Related Experiment Video
Updated: May 7, 2026
Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
Published on: January 5, 2024
The combination of a histogram-based clustering algorithm and support vector machine for the diagnosis of
Muthu Subash Kavitha1, Akira Asano, Akira Taguchi
1Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, Korea. ; Graduate School of Engineering, Hiroshima University, Hiroshima, Japan.
Insights
This study introduces a novel histogram-based automatic clustering (HAC) and support vector machine (SVM) approach to analyze dental panoramic radiographs (DPRs) for precise osteoporosis diagnosis in postmenopausal women.
Area of Science:
- Radiology and Imaging
- Osteoporosis Research
- Artificial Intelligence in Medicine
Background:
- Accurate diagnosis of low bone mineral density (BMD) and osteoporosis in postmenopausal women is crucial for prevention.
- Current diagnostic methods may require improvement for enhanced precision.
Purpose of the Study:
- To develop and validate an automated method using histogram-based automatic clustering (HAC) and support vector machine (SVM) for precise osteoporosis diagnosis.
- To improve the diagnostic accuracy of identifying postmenopausal women with low BMD or osteoporosis through dental panoramic radiographs (DPRs).
Main Methods:
- Integration of a novel HAC algorithm with a computer-aided diagnosis system utilizing support vector machine (SVM) classifiers.
- Extraction of moment-based features (mean, variance, skewness, kurtosis) from mandibular cortical width in DPRs.
- Comparison of SVM model performance against a back propagation (BP) neural network model using data from 100 postmenopausal women.
Main Results:
- The HAC-SVM model achieved high diagnostic accuracy for low BMD at the lumbar spine (93.0%) and femoral neck (89.0%).
- High sensitivity (95.8% at lumbar spine, 96.0% at femoral neck) and specificity (86.6% at lumbar spine, 84.0% at femoral neck) were reported.
- The SVM model demonstrated superior diagnostic efficacy compared to the BP neural network model.
Conclusions:
- The proposed HAC-SVM model applied to DPRs shows significant potential for assisting dentists in the early diagnosis of osteoporosis.
- This approach could contribute to reducing morbidity and mortality associated with low BMD and osteoporosis.
- The study highlights the utility of AI-driven analysis of dental radiographs for systemic health assessment.
Purpose:
To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis.
Materials And Methods:
We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion.
Results:
The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck.
Conclusion:
Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.
Related Concept Videos
01:18Classification of Bones
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The long...
01:05Histogram
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...