance is evaluated using 322 images of mini-MIAS database. Highest accuracy of 96.46% is obtained for two-class problem (fatty and dense) using proposed approach. Performance of selected features by CFS is also evaluated by Naïve Bayes, Multilayer Perceptron, RBF Network, J48 and kNN classifier. The proposed CFS-SMO method outperforms all other classifiers giving a sensitivity of 100%. This makes it suitable to be taken as a second opinion in classifying breast tissue density.

Comparison of Mammography in Combination with Breast Ultrasonography Versus Mammography Alone for Breast Cancer Screening in Asymptomatic Women

To compare the agreement of screening breast mammography plus ultrasound and reviewed mammography alone in asymptomatic women. All breast imaging data were obtained for women who presented for routine medical checkup at National Cancer Institute (NCI), Thailand from January 2010 to June 2013. A radiologist performed masked interpretations of selected mammographic images retrieved from the computer imaging database. Previous mammography, ultrasound reports and clinical data were blinded before film re-interpretation. Kappa values were calculated to assess the agreement between BIRADS assessment category and BIRADS classification of density obtained from the mammography with ultrasound in imaging database and reviewed mammography alone. Regarding BIRADS assessment category, concordance between the two interpretations were good. Observed agreement was 96.1%. There was moderate agreement in which the Kappa value was 0.58% (95%CI; 0.45, 0.87). The agreement of BI-RADS classification of density was substantial, with a Kappa value of 0.60 (95%CI; 0.54, 0.66). Different results were obtained when a subgroup of patients aged ≥60 years were analyzed. In women in this group, observed agreement was 97.6%. There was also substantial agreement in which the Kappa value was 0.74% (95%CI; 0.49, 0.98). The present study revealed that concordance between mammography plus ultrasound and reviewed mammography alone in asymptomatic women is good. However, there is just moderate agreement which can be enhanced if age- targeted breast imaging is performed. Substantial agreement can be achieved in women aged ≥60. Adjunctive breast ultrasound is less important in women in this group.

Correlation between the Bone Density Recorded by a Computerized Implant Motor and by a Histomorphometric Analysis: A Preliminary In Vitro Study on Bovine Ribs

The purpose of the present preliminary in vitro study on bovine ribs was to validate a new intraoperative site-specific classification of bone Density Index (IDI), obtained by an innovative computerized implant motor, by correlating these data with the data obtained by the histomorphometrical evaluation of the same samples. Five segments of bovine ribs were used, and a total of 22 perforations were performed. A computerized implant motor ("Torque Measuring Motor") was used to evaluate the bone density, which was classified into four classes: ID1, ID2, ID3, and ID4. Histomorphometrical analysis of bone density, expressed as percentage of bony trabeculae over the total biopsy area, was also performed. The data of bone density obtained by the implant motor were statistically correlated with the histomorphometrical results. A significant positive correlation was found between the bone density measured by the implant motor and the bone density assessed by histomorphometry (r = 0.89, p < .0001). Moreover, a significant positive correlation in D1, D2, and D4, whereas a negative, not significant correlation in D3 was found. Within the limitations of this in vitro study, the intraoperative site-specific classification of bone density, obtained with this innovative system, could be helpful for the clinician to tailor the surgical protocol to the different situations in implant dentistry.

Fully automated quantification of [18F]flutemetamol images used to categorize scans into either normal or abnormal amyloid levels: Sensitivity and specificity against histopathology and concordance with visual read results

[18 F]flutemetamol clinical trials have used visual assessment for dichotomous categorization of scans. However, quantification will provide additional information about level of tracer uptake and may also help enable consistency in image assessment over time and across different centers. In this study, we investigate the performance of a fully automated quantification method by calculating sensitivity and specificity against histopathology and by comparing scan categorization using quantification and visual evaluation, respectively. [18 F]Flutemetamol images from 10 prior studies were used. A total of 276 scans were used from 172 AD, MCI and healthy volunteer (HV) subjects, 36 normal pressure hydrocephalus (NPH) subjects, and 68 end-of-life subjects from an autopsy trial, where a histopathology standard of truth (SOT) was available. Five visual readers, trained with an electronic training program, categorized all scans. A fully automated PET-only, MNI-space, quantification method was used to compute SUVR values for a composite neocortical region using pons as reference. An SUVR threshold was derived by ranking the PET scans from the autopsy cohort based on the composite SUVR and comparing data with SOT, a dichotomous Bielschowsky silver stain assessment of neuritic plaques based on CERAD criteria. The derived threshold was used to categorize all 276 scans into normal and abnormal. For the 68 scans from end-of-life subjects, sensitivity and specificity against SOT were computed. For all 276 scans quantification results were compared to categorization using visual assessment. In the autopsy cohort, classification by quantification gave three false-positive and four false-negative scans, yielding 91% sensitivity and 88% specificity. All three false-positive cases were either borderline normal by SOT or had moderate to heavy cortical diffuse plaque burden. There was concordance between quantitative and visual read categorization for 268 of the 276 scans (97.1%). After excluding the autopsy scans, many of which were atypical with gross atrophy making both visual assessment and quantification difficult, quantitative and visual read categorization agreed in 206 of 208 remaining scans (99.0%). Categorization of [18 F]flutemetamol amyloid imaging data using an automated PET-only quantification method showed good agreement with histopathological classification of neuritic plaque density, and there was a strong concordance with visual read results.

Definizione della qualità ossea registrata nelle diverse aree anatomo-strutturali

ObjectivesThe purpose of this work is to provide a classification of bone density for mandibular and maxillary anatomical structural areas. A proper pre-implant diagnosis that takes this aspect into due account represents a prognostic factor of primary importance specifically with reference to immediate load procedures. Materials and methodsThe device used for this research allows a site-specific intraoperative evaluation of bone density. A total of 1254 implants were analyzed and the data pertaining to the detected bone type were reviewed in depth. Based on this research, a classification of the anatomical structural areas on a clinical statistical basis was suggested. Results and conclusionsKnowing the qualitative and quantitative availability of an anatomical structural area in advance allows us to face even complex implant-prosthesis rehabilitation with a diagnostic datum that has been difficult to extrapolate so far from investigation techniques but with a high prognostic value. The new classification suggested by the authors allows for a quicker and more intuitive selection of applicable devices.

AUTOMATED MODELING OF 3D BUILDING ROOFS USING IMAGE AND LIDAR DATA

Abstract. In this work, an automated approach for 3D building roof modelling is presented. The method consists of two main parts, namely roof detection and 3D geometric modelling. For the detection, a combined approach of four methods achieved the best results, using slope-based DSM filtering as well as classification of multispectral images, elevation data and vertical LiDAR point density. In the evaluation, the combination of the four methods yields 94% correct detection at an omission error of 12%. Roof modelling is done by plane detection with RANSAC, followed by geometric refinement and merging of neighbouring segments to clean up oversegmentation. Walls are then detected and excluded, and the roof shapes are vectorised with the alpha-shape method. The resulting polygons are refined using 3D straight edges reconstructed by automatic straight edge extraction and matching, as well as 3D corner points constructed by intersection of the 3D edges. The results are quantitatively assessed by comparing to ground truth manually extracted from high-quality images, using several metrics for both the correctness and completeness of the roof polygons and for their geometric accuracy. The median value of correctness of the roof polygons is calculated as 96%, while the median value of completeness is 88%.

Interobserver agreement in breast radiological density attribution according to BI-RADS quantitative classification

The authors sought to assess interobserver agreement in classifying mammography density according to quantitative Breast Imaging Reporting and Data System (BI-RADS) criteria. Six expert mammography readers were tested on a set of 100 mammograms. Interobserver agreement was determined according to the kappa statistic, adjusting for chance agreement, on a four-category (D1 vs. D2 vs. D3 vs. D4) or two-category (D1-2 vs. D3-4) basis. Agreement with a panel of 12 readers who had been tested on the same set in a previous study was also assessed. The six readers showed good agreement when compared in pairs [agreement on a four-category basis was substantial (kappa=0.60-0.80) for 13 pairs and almost perfect (kappa>0.80) for two pairs); agreement on a two-category basis was substantial for 12 pairs and almost perfect for three pairs) or compared with the panel (on a four-category basis, agreement was substantial for five of six readers and almost perfect for one; on a two-category basis, agreement was substantial for all readers). In agreement with previous studies, visual classification of mammography density according to BI-RADS quantitative criteria was highly reproducible among readers; nevertheless, attribution to the "dense breast" (BI-RADS D3-4) category, which might be adopted as a determinant of different screening protocols (such as adjunct ultrasonography or yearly interval) varied among readers (range 6-15%). Controlled studies should be performed comparing visual with computer-density category attribution, the latter possibly being a better alternative due to its absolute reproducibility.

Content-based image retrieval applied to BI-RADS tissue classification in screening mammography.

To present a content-based image retrieval (CBIR) system that supports the classification of breast tissue density and can be used in the processing chain to adapt parameters for lesion segmentation and classification. Breast density is characterized by image texture using singular value decomposition (SVD) and histograms. Pattern similarity is computed by a support vector machine (SVM) to separate the four BI-RADS tissue categories. The crucial number of remaining singular values is varied (SVD), and linear, radial, and polynomial kernels are investigated (SVM). The system is supported by a large reference database for training and evaluation. Experiments are based on 5-fold cross validation. Adopted from DDSM, MIAS, LLNL, and RWTH datasets, the reference database is composed of over 10 000 various mammograms with unified and reliable ground truth. An average precision of 82.14% is obtained using 25 singular values (SVD), polynomial kernel and the one-against-one (SVM). Breast density characterization using SVD allied with SVM for image retrieval enable the development of a CBIR system that can effectively aid radiologists in their diagnosis.

Automated assessment of breast tissue density in digital mammograms

Mammographic density is known to be an important indicator of breast cancer risk. Classification of mammographic density based on statistical features has been investigated previously. However, in those approaches the entire breast including the pectoral muscle has been processed to extract features. In this approach the region of interest is restricted to the breast tissue alone eliminating the artifacts, background and the pectoral muscle. The mammogram images used in this study are from the Mini-MIAS digital database. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: (1) preprocessing, (2) feature extraction, and (3) classification. Gray level thresholding and connected component labeling is used to eliminate the artifacts and pectoral muscles from the region of interest. Statistical features are extracted from this region which signify the important texture features of breast tissue. These features are fed to the support vector machine (SVM) classifier to classify it into any of the three classes namely fatty, glandular and dense tissue.The classifier accuracy obtained is 95.44%.

Visible/Near Infrared Reflectance (VNIR) Spectroscopy for Detecting Twospotted Spider Mite (Acari: Tetranychidae) Damage in Strawberries

The twospotted spider mite, Tetranychus urticae Koch, is among the most economically important pests in strawberries (Fragaria spp.). As T. urticae feeds, it ingests mesophyll cells that contain pigments essential for physiologic function and alters radiant energy use of the leaf tissue, severely compromising plant health and productivity. In our study, diffuse reflectance spectroscopy in the visible and near infrared (VNIR) portions of the spectrum was used to identify specific spectral regions altered by T. urticae feeding and to quantitatively assess T. urticae density. During the 2006-2007 growing season, 80 strawberry leaflets with varying levels of T. urticae infestation were collected. Spectral classification of both mite density (continuous) and mite density class (categorical) were developed. Spider mite density classes were low infestation (0-20 mites/leaflet), moderate infestation (20-50 mites/leaflet), and high infestation (> or = 50 mites/leaflet). Continuous spectral prediction for leaf infestation was developed using partial least squares (PLS) regression. Classification trees were used to train spectra to categorical levels of infestation. Both models were calibrated with 67% of the samples, and accuracy was evaluated using the remaining 33%. Categorical validation accuracy was 81%, with odds ratios for correctly predicting extreme categories (low and high) of 33 and 47.7, respectively. Continuous validation efficiency was also high, with an r2 between predicted and observed of 0.85 and a root-mean-squared error (RMSE) of 12.2 mites per leaf. Developing a spectral pest monitoring system would provide a diagnostic tool allowing early and effective intervention for precision management of T. urticae in strawberry.

Multiclass primal Support Vector Machines for breast density classification

Parenchymal patterns defining the density of breast tissue are detected by advanced correlation pattern recognition in an integrated Computer-Aided Detection (CAD) and diagnosis system. Fractal signatures of density are modelled according to four clinical categories. A Support Vector Machine (SVM) in the primal formulation solves the multiclass problem using 'One-Versus-All' (OVA) and 'All-Versus-All' (AVA) decompositions, achieving 85% and 94% accuracy, respectively. Fully automated classification of breast density via a texture model derived from fractal dimension, dispersion, and lacunarity moves current qualitative methods forward to objective quantitative measures, amenable with the overarching vision of substantiating the role of density in epidemiological risk models of breast cancer.

Automated Classification of Breast Parenchymal Density: Topologic Analysis of X-Ray Attenuation Patterns Depicted with Digital Mammography

We used an algorithm for quantitative image processing to classify breast tissue into the categories fibrosis, involution atrophy, and normal. The algorithm entailed use of Minkowski functionals in topologic analysis of x-ray attenuation patterns on digital mammograms. The results were compared with those of techniques based on evaluation of gray-level histograms. One hundred digital mammograms were classified by consensus of two experienced readers. A topologic parameter extracted from the Minkowski functional spectra was obtained for retromammilar image sections (512 x 512 pixels). From the gray-level histogram of each of these samples, the 20th percentile, median, and mean were determined. Discriminant analysis was used to assess the predictive value of the methods with respect to correct categorization. The mean gray-level intensity of normal breast tissue was 90 +/- 9, and the 20th percentile was 68 +/- 18. The mean gray-level intensity was 84 +/- 7 for involution and 90 +/- 8 for fibrosis; the 20th percentile was 75 +/- 6 for involution and 73 +/- 10 for fibrosis. The results of discriminant analysis showed that use of the gray-level histogram parameters led to correct classification in 66% of cases. Use of topologic analysis with Minkowski functionals increased the rate of correct classification to 83%. When a combined model of histogram-derived parameters and Minkowski functionals was used, 89% of cases were categorized correctly. Topologic analysis of x-ray attenuation patterns on digital mammograms obtained with Minkowski functionals is simple and robust, and the results agree with radiologists' ratings. Because correct classification is significantly higher than with use of density features, our technique may be an objective and quantitative alternative in the evaluation of the parenchymal structure of the breast.

Nonsteroidal Anti-inflammatory Drugs and Change in Mammographic Density: A Cohort Study Using Pharmacy Records on Over 29,000 Postmenopausal Women

Use of nonsteroidal anti-inflammatory drugs (NSAID) has been associated with a decrease in breast cancer risk, but it is unknown if they also reduce mammographic density, a strong intermediate marker of breast cancer risk. We investigated NSAID use and mammographic density in 29,284 postmenopausal women who had two screening mammograms at Group Health in Seattle. We used pharmacy records to classify women as NSAID nonusers, continuers, initiators, or discontinuers based on use between the two mammograms and nine separate prescription and nonprescription NSAID classes. Using unordered polytomous logistic regression methods, we modeled the odds ratio (OR) of staying not dense, decreasing density, or increasing density relative to remaining dense based on Breast Imaging Reporting Data System classification of density. There was no association with density change from initiation or continuation of NSAIDs. However, both initiators and continuers of any NSAIDs were more likely to stay not dense than stay dense [OR, 1.12; 95% confidence interval (95% CI), 1.04-1.20; OR, 1.25; 95% CI, 1.05-1.49, respectively]. This association with staying not dense for initiators and continuers of any NSAID use was observed primarily among women ages <65 years at first mammogram (OR, 1.24; 95% CI, 1.12-1.36; OR, 1.48; 95% CI, 1.14-1.93, respectively). Initiation of NSAID use did not reduce mammographic density over the short term. Continuers of NSAID use were more likely to stay not dense compared with nonusers, suggesting that it is plausible that longer-term use of NSAIDs may be needed to reduce density.

Application of the 1994 WHO Classification to Populations Other Than Postmenopausal Caucasian Women: The 2005 ISCD Official Positions

In 2003, the International Society for Clinical Densitometry (ISCD) developed Official Positions regarding the applicability of the World Health Organization (WHO) classification of bone mineral density to populations other than postmenopausal women. However, these prior Official Positions do not fully address bone mineral density reporting in females prior to menopause, men, and non-whites. During the 2005 ISCD Position Development Conference, members of the ISCD Expert Panel in conjunction with the ISCD Scientific Advisory Committee re-addressed these topics and, based upon stringent reviews of best available data, developed ISCD Official Positions that provide greater specificity and clarification with respect to the following: (1) the utility of the term 'osteopenia'; (2) utilization of T- and Z-scores for bone mineral density reporting; (3) when to apply the WHO densitometric classification; and (4) which normative database(s) should be used for non-white individuals. Briefly, the term "osteopenia" is retained, but 'low bone mass' or 'low bone density' is preferred. Z-scores, not T-scores, are preferred in females prior to menopause and males under age 50. In these individuals, a Z-score of -2.0 or lower is defined as "below the expected range for age" and a Z-score above -2.0 is "within the expected range for age." T-scores are preferred and the WHO classification is applicable for postmenopausal women and men age 50 and older. These Official Positions, rationale and evidence are discussed in the following report.

Premenopausal bone health assessment

The World Health Organization criteria for classification of bone mineral density (BMD) cannot be applied to premenopausal women because the relationship between BMD and fracture risk is not the same as in postmenopausal women. Approximately 2.5% of premenopausal women have BMD that is more than 2.0 standard deviations below the mean BMD of an age-, gender-, and ethnicity-matched reference population. Most premenopausal women with low BMD have low peak bone mass and low 5- to 10-year probability of fracture. The management of these patients involves nonpharmacologic lifestyle measures and reassurances that fracture risk is low. A minority of premenopausal women with low BMD have significant elevation of fracture risk, usually a result of contributing diseases, conditions, or medications that may be identified and treated. Premenopausal women with fractures are at increased risk for postmenopausal osteoporosis and fractures later in life.