Diagnosis of osteoporosis by extraction of trabecular features from hip radiographs using support vector machine: An investigation panorama with DXA (original) (raw)
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BMC Medical Imaging, 2012
Background: Early diagnosis of osteoporosis can potentially decrease the risk of fractures and improve the quality of life. Detection of thin inferior cortices of the mandible on dental panoramic radiographs could be useful for identifying postmenopausal women with low bone mineral density (BMD) or osteoporosis. The aim of our study was to assess the diagnostic efficacy of using kernel-based support vector machine (SVM) learning regarding the cortical width of the mandible on dental panoramic radiographs to identify postmenopausal women with low BMD. Methods: We employed our newly adopted SVM method for continuous measurement of the cortical width of the mandible on dental panoramic radiographs to identify women with low BMD or osteoporosis. The original X-ray image was enhanced, cortical boundaries were determined, distances among the upper and lower boundaries were evaluated and discrimination was performed by a radial basis function. We evaluated the diagnostic efficacy of this newly developed method for identifying women with low BMD (BMD T-score of -1.0 or less) at the lumbar spine and femoral neck in 100 postmenopausal women (≥50 years old) with no previous diagnosis of osteoporosis. Sixty women were used for system training, and 40 were used in testing. Results: The sensitivity and specificity using RBF kernel-SVM method for identifying women with low BMD were 90.9% [95% confidence interval (CI), 85.3-96.5] and 83.8% (95% CI, 76.6-91.0), respectively at the lumbar spine and 90.0% (95% CI, 84.1-95.9) and 69.1% (95% CI, 60.1-78.6), respectively at the femoral neck. The sensitivity and specificity for identifying women with low BMD at either the lumbar spine or femoral neck were 90.6% (95% CI, 92.0-100) and 80.9% (95% CI, 71.0-86.9), respectively. Conclusion: Our results suggest that the newly developed system with the SVM method would be useful for identifying postmenopausal women with low skeletal BMD.
Proceedings of the 2019 InSITE Conference
Aim/Purpose: The aim of the study was to analyze the structure of the bone tissue by using texture analysis of the bone trabeculae, as visualized in a routine radiograph of the proximal femur . This could provide objective information regarding both the mineral content and the spatial structure of bone tissue. Therefore, machine-learning tools were applied to explore the use of texture analysis for obtaining information on the bone strength. Background: One in three women in the world develops osteoporosis, which weakens the bones, causes atraumatic fractures and lowers the quality of life. The damage to the bones can be minimized by early diagnosis of the disease and preventive treatment, including appropriate nutrition, bone-building exercise and medications. Osteoporosis is currently diagnosed primarily by DEXA (Dual Energy X-ray Absorptiometry), which measures the bone mineral density alone. However, bone strength is determined not only by its mineral density but also by the spa...
Support Vector Machine Classification using Correlation Prototypes for Bone Age Assessment
Informatik aktuell, 2012
Bone age assessment (BAA) on hand radiographs is a frequent and time consuming task in radiology. Our method for automatic BAA is done in several steps: (i) extract of 14 epiphyseal regions from the radiographs, (ii) for each region, retain image features using the IRMA framework, (iii) use these features to build a classifier model, (iv) classify unknown hand images. In this paper, we combine a support vector machine (SVM) with cross-correlation to a prototype image for each class. These prototypes are obtained choosing the most similar image in each class according to mean cross-correlation. Comparing SVM with k nearest neighbor (kNN) classification, a systematic evaluation is presented using 1,097 images of 30 diagnostic classes. Mean error in age prediction is reduced from 1.0 to 0.9 years for 5-NN and SVM, respectively.
Osteoporosis is a disease characterized by impairment of bone microarchitecture that causes high socioeconomic impacts in the world because of fractures and hospitalizations. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing the disease, access to DXA in developing countries is still limited due to its high cost, being present only in specialized hospitals. In this paper, we analyze the performance of Osseus, a low-cost portable device based on electromagnetic waves that measures the attenuation of the signal that crosses the medial phalanx of a patient's middle finger and was developed for osteoporosis screening. The analysis is carried out by predicting changes in bone mineral density using Osseus measurements and additional common risk factors used as input features to a set of supervised classification models, while the results from DXA are taken as target (real) values during the training of the machine learning algorithms. The dataset cons...
Methods for Computer-Aided Osteoporosis Screening System
International Journal of Engineering and Advanced Technology, 2020
Osteoporosis is a disease that is not only a national issue but has also become a global issue. Although morbidity and mortality rates are relatively low in osteoporosis, fractures because of the disease makes the sufferer feel sick and suffering and affects socio-economic conditions in terms of health care systems and communities. Osteoporosis can be prevented by conducting early detection. Currently, DEXA is used to perform an osteoporosis test that becomes the World Health Organization (WHO) standard. However, the examination with DEXA is still relatively expensive. It technically can’t show the bones’ architecture. So that the examination method using a bone image that has trabeculae like wrist, thigh, jaw, hand, or foot is developed. Some research results on the osteoporosis examination system are presented in this article. The methods include such processes as image acquisition, image enhancement, image analysis (extraction and feature selection), as well as the classification...
Osteoporosis detection using machine learning techniques and feature selection
Osteoporosis is a disease of bones that leads to an increased risk of fracture and it is characterized by low bone mineral density and micro-architectural deterioration of bone tissue. In this article, the dataset consists of 3426 subjects (1083 pathological and 2343 healthy cases) whose diagnosis was based on laboratory and osteal bone densitometry examination. In all cases, four diagnostic factors for osteoporosis risk prediction, namely age, sex, height and weight were stored for later evaluation with the selected classifiers. In order to categorize subjects into two classes (osteoporosis, non-osteoporosis), twenty machine learning techniques were assessed, based on their popularity and frequency in biomedical engineering problems. All classifiers have been evaluated using the well-known 10-fold cross validation method and the results are reported analytically. In addition, a feature selection method identified that with the use of only two diagnostic factors (age and weight), similar performance could be achieved. The scope of the proposed exhaustive methodology is to assist therapists in osteoporosis prediction, avoiding unnecessary further testing with bone densitometry.
Computer aided diagnostic tool for osteoporosis estimation
International Journal of Biomedical Engineering and Technology, 2012
Quantitative estimation of osteoporosis is the need of the hour. A majority of the older population worldwide is suffering from this disease. X-ray examinations are the most widely used method for osteoporosis estimation. It has severe limitation of examination being qualitative. The paper presents a novel approach by digitising the X-ray films using an X-ray digitiser and analysing the digitised films using LabVIEW Software. The method calculates the Osteoporosis Estimation Index on the basis of the ratio of histogram calculated for selected reference ROI and measurand ROI on the digitised patient images. The results indicate the accuracy and reliability of the technique over the present methods used.
International Journal of Environmental Research and Public Health, 2021
Osteoporosis is treatable but often overlooked in clinical practice. We aimed to construct prediction models with machine learning algorithms to serve as screening tools for osteoporosis in adults over fifty years old. Additionally, we also compared the performance of newly developed models with traditional prediction models. Data were acquired from community-dwelling participants enrolled in health checkup programs at a medical center in Taiwan. A total of 3053 men and 2929 women were included. Models were constructed for men and women separately with artificial neural network (ANN), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and logistic regression (LoR) to predict the presence of osteoporosis. Area under receiver operating characteristic curve (AUROC) was used to compare the performance of the models. We achieved AUROC of 0.837, 0.840, 0.843, 0.821, 0.827 in men, and 0.781, 0.807, 0.811, 0.767, 0.772 in women, for ANN, SVM, RF, KNN, and LoR models...
Basic study for automatic recognition of osteoporosis using abdominal x-ray CT images
Medical Imaging 2004: Image Processing, 2004
We have developed an algorithm that can be used to distinguish the central part of the vertebral body from an abdominal X-ray CT image and to automatically calculate three measures to diagnose the degree of osteoporosis in a patient. In addition, we examined whether it is possible to use these CT images as an aid in diagnosing osteoporosis. Three measures that were automatically extracted from the central part of a vertebral body in the CT images were compared with the bone mineral density (BMD) values that were obtained from the same vertebral body. We calculated the mean CT number, coefficient of variation, and the first moment of power spectrum in the recognized vertebral body. We judged whether a patient had osteoporosis using the diagnostic criteria for primary osteoporosis (Year 2000 revision, published by the Japanese Society for Bone and Mineral Research). We classified three measures for normal and abnormal groups using the principal component analysis, and the two groups were compared with the results obtained from the diagnostic criteria. As a result, it was found that the algorithm could be used to distinguish the central part of the vertebral body in the CT images and to calculate these measures automatically. When distinguishing whether a patient was osteoporotic or not with the three measures obtained from the CT images, the ratio (sensitivity) usable for diagnosing a patient as osteoporotic was 0.93 (14/15), and the ratio (specificity) usable for diagnosing a patient as normal was 0.64 (7/11). Based on these results, we believe that it is possible to utilize the measures obtained from these CT images to aid in diagnosing osteoporosis.
Prediction of Osteoporosis using Soft Computing Techniques : A Review Ms
2018
Osteoporosis is one of the unpredictable disease, in which the micro architecture of bones becomes weak due to the low bone mineral density. Today this disease is very common amongst old people aged more than 50 years. This common public health problem has no symptoms and only technique available to diagnose is dual-energy x-ray absorptiometry (DEXA) scan. Generally bone mineral density (BMD) can be measured using DEXA, which is compared against the standard BMD values for diagnoses. The availability of DEXA machine in a country like India is a serious issue, because of its high cost and the high percentage of population living in rural areas with limited health care facilities. Here, our aim is to detect this disease on the basis of patient’s medical history without the necessity of DEXA scans report; so that it becomes feasible for the Indian population. For this proposed work will try to implement various Soft computing techniques which are available nowadays. © 2018 Elixir All r...