ArticleImaging science in dentistry2013
The combination of a histogram-based clustering algorithm and support vector machine for the diagnosis of osteoporosis.
Article in Imaging science in dentistry, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 5 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- The Diagnostic Value of Image-Based Machine Learning for Osteoporosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Two-decade dialogue between artificial intelligence and osteoporosis: research trajectories and frontier projections under bibliometric and visual analysis.Frontiers in medicine · 2025Pooled it
- Diagnostic accuracy of artificial intelligence models in detecting osteoporosis using dental images: a systematic review and meta-analysis.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Pooled it
- The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review.Dento maxillo facial radiology · 2020Pooled it
- Panoramic measures for oral bone mass in detecting osteoporosis: a systematic review and meta-analysis.Journal of dental research · 2015Pooled it
- A prediction model of pediatric bone density from plain spine radiographs using deep learning.Scientific reports · 2025Article
- Automatic detection of the mental foramen for estimating mandibular cortical width in dental panoramic radiographs: the seventh survey of the Tromsø Study (Tromsø7) in 2015-2016.The Journal of international medical research · 2022Article
- Current applications and development of artificial intelligence for digital dental radiography.Dento maxillo facial radiology · 2022Article
- Prediction of osteoporosis from simple hip radiography using deep learning algorithm.Scientific reports · 2021Article
- Clinical guidelines for the application of panoramic radiographs in screening for osteoporosis.Oral radiology · 2021Review
- Mandibular Radiomorphometric Indices and Tooth Loss as Predictors for the Risk of Osteoporosis using Panoramic Radiographs.Oral health & preventive dentistry · 2020Article
- Computer-aided diagnosis systems for osteoporosis detection: a comprehensive survey.Medical & biological engineering & computing · 2020Review
- Osteoporosis Recognition in Rats under Low-Power Lens Based on Convexity Optimization Feature Fusion.Scientific reports · 2019Article
- Dissecting the Genetics of Osteoporosis using Systems Approaches.Trends in genetics : TIG · 2019Review
- Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study.Dento maxillo facial radiology · 2019Article
- Classification of osteoporosis by artificial neural network based on monarch butterfly optimisation algorithm.Healthcare technology letters · 2018Article
- Artificial intelligence on the identification of risk groups for osteoporosis, a general review.Biomedical engineering online · 2018Review
- Quantitative assessment of mandibular cortical erosion on dental panoramic radiographs for screening osteoporosis.International journal of computer assisted radiology and surgery · 2016Article
- IDIOS: An innovative index for evaluating dental imaging-based osteoporosis screening indices.Imaging science in dentistry · 2016Article
- Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches.Dento maxillo facial radiologyArticle
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4 authors.
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Abstract
purposeTo 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
methodsWe 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.
resultsThe 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.
conclusionOur 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.
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