ArticleDento maxillo facial radiology2022
Comparison of five convolutional neural networks for predicting osteoporosis based on mandibular cortical index on panoramic radiographs.
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 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 32 citations in OpenAlex.
- 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
- Edge-Deployable Lightweight Deep Learning for Hypertensive Retinopathy Grading from En-Face OCT: A Patient-Level Feasibility Study.Bioengineering (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
- 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 Deep Metric Learning and Multimodal Gated Fusion Framework for AI-Driven Risk Assessment of Lingual Plate Perforation and Mandibular Canal Injury in Posterior Mandible Implants.International journal of dentistry · 2026Article
- Development of AI-Based Predictive Models for Osteoporosis Diagnosis in Postmenopausal Women from Panoramic Radiographs.Journal of clinical medicine · 2025Article
- Quantitative analysis of mandibular cortical morphology using artificial intelligence-based computer assisted diagnosis for panoramic radiography on underlying diseases and dental status in women over 20 years of age.Journal of dental sciences · 2024Article
- Deep learning system for distinguishing between nasopalatine duct cysts and radicular cysts arising in the midline region of the anterior maxilla on panoramic radiographs.Imaging science in dentistry · 2024Article
- Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Osteoporosis Screening Using Dental Panoramic Radiographs and Age at Menarche.Diagnostics (Basel, Switzerland) · 2023Article
- Personalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging.Dento maxillo facial radiology · 2023Review
- 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 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe aim of the present study was to compare five convolutional neural networks for predicting osteoporosis based on mandibular cortical index (MCI) on panoramic radiographs.
methodsPanoramic radiographs of 744 female patients over 50 years of age were labeled as C1, C2, and C3 depending on the MCI. The data of the present study were reviewed in different categories including (C1, C2, C3), (C1, C2), (C1, C3), and (C1, (C2 +C3)) as two-class and three-class predictions. The data were separated randomly as 20% test data, and the remaining data were used for training and validation with fivefold cross-validation. AlexNET, GoogleNET, ResNET-50, SqueezeNET, and ShuffleNET deep-learning models were trained through the transfer learning method. The results were evaluated by performance criteria including accuracy, sensitivity, specificity, F1-score, AUC, and training duration. The Gradient-Weighted Class Activation Mapping (Grad-CAM) method was applied for visual interpretation of where deep-learning algorithms gather the feature from image regions.
resultsThe dataset (C1, C2, C3) has an accuracy rate of 81.14% with AlexNET; the dataset (C1, C2) has an accuracy rate of 88.94% with GoogleNET; the dataset (C1, C3) has an accuracy rate of 98.56% with AlexNET; and the dataset (C1,(C2+C3)) has an accuracy rate of 92.79% with GoogleNET.
conclusionThe highest accuracy was obtained in the differentiation of C3 and C1 where osseous structure characteristics change significantly. Since the C2 score represent the intermediate stage (osteopenia), structural characteristics of the bone present behaviors closer to C1 and C3 scores. Therefore, the data set including the C2 score provided relatively lower accuracy results.
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