ArticleDento maxillo facial radiology2022
Osteoporosis screening support system from panoramic radiographs using deep learning by convolutional neural network.
Article in Dento maxillo facial radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 37 citations in OpenAlex.
- The Diagnostic Value of Image-Based Machine Learning for Osteoporosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Deep learning-based evaluation of panoramic radiographs for osteoporosis screening: a systematic review and meta-analysis.BMC medical imaging · 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
- Artificial Intelligence-Assisted Structural Analysis of Bones with Paget's Disease of Bone and Osteoporosis: Lessons from Mouse Models.Diagnostics (Basel, Switzerland) · 2026Article
- Reproducibility of Mandibular Cortical Index Classification Among Dental Examiners and a Single Generative AI Platform: An Observer Agreement Study.Clinics and practice · 2026Article
- Consensus statement on the application of artificial intelligence in osteoporosis screening and management: perspectives from the Asia-Pacific region.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Article
- U-net-based segmentation of foreign bodies and ghost images in panoramic radiographs.Oral radiology · 2026Article
- Intelligent identification of osteoporosis on hip X-rays using vision transformer.Bone reports · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- A Logistic Regression Model for Predicting Osteoporosis Using Alveolar Bone Mineral Density Measured on Intraoral Radiographs Combined with Panoramic Mandibular Cortical Index.Journal of clinical medicine · 2025Article
- Emerging applications of feature selection in osteoporosis research: from biomarker discovery to clinical decision support.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2025Review
- Reply on "Dental panoramic radiographs by dental students versus artificial intelligence".Osteoporosis and sarcopenia · 2025Article
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- Application of Artificial Intelligence Methods on Osteoporosis Classification with Radiographs-A Systematic Review.Bioengineering (Basel, Switzerland) · 2024Review
- Modular Neural Networks for Osteoporosis Detection in Mandibular Cone-Beam Computed Tomography Scans.Tomography (Ann Arbor, Mich.) · 2023Article
- Osteoporosis Screening Using Dental Panoramic Radiographs and Age at Menarche.Diagnostics (Basel, Switzerland) · 2023Article
- Comparison of detection performance of soft tissue calcifications using artificial intelligence in panoramic radiography.Scientific reports · 2022Article
- Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study was performed to develop computer-aided screening systems that could predict osteoporosis. The systems were constructed using panoramic radiographs of women aged ≥ 50 years through three types of deep convolutional neural networks (CNNs): Alexnet, VGG-16, and GoogLeNet; the performances of the constructed systems were evaluated.
methodsOne oral radiologist classified 1500 panoramic radiographs into three types. In C1, the endosteal margin of the cortex was smooth and sharp, whereas porosities were observed in C2 and C3. The risks of osteoporosis were higher in C2 and C3 than in C1; C3 had the highest risk. This information was included with the images as training data; three CNNs were transfer trained. Using each trained CNN, the diagnostic accuracy was assessed using panoramic radiographs and bone mineral density inspection findings in the lumbar spine and femoral neck of 100 additional patients.
resultsAll CNNs exhibited relatively good agreement with the oral radiologist's judgement (86.0%-90.7%). The predictive results of the three systems for osteoporosis of the lumbar spine showed sensitivities of 78.3%-82.6%, specificities of 71.4%-79.2%, and accuracies of 74.0%-79.0%. The predictive results for osteoporosis of the femoral neck showed sensitivities of 80.0%-86.7%, specificities of 67.1%-74.1%, and accuracies of 70.0%-75.0%.
conclusionsThe constructed systems were generally more accurate than the previously developed conventional system. The new systems may facilitate osteoporosis prediction and prevent subsequent fractures by encouraging patients with suspected osteoporosis to undergo further inspections (
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.