Midpalatal suture maturation in 15- to 35-year-olds: morphological assessment in the coronal plane using CBCT-an exploratory study.
Tannia VillarroelSharime YagnamDaniela VicuñaGuillermo ConchaRodrigo OyonartePublished in: Odontology (2023)
Rapid maxillary expansion (RME) is used in patients presenting transverse maxillary deficiency. However, RME may be unpredictable after late adolescence if the midpalatal sutural maturation stage (MPMS) is in late stage. Since MPMS evaluation is influenced by the expertise of the operator and image quality, this classification method could be complemented. Therefore, this study aimed to analyze the morphology of the midpalatal suture (MPS) and its surrounding bone in the coronal plane using cone beam computed tomography (CBCT) images of adolescents and young adults and to correlate the findings with their respective MPMS status. CBCT scans of 200 patients aged 15-35 years of both sexes were evaluated. MPS and surrounding bone tissue characteristics in the coronal and axial sections were analyzed, and MPMS was evaluated (A to E). Six categories were identified for the coronal evaluation, analyzing it in 2 standardized locations. Sutural morphology: I, hypodense sutural line limited by 2 hyper-dense para-sutural lines; II, hypodense sutural line; and III, suture not visible. The presence of para-sutural cancellous bone: CB0, compact bone; CB1, dense cancellous tissue; CB2, spaced cancellous bone. Midpalatal bone thickness (MBTh) was also measured. ANOVA-Bonferroni, chi-square, logistic regression and t-tests were used. MPMS increased with age. Sutural morphology I and CB0 bone were the most prevalent. Here we show that sutural morphology, para-sutural bone characteristics, and MPSM were significantly associated. The coronal evaluation of the MPS can complement the information from the MPSM assessment, allowing the identification of patent MPS even in the presence of late MPSM.
Keyphrases
- bone mineral density
- cone beam computed tomography
- image quality
- soft tissue
- bone loss
- end stage renal disease
- bone regeneration
- computed tomography
- newly diagnosed
- chronic kidney disease
- ejection fraction
- prognostic factors
- deep learning
- depressive symptoms
- optical coherence tomography
- machine learning
- peritoneal dialysis
- patient reported
- case report
- loop mediated isothermal amplification
- convolutional neural network