ArticlePloS one2026
CBCT-based volumetric assessment of the maxillary sinus in temporomandibular disorder: Integration of morphometric analysis and classical machine learning classification.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Biomarkers for Treatment Response in Orthodontics: Molecular Mechanisms, Clinical Utility, and Future Directions.International journal of molecular sciences · 2026Review
- Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aims to determine the maxillary sinus volume and surface area values and their relationship in individuals with and without temporomandibular disorders (TMDs) using Cone-Beam Computed Tomography and Machine Learning.
methodsThis retrospective study was performed on 127 subjects, 66 in the control group (41 females, 25 males; mean age 28.35 ± 9.9years) and 61 in the TMD group (54 females, 7 males; mean age 35 ± 12.6years) using dento-maxillo-facial CBCT images. Images were acquired as DICOM files and imported into 3D Slicer (version 5.6.2). The volume and surface area of the maxillary sinus were automatically calculated by the 3D Slicer programme. In addition, automatic prediction was performed using classical machine learning techniques on the dataset obtained in the study.
resultsMaxillary sinus volume was 30.85 ± 10.14 cm3 in the control group and 26.97 ± 10.33 cm3 in the TMD group. Maxillary sinus volume and surface area were significantly smaller in the TMD group compared to controls. No significant differences were observed between age decades in either group. Furthermore, the results obtained in machine learning showed that gender selection generally improved the results, and the most successful classifier was the Logistic Regression algorithm.
conclusionsThis study demonstrates that TMDs were associated with smaller sinus volume. Furthermore, a machine learning-based model can be used to discriminate temporomandibular dysfunction even when the size of the dataset is small.
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