ArticleDento maxillo facial radiology2023
A support vector machine-based algorithm to identify bisphosphonate-related osteonecrosis throughout the mandibular bone by using cone beam computerized tomography images.
Article in Dento maxillo facial radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Deep learning-based automatic segmentation of MRONJ lesions on CBCT images.BMC oral health · 2026Article
- Automated Kellgren-Lawrence grading of knee osteoarthritis using a multi-scale attention-based deep learning framework.Frontiers in medicine · 2026Article
- Prospects of artificial intelligence in dentistry.Dental research journal · 2024Article
- Machine Learning Approaches for Integrating Clinical and Radiographic Data in the Early Detection of Osteonecrosis of the Jaw : Machine Learning Approaches for Integrating Clinical Data in Osteonecrosis of the Jaw.Galen medical journal · 2024Review
- Artificial intelligence for maxillofacial prosthodontics: A technological shift in craniofacial rehabilitation- a scoping review.Journal of oral biology and craniofacial researchReview
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to develop an algorithm to distinguish the patients with bisphosphonate-related osteonecrosis of the jaws (BRONJ) from healthy controls using CBCT images by evaluating both trabecular and cortical bone changes through the whole body of the mandibular bone.
methodsPatient data set was created from axial CBCT images of 7 BRONJ patients (28 slices) and 8 healthy controls (27 slices). The healthy bone of healthy controls, bone sclerosis of BRONJ patients, bone necrosis of BRONJ patients, and normal appearing bone of BRONJ patients (NBP) were labeled on CBCT images by three maxillofacial radiologists. Proposed algorithm had preparation and background cancellation, mandibular bone segmentation and centerline determination, spatial transformation of gray values, and classification steps.
resultsSignificant differences between the statistical moments (mean, variance, skewness, kurtosis, standard error, median, mode and coefficient of variance) of healthy and diseased (bone sclerosis and necrosis) groups were observed (
conclusionThe proposed algorithm differentiated the mandibular bones of the healthy and the BRONJ patients with high accuracy in the present test sample.
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