Texture analysis of mandibular cortical bone on digital dental panoramic radiographs for the diagnosis of osteoporosis in Korean women

Kavitha, Muthu Subash; An, Shengli; An, Chang-Hyeon; Huh, Kyung-Hoe; Yi, Won-Jin; Heo, Min-Suk; Lee, Sam-Sun; Choi, Soon-Chul

doi:10.1016/j.oooo.2014.11.009

Cited by 96 publications

(89 citation statements)

References 45 publications

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“…On the other hand, in an artificial neural network, learned knowledge is not transparent to the user and is concealed in several connections, thus rendering it incomprehensive. The combination of textures and mandibular cortical width based on the SVM model classifier contributed to a better assessment of osteoporosis compared with the use of only individual measurements 15 and reported a 96.8% accuracy, which is lower than our present result with femoral neck BMD. The study by Mantzaris et al 38 applied probabilistic neural networks based on the clinical characteristics of patients, which proved to be an effective potential soft computing technique for the evaluation of osteoporosis risk.…”

Section: Discussioncontrasting

confidence: 95%

“…Detection of osteoporosis using hybrid genetic swarm fuzzy classifier Kavitha et al a previous study. 15 The mean mandibular cortical width and C.FD values from both sides of the mandibular cortex were used in this study.…”

Section: Assessment Of the Mandibular Cortical Bonementioning

confidence: 99%

“…The trabecular fractal dimension was measured on the segmented image, which is an indicator of the complexity of the trabecular bone structure. 15 In addition, the following attributes were extracted from the segmented image based on structural anisotropy and mechanical properties of the trabecular bone: …”

Section: Assessment Of the Trabecular Bonementioning

confidence: 99%

“…5,8,9 However, these models require strong assumptions to predict the relationship between disease risk and each risk factor. In recent years, use of classifier systems like multilayer perceptron, 10 Bayes classifier, 11 random forest classifier, 12 multilayer feed-forward neural network 13 and support vector machine (SVM) based on cortical 14,15 or trabecular bone features 16,17 has been developed for the detection of a low BMD or osteoporosis. Although all these classifiers delivered an acceptable diagnostic accuracy, they did not explain the input variables involved, the interpretations of experimental results or how they produced a predicted outcome.…”

Section: Introductionmentioning

confidence: 99%

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Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches

Kavitha¹,

Ganeshkumar²,

Park³

et al. 2016

Dentomaxillofacial Radiology

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Objectives: This study proposed a new automated screening system based on a hybrid genetic swarm fuzzy (GSF) classifier using digital dental panoramic radiographs to diagnose females with a low bone mineral density (BMD) or osteoporosis. Methods: The geometrical attributes of both the mandibular cortical bone and trabecular bone were acquired using previously developed software. Designing an automated system for osteoporosis screening involved partitioning of the input attributes to generate an initial membership function (MF) and a rule set (RS), classification using a fuzzy inference system and optimization of the generated MF and RS using the genetic swarm algorithm. Fivefold cross-validation (5-FCV) was used to estimate the classification accuracy of the hybrid GSF classifier. The performance of the hybrid GSF classifier has been further compared with that of individual genetic algorithm and particle swarm optimization fuzzy classifiers. Results: Proposed hybrid GSF classifier in identifying low BMD or osteoporosis at the lumbar spine and femoral neck BMD was evaluated. The sensitivity, specificity and accuracy of the hybrid GSF with optimized MF and RS in identifying females with a low BMD were 95.3%, 94.7% and 96.01%, respectively, at the lumbar spine and 99.1%, 98.4% and 98.9%, respectively, at the femoral neck BMD. The diagnostic performance of the proposed system with femoral neck BMD was 0.986 with a confidence interval of 0.942-0.998. The highest mean accuracy using 5-FCV was 97.9% with femoral neck BMD. Conclusions: The combination of high accuracy along with its interpretation ability makes this proposed automatic system using hybrid GSF classifier capable of identifying a large proportion of undetected low BMD or osteoporosis at its early stage.

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Section: Discussioncontrasting

confidence: 95%

Section: Assessment Of the Mandibular Cortical Bonementioning

confidence: 99%

Section: Assessment Of the Trabecular Bonementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches

Kavitha¹,

Ganeshkumar²,

Park³

et al. 2016

Dentomaxillofacial Radiology

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show abstract

“…It also has great potential as a screening tool for osteoporosis due to its widespread use, practicality, and low cost [23]. Most researches, such as in [23]- [25], are using features of cortical bone from dental panoramic radiographs for osteoporosis detection. Another research proposed the use of mandibular condyle for detection of temporo-mandibular joint (TMJ) dislocation and its abnormalities [26].…”

Section: Ii3 Dental Panoramic Radiographsmentioning

confidence: 99%