Retrospective analysis of the upper airway anatomy and Sella turcica morphology across different skeletal malocclusions: a computerized technique.

OBJECTIVE: This study aimed to investigate the normal volumetric space and variations in the measurements of different landmarks in adults with different skeletal relations of the maxilla and the mandible based on CBCT data. The study also analyses these landmarks to locate any correlations. BACKGROUND: Numerous studies in orthodontics have found a relationship between orthodontic treatment and changes in the anatomy and function of the airway. Severe changes in airway morphology can cause breathing difficulties, lower quality of life, and even result in life-threatening conditions such as obstructive sleep apnoea. Consequently, orthodontic diagnosis and treatment planning require a thorough understanding of the airway space and its function. METHODS: The present retrospective study was conducted using CBCT records of 120 adult patients, containing 40 samples of each skeletal class (20 males and 20 females). The boundaries were defined for the 3 major regions: the nasopharynx, the oropharynx, and the hypopharynx. Various measurements were recorded across these regions, as well as selective cephalometric landmarks. The obtained data was used to calculate average and standard deviation, while regression analysis was used to evaluate correlations and t-test was used to test statistical significance of gender differences. RESULTS: The results demonstrate that skeletal Class III individuals exhibit a reduced airway volume in the nasopharynx compared to other groups, whereas skeletal Class II individuals displayed a diminished airway volume in the hypopharynx. A strong correlation was observed for Sella turcica parameters. There were no significant differences in skeletal parameters across genders. Nasopharynx cavity volume demonstrated significant differences between skeletal Class I-Class III as well as between skeletal Class II-Class III. Hypopharynx cavity volume also demonstrated significant differences between skeletal Class I-Class II and between skeletal Class II-Class III. CONCLUSION: The major findings are the presence of a reduced nasopharyngeal volume in skeletal Class III malocclusions while skeletal Class II individuals displayed a diminished hypopharyngeal volume, making these critical areas to consider during the diagnostic and orthodontic treatment planning stages. This study also revealed a consistent correlation between Sella turcica parameters across various facial skeletal profiles, with skeletal Class II patients exhibiting a distinct pattern and skeletal Class I and Class III demonstrating an average relationship.

Development and validation of predictive models for skeletal malocclusion classification using airway and cephalometric landmarks.

OBJECTIVE: This study aimed to develop a deep learning model to predict skeletal malocclusions with an acceptable level of accuracy using airway and cephalometric landmark values obtained from analyzing different CBCT images. BACKGROUND: In orthodontics, multitudinous studies have reported the correlation between orthodontic treatment and changes in the anatomy as well as the functioning of the airway. Typically, the values obtained from various measurements of cephalometric landmarks are used to determine skeletal class based on the interpretation an orthodontist experiences, which sometimes may not be accurate. METHODS: Samples of skeletal anatomical data were retrospectively obtained and recorded in Digital Imaging and Communications in Medicine (DICOM) file format. The DICOM files were used to reconstruct 3D models using 3DSlicer (slicer.org) by thresholding airway regions to build up 3D polygon models of airway regions for each sample. The 3D models were measured for different landmarks that included measurements across the nasopharynx, the oropharynx, and the hypopharynx. Male and female subjects were combined as one data set to develop supervised learning models. These measurements were utilized to build 7 artificial intelligence-based supervised learning models. RESULTS: The supervised learning model with the best accuracy was Random Forest, with a value of 0.74. All the other models were lower in terms of their accuracy. The recall scores for Class I, II, and III malocclusions were 0.71, 0.69, and 0.77, respectively, which represented the total number of actual positive cases predicted correctly, making the sensitivity of the model high. CONCLUSION: In this study, it is observed that the Random Forest model was the most accurate model for predicting the skeletal malocclusion based on various airway and cephalometric landmarks.