A preliminary study on discrimination of osteoporotic fractured group from nonfractured group using support vector machine

Lee, Sooyeul; Lee, Jeong Won; Jeong, Ji-Wook; Yoo, Done-Sik; Kim, Seunghwan

doi:10.1109/iembs.2008.4649193

Cited by 10 publications

(7 citation statements)

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“…In 2007, 2008 and 2009 the application of neural networks and support vector machine was directed to better evaluation of X-ray or ultrasound images, combining several texture parameters with bone density indicators [ 20 ]. Moura Meneses et al [ 21 ], in 2008, ANN was used in bone marrow X-ray tomography for histomorphometric analysis, and a Perceptron Multiplayer model was used.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence on the identification of risk groups for osteoporosis, a general review

et al. 2018

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IntroductionThe goal of this paper is to present a critical review on the main systems that use artificial intelligence to identify groups at risk for osteoporosis or fractures. The systems considered for this study were those that fulfilled the following requirements: range of coverage in diagnosis, low cost and capability to identify more significant somatic factors.MethodsA bibliographic research was done in the databases, PubMed, IEEExplorer Latin American and Caribbean Center on Health Sciences Information (LILACS), Medical Literature Analysis and Retrieval System Online (MEDLINE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, Web of Science, and Science Direct searching the terms “Neural Network”, “Osteoporosis Machine Learning” and “Osteoporosis Neural Network”. Studies with titles not directly related to the research topic and older data that reported repeated strategies were excluded. The search was carried out with the descriptors in German, Spanish, French, Italian, Mandarin, Portuguese and English; but only studies written in English were found to meet the established criteria. Articles covering the period 2000–2017 were selected; however, articles prior to this period with great relevance were included in this study.DiscussionBased on the collected research, it was identified that there are several methods in the use of artificial intelligence to help the screening of risk groups of osteoporosis or fractures. However, such systems were limited to a specific ethnic group, gender or age. For future research, new challenges are presented.ConclusionsIt is necessary to develop research with the unification of different databases and grouping of the various attributes and clinical factors, in order to reach a greater comprehensiveness in the identification of risk groups of osteoporosis. For this purpose, the use of any predictive tool should be performed in different populations with greater participation of male patients and inclusion of a larger age range for the ones involved. The biggest challenge is to deal with all the data complexity generated by this unification, developing evidence-based standards for the evaluation of the most significant risk factors.

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence on the identification of risk groups for osteoporosis, a general review

et al. 2018

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“…In later researches, image processing and analysis based on X‐ray and CT became the mainstream (Cervinka et al, ; Lee et al, ). These methods do not rely on manual measurement but on image processing and analysis.…”

Section: Introductionmentioning

confidence: 99%

“…All features are calculated by programs based on corresponding algorithms, which can provide analysis with higher accuracy. Lee et al () used plain X‐ray images to distinguish the osteoporosis group and nonosteoporosis group. In his research, 94 women were studied in which 47 belonged to the nonfractured group and 47 experienced osteoporotic fractures.…”

Section: Introductionmentioning

confidence: 99%

“…For example, BMD has a higher average value in normal people, which is considered as a golden standard for osteoporosis prediction. But in fact, this value has an overlap between osteoporosis and normal people, as factors like age, gender, and height have an impact on it (Lee et al, ). As a result, accurate diagnosis cannot be usually achieved by such a single feature.…”

Section: Introductionmentioning

confidence: 99%

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Full supervised learning for osteoporosis diagnosis using micro‐CT images

Chen

et al. 2013

Microscopy Res & Technique

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Early osteoporosis diagnosis is of important significance for reducing fracture risk. Image analysis provides a new perspective for noninvasive diagnosis in recent years. In this article, we propose a novel method based on machine-learning method performed on micro-CT images to diagnose osteoporosis. The aim of this work is to find a way to more effectively and accurately diagnose osteoporosis on which many methods have been proposed and practiced. In this method, in contrast to the previously proposed methods in which features are analyzed individually, several features are combined to build a classifier for distinguishing osteoporosis group and normal group. Twelve features consisting of two groups are involved in our research, including bone volume/total volume (BV/TV), bone surface/bone volume (BS/BV), trabecular number (Tb.N), obtained from the software of micro-CT, and other four features from volumetric topological analysis (VTA). Support vector machine (SVM) method and k-nearest neighbor (kNN) method are introduced to create classifiers with these features due to their excellent performances on classification. In the experiment, 200 micro-CT images are used in which half are from osteoporosis patients and the rest are from normal people. The performance of the obtained classifiers is evaluated by precision, recall, and F-measure. The best performance with precision of 100%, recall of 100%, and F-measure of 100% is acquired when all the features are included. The satisfying result demonstrates that SVM and kNN are effective for diagnosing osteoporosis with micro-CT images.

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“…A classifier is a systematic approach to building classification models from an input data set. Examples include Decision Tree Classifiers [6], Rule-Based Classifiers [7], Neural Networks [8], Support Vector Machines [9] and Naïve Bayes Classifiers [10]. Fusion classifiers or Multiple Classifier Systems (MCS) have received considerable attention in applied statistics [11], machine learning [12] and pattern recognition [13] for over a decade.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Systems for Automatic Fracture Detection

Mahendran¹,

Baboo²

2012

IJET

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Fracture detection based on image classification is an area of research which has proved to be challenging for the past several decades. This field has gained more attention due to the new challenges posed by voluminous image databases. In this research work, fusion-based classifiers are constructed, which extracts features from the images, use these features to train and test the classifiers for the purpose of detecting fractures in X-Ray images. The various features extracted are Contrast, Homogeneity, Energy, Entropy, Mean, Variance, Standard Deviation, Correlation, Gabor orientation (GO), Markov Random Field (MRF), and intensity gradient direction (IGD). Three classifiers, BPNN, SVM and NB classifiers are used. Using these features and classifiers, three single classifiers and four multiple classifiers were developed. All the classifiers were tested vigorously with the test dataset for evaluating the winner combination of classifiers and features that correctly identifies fractures in a bone image. The performance metrics used are sensitivity, specificity, positive predictive value, negative predictive value, accuracy and execution time. The experimental results showed that usage of fusion classifiers enhances the detection capacity and the combination SVM and BPNN produces the best result.

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A preliminary study on discrimination of osteoporotic fractured group from nonfractured group using support vector machine

Cited by 10 publications

References 10 publications

Artificial intelligence on the identification of risk groups for osteoporosis, a general review

Artificial intelligence on the identification of risk groups for osteoporosis, a general review

Full supervised learning for osteoporosis diagnosis using micro‐CT images

Ensemble Systems for Automatic Fracture Detection

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