ArticleDento maxillo facial radiology
Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches.
Article in Dento maxillo facial radiology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 3 syntheses or guidelines pooled 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
- Use of fractal analysis in dental images for osteoporosis detection: 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 · 2021Pooled it
- The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review.Dento maxillo facial radiology · 2020Pooled it
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Mandibular condyle detection using deep learning and double attractor-based energy valley optimizer algorithm.BMC oral health · 2025Article
- Application of Artificial Intelligence Methods on Osteoporosis Classification with Radiographs-A Systematic Review.Bioengineering (Basel, Switzerland) · 2024Review
- Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review.Bioengineering (Basel, Switzerland) · 2024Review
- Clinical decision support system using hierarchical fuzzy diagnosis model for migraine and tension-type headache based on International Classification of Headache Disorders, 3rd edition.Frontiers in neurology · 2024Article
- Machine Learning in Dentistry: A Scoping Review.Journal of clinical medicine · 2023Article
- The Use of Artificial Intelligence in Dentistry Practices.The Eurasian journal of medicine · 2022Article
- Comparison of five convolutional neural networks for predicting osteoporosis based on mandibular cortical index on panoramic radiographs.Dento maxillo facial radiology · 2022Article
- Current applications and development of artificial intelligence for digital dental radiography.Dento maxillo facial radiology · 2022Article
- A brief introduction to concepts and applications of artificial intelligence in dental imaging.Oral radiology · 2021Article
- Changing trabecular patterns in panoramic radiographs of Swedish women during 25 years of follow-up.Dento maxillo facial radiology · 2020Article
- Evaluation of Transfer Learning with Deep Convolutional Neural Networks for Screening Osteoporosis in Dental Panoramic Radiographs.Journal of clinical medicine · 2020Article
- Osteoporosis Recognition in Rats under Low-Power Lens Based on Convexity Optimization Feature Fusion.Scientific reports · 2019Article
- 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
- Article
- Artificial intelligence applications in bone fractures: A bibliometric and science mapping analysis.Digital healthArticle
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesThis 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.
methodsThe 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.
resultsProposed 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.
conclusionsThe 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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