ArticleScientific reports2023
Validation of bone mineral density measurement using quantitative CBCT image based on deep learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- 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
- A narrative review of artificial intelligence in dental imaging: from dataset design to clinical translation.Oral radiology · 2026Review
- Site-Specific Associations Between CBCT-Derived Gray Value and Fractal Dimension and DEXA T-Scores in Postmenopausal Women: A Cross-Sectional Analytical Study.Clinical and experimental dental research · 2026Article
- Feasibility of multisource CBCT for improving the predictability of dental implant primary stability compared to conventional CBCT.Scientific 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 in osteoporosis assessment using CT imaging: a scoping review.Frontiers in medicine · 2026Review
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Palatal height, thickness and density according to facial biotype in Peruvian adults: a tomographic study.Acta odontologica latinoamericana : AOL · 2025Observational
- Post-Extraction Bone Changes in Molars Within Personalized Implant-Prosthetic Therapy as Evaluated with Fractal Analysis of CBCT.Journal of personalized medicine · 2025Article
- Prediction of Dental Implants Primary Stability With Cone Beam Computed Tomography-Based Homogenized Finite Element Analysis.Clinical implant dentistry and related research · 2025Article
- Bone Densitometric Analysis using Cone Beam Computed Tomography (Cone Beam CT) and Computed Tomography (CT): Establishing the Correlation between Predicted and Actual Values.Advanced biomedical research · 2025Article
- The Value of Mandibular Indices on Cone Beam Computed Tomography in Secondary Causes of Low Bone Mass.Journal of clinical medicine · 2024Article
- Artificial Intelligence Application in a Case of Mandibular Third Molar Impaction: A Systematic Review of the Literature.Journal of clinical medicine · 2024Review
- Menopause-Associated Changes in Mandibular Bone Microarchitecture Are Site-Specific.Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons · 2024Article
- Improving the accuracy of bone mineral density using a multisource CBCT.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The bone mineral density (BMD) measurement is a direct method of estimating human bone mass for diagnosing osteoporosis, and performed to objectively evaluate bone quality before implant surgery in dental clinics. The objective of this study was to validate the accuracy and reliability of BMD measurements made using quantitative cone-beam CT (CBCT) image based on deep learning by applying the method to clinical data from actual patients. Datasets containing 7500 pairs of CT and CBCT axial slice images from 30 patients were used to train a previously developed deep-learning model (QCBCT-NET). We selected 36 volumes of interest in the CBCT images for each patient in the bone regions of potential implants sites on the maxilla and mandible. We compared the BMDs shown in the quantitative CBCT (QCBCT) images with those in the conventional CBCT (CAL_CBCT) images at the various bone sites of interest across the entire field of view (FOV) using the performance metrics of the MAE, RMSE, MAPE (mean absolute percentage error), R
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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.