Reliability and validity of two software systems used to measure the pharyngeal airway space in three-dimensional analysis

Impact of class II and class III skeletal malocclusion on pharyngeal airway dimensions: A systematic literature review and meta-analysis

Background

This study is a pioneer systematic review and meta-analysis aimed at comparing the influence of Class II and Class III skeletal malocclusions on pharyngeal airway dimensions. It stands as the inaugural comprehensive assessment to collate and analyze the disparate findings from previously published articles on this topic. The objective of this study was to identify published articles that compare the effects of class II and class III skeletal malocclusion on the pharyngeal airway dimensions.

Methods

An all-inclusive search for existing published studies was done to identify peer-reviewed scholarly articles that compared the influence of class II and class III skeletal malocclusion on pharyngeal airway dimensions. The search was done via five electronic databases: Cochrane Library, EMBASE, Scopus, Web of Science, and PubMed. Screening of the articles was done and the eligible studies were critically assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist.

Results

The initial search yielded 476 potential articles of which, nine were finally included in this study for a total of 866 patients. Three studies were of cross-sectional design and six were of retrospective study design. Following a critical analysis and review of the studies, class III skeletal malocclusion had significantly larger volume and area measurements compared to class II skeletal malocclusion.

Conclusion

Research in the field of literature has established that variations in skeletal classifications have a discernible effect on the size of the pharyngeal airways. With the advancement of skeletal malocclusions to a class III, there is an observed increase in both the volume and cross-sectional area of the airways.

Deep Learning Models for Automatic Upper Airway Segmentation and Minimum Cross-Sectional Area Localisation in Two-Dimensional Images

OBJECTIVE

To develop and validate convolutional neural network algorithms for automatic upper airway segmentation and minimum cross-sectional area (CSAmin) localisation in two-dimensional (2D) radiographic airway images.

MATERIALS AND METHODS

Two hundred and one 2D airway images acquired using cone-beam computed tomography (CBCT) scanning were randomly assigned to a test group (n = 161) to train artificial intelligence (AI) models and a validation group (n = 40) to evaluate the accuracy of AI processing. Four AI models, UNet18, UNet36, DeepLab50 and DeepLab101, were trained to automatically segment the upper airway 2D images in the test group. Precision, recall, Intersection over Union, the dice similarity coefficient and size difference were used to evaluate the performance of the AI-driven segmentation models. The CSAmin height in each image was manually determined using three-dimensional CBCT data. The nonlinear mathematical morphology technique was used to calculate the CSAmin level. Height errors were assessed to evaluate the CSAmin localisation accuracy in the validation group. The time consumed for airway segmentation and CSAmin localisation was compared between manual and AI processing methods.

RESULTS

The precision of all four segmentation models exceeded 90.0%. No significant differences were found in the accuracy of any AI models. The consistency of CSAmin localisation in specific segments between manual and AI processing was 0.944. AI processing was much more efficient than manual processing in terms of airway segmentation and CSAmin localisation.

CONCLUSIONS

We successfully developed and validated a fully automatic AI-driven system for upper airway segmentation and CSAmin localisation using 2D radiographic airway images.

Three-Dimensional Pharyngeal Airway Space Changes Following Isolated Mandibular Advancement Surgery in 120 Patients: A 1-Year Follow-up Study

Lack of evidence exists related to the three-dimensional (3D) pharyngeal airway space (PAS) changes at follow-up after isolated bilateral sagittal split osteotomy (BSSO) advancement surgery. The present study assessed the 3D PAS changes following isolated mandibular advancement at a follow-up period of 1 year. A total of 120 patients (40 males, 80 females, mean age: 26.0 ± 12.2) who underwent BSSO advancement surgery were recruited. Cone-beam computed tomography (CBCT) scans were acquired preoperatively (T0), immediately following surgery (T1), and at 1 year of follow-up (T2). The volume, surface area, and minimal cross-sectional area (mCSA) of the airway were assessed. The total airway showed a 38% increase in volume and 13% increase in surface area from T0 to T1, where the oropharyngeal region showed the maximum immediate change. At T1−T2 follow-up, both volumetric and surface area showed a relapse of less than 7% for all sub-regions. The mCSA showed a significant increase of 71% from T0 to T1 (p < 0.0001), whereas a non-significant relapse was observed at T1−T2 (p = 0.1252). The PAS remained stable at a follow-up period of 1 year. In conclusion, BSSO advancement surgery could be regarded as a stable procedure for widening of the PAS with maintenance of positive space at follow-up.

Deep Learning Based Airway Segmentation Using Key Point Prediction

The purpose of this study was to investigate the accuracy of the airway volume measurement by a Regression Neural Network-based deep-learning model. A set of manually outlined airway data was set to build the algorithm for fully automatic segmentation of a deep learning process. Manual landmarks of the airway were determined by one examiner using a mid-sagittal plane of cone-beam computed tomography (CBCT) images of 315 patients. Clinical dataset-based training with data augmentation was conducted. Based on the annotated landmarks, the airway passage was measured and segmented. The accuracy of our model was confirmed by measuring the following between the examiner and the program: (1) a difference in volume of nasopharynx, oropharynx, and hypopharynx, and (2) the Euclidean distance. For the agreement analysis, 61 samples were extracted and compared. The correlation test showed a range of good to excellent reliability. A difference between volumes were analyzed using regression analysis. The slope of the two measurements was close to 1 and showed a linear regression correlation (r2 = 0.975, slope = 1.02, p < 0.001). These results indicate that fully automatic segmentation of the airway is possible by training via deep learning of artificial intelligence. Additionally, a high correlation between manual data and deep learning data was estimated.

Cone beam computed tomographic evaluation of pharyngeal airway in North Indian children with different skeletal patterns

BACKGROUND

In growing patients with skeletal discrepancies, early assessment of functional factors can be vital for the restoration of normal craniofacial growth.

AIM

To compare airway volumes in patients with mandibular retrognathism with the normal anteroposterior skeletal relationship, thereby assessing the association between cephalometric variables and airway morphology.

METHODS

Cone-beam computed tomography volume scans, and lateral cephalograms, 3-dimensional airway volume and cross-sectional areas of 120 healthy children (54 boys and 66 girls mean age 15.19 ± 1.28) which were done for orthodontic assessment were evaluated. The subjects were divided into 2 groups based on the angle formed between point A, Nasion and point B (ANB) values and cephalometric variables (such as anterior and posterior facial height, gonial angle etc.) airway volumes, and cross-sectional measurements were compared using independent t tests. Pearson’s correlation coefficient test was used to detect any relationship of different parts of the airway and between airway volume and 2-dimensional cephalometric variables.

RESULTS

Means and standard deviations for cephalometric, cross-sectional, and volumetric variables were compared. ANB, mandibular body length and facial convexity were statistically highly significant (P < 0.01) whereas condylion to point A, nasal airway and total airway volume (P < 0.05) were statistically significant. The nasal airway volume and the superior pharyngeal airway volume had a positive correlation (P < 0.01), nasal airway was correlated to middle (P < 0.05) and total airway superior had a relation with middle (P < 0.05), inferior and total airway (P < 0.05), middle was related to all other airways; inferior was also related to all the airways except nasal. Lateral cephalometric values were positively correlated with the airway volume with Frankfurt Mandibular Plane Angle and facial convexity showed significant correlations with total airway volume (P < 0.05). Additionally, ANB angle was significantly correlated with total airway volume and superior airway (P < 0.05).

CONCLUSION

The mean total airway volume in patients with retrognathic mandible was significantly smaller than that of patients with a normal mandible.