Three-dimensional tomographic assessment of the upper airway using 2 different imaging software programs: A comparison study

Comparison of invisalign mandibular advancement and twin-block on upper airway and hyoid bone position improvements for skeletal class II children: a retrospective study

BACKGROUND

This study is to evaluate and compare the improvement of upper airway morphology and hyoid bone position in children with Class II mandibular retrusion treated with Invisalign mandibular advancement (MA) and Twin-Block (TB) appliances, utilizing cone beam computed tomography (CBCT).

METHODS

32 children aged between 8 and 11.5 years old were included in this study, with an average age of 10.2 years old. These children were divided into two groups, MA and TB, with 16 children in each group. Changes in upper airway morphology and hyoid bone position before and after treatment were analyzed using CBCT.

RESULTS

(1) Changes in upper airway before and after treatment: the oropharynx volume (Or-V), the oropharynx minimum cross-sectional area (Or-mCSA), the hypopharynx volume (Hy-V), and the hypopharynx minimum cross-sectional area (Hy-mCSA) in both the MA and TB groups increased after treatment, and the differences were statistically significant (P < 0.05) compared to pre-treatment status. (2) Changes in hyoid bone position before and after treatment: The distances between H point and third cervical vertebra (H-C3), H point and pogonion (H-RGN), H point and mandibular plane (H-MP), H point and Frankfort horizontal plane (H-FH), H and S point (H-S), and H point and palatal plane (H-PP) in both the MA and TB groups increased after treatment, and the differences were statistically significant (P < 0.05).

CONCLUSION

Both MA and TB appliances effectively improved the structural narrowness of the upper airway and reduced respiratory resistance, thus improving breath quality. However, MA showed more effectiveness in improving the narrowest part of the hypopharynx compared to TB. Both appliances also promoted anterior downward movement of the hyoid bone, which opens the upper airway of the oropharynx and hypopharynx and helps the upper airway morphology return to normal range.

Automatic three-dimensional nasal and pharyngeal airway subregions identification via Vision Transformer

OBJECTIVES

Upper airway assessment requires a fully-automated segmentation system for complete or sub-regional identification. This study aimed to develop a novel Deep Learning (DL) model for accurate segmentation of the upper airway and achieve entire and subregional identification.

METHODS

Fifty cone-beam computed tomography (CBCT) scans, including 24,502 slices, were labelled as the ground truth by one orthodontist and two otorhinolaryngologists. A novel model, a lightweight multitask network based on the Swin Transformer and U-Net, was built for automatic segmentation of the entire upper airway and subregions. Segmentation performance was evaluated using Precision, Recall, Dice similarity coefficient (DSC) and Intersection over union (IoU). The clinical implications of the precision errors were quantitatively analysed, and comparisons between the AI model and Dolphin software were conducted.

RESULTS

Our model achieved good performance with a precision of 85.88-94.25%, recall of 93.74-98.44%, DSC of 90.95-96.29%, IoU of 83.68-92.85% in the overall and subregions of three-dimensional (3D) upper airway, and a precision of 91.22-97.51%, recall of 90.70-97.62%, DSC of 90.92-97.55%, and IoU of 83.41-95.29% in the subregions of two-dimensional (2D) crosssections. Discrepancies in volume and area caused by precision errors did not affect clinical outcomes. Both our AI model and the Dolphin software provided clinically acceptable consistency for pharyngeal airway assessments.

CONCLUSION

The novel DL model not only achieved segmentation of the entire upper airway, including the nasal cavity and subregion identification, but also performed exceptionally well, making it well suited for 3D upper airway assessment from the nasal cavity to the hypopharynx, especially for intricate structures.

CLINICAL SIGNIFICANCE

This system provides insights into the aetiology, risk, severity, treatment effect, and prognosis of dentoskeletal deformities and obstructive sleep apnea. It achieves rapid assessment of the entire upper airway and its subregions, making airway management-an integral part of orthodontic treatment, orthognathic surgery, and ENT surgery-easier.

Automatic detection of adenoid hypertrophy on cone-beam computed tomography based on deep learning

INTRODUCTION

This study proposed an automatic diagnosis method based on deep learning for adenoid hypertrophy detection on cone-beam computed tomography.

METHODS

The hierarchical masks self-attention U-net (HMSAU-Net) for segmentation of the upper airway and the 3-dimensional (3D)-ResNet for diagnosing adenoid hypertrophy were constructed on the basis of 87 cone-beam computed tomography samples. A self-attention encoder module was added to the SAU-Net to optimize upper airway segmentation precision. The hierarchical masks were introduced to ensure that the HMSAU-Net captured sufficient local semantic information.

RESULTS

We used Dice to evaluate the performance of HMSAU-Net and used diagnostic method indicators to test the performance of 3D-ResNet. The average Dice value of our proposed model was 0.960, which was superior to the 3DU-Net and SAU-Net models. In the diagnostic models, 3D-ResNet10 had an excellent ability to diagnose adenoid hypertrophy automatically with a mean accuracy of 0.912, a mean sensitivity of 0.976, a mean specificity of 0.867, a mean positive predictive value of 0.837, a mean negative predictive value of 0.981, and a F1 score of 0.901.

CONCLUSIONS

The value of this diagnostic system lies in that it provides a new method for the rapid and accurate early clinical diagnosis of adenoid hypertrophy in children, allows us to look at the upper airway obstruction in three-dimensional space and relieves the work pressure of imaging doctors.

Shape modelling of the oropharynx distinguishes associations with body morphology but not whiplash-associated disorder

Characterization of the oropharynx, a subdivision of the pharynx between the soft palate and the epiglottis, is limited to simple measurements. Structural changes in the oropharynx in whiplash-associated disorder (WAD) cohorts have been quantified using two-dimensional (2D) and three-dimensional (3D) measures but the results are inconsistent. Statistical shape modelling (SSM) may be a more useful tool for systematically comparing morphometric features between cohorts. This technique has been used to quantify the variability in boney and soft tissue structures, but has not been used to examine a hollow cavity such as the oropharynx. The primary aim of this project was to examine the utility of SSM for comparing the oropharynx between WAD cohorts and control; and WAD severity cohorts. The secondary aim was to determine whether shape is associated with sex, height, weight and neck length. Magnetic resonance (MR) T1-weighted images were obtained from healthy control (n = 20), acute WAD (n = 14) and chronic WAD (n = 14) participants aged 18-39 years. Demographic, WAD severity (neck disability index) and body morphometry data were collected from each participant. Manual segmentation of the oropharynx was undertaken by blinded researchers between the top of the soft palate and tip of the epiglottis. Digital 3D oropharynx models were constructed from the segmented images and principal component (PC) analysis was performed with the PC weights normalized to z-scores for consistency. Statistical analyses were undertaken using multivariate linear models. In the first statistical model the independent variable was group (acute WAD, chronic WAD, control); and in the second model the independent variable was WAD severity (recovered/mild, moderate/severe). The covariates for both models included height, weight, average neck length and sex. Shape models were constructed to visualize the effect of perturbing these covariates for each relevant mode. The shape model revealed five modes which explained 90% of the variance: mode 1 explained 59% of the variance and primarily described differences in isometric size of the oropharynx, including elongation; mode 2 (13%) primarily described lateral (width) and AP (depth) dimensions; mode 3 (8%) described retroglossal AP dimension; mode 4 (6%) described lateral dimensions at the retropalatal-retroglossal junction and mode 5 (4%) described the lateral dimension at the inferior retroglossal region. There was no difference in shape (mode 1 p = 0.52; mode 2 p = 0.96; mode 3 p = 0.07; mode 4 p = 0.54; mode 5 p = 0.74) between control, acute WAD and chronic WAD groups. There were no statistical differences for any mode (mode 1 p = 0.12; mode 2 p = 0.29; mode 3 p = 0.56; mode 4 p = 0.99; mode 5 p = 0.96) between recovered/mild and moderate/severe WAD. Sex was not significant in any of the models but for mode 1 there was a significant association with height (p = 0.007), mode 2 neck length (p = 0.044) and in mode 3 weight (p = 0.027). Although SSM did not detect differences between WAD cohorts, it did detect associations with body morphology indicating that it may be a useful tool for examining differences in the oropharynx.

Assessment of the accuracy of imaging software for 3D rendering of the upper airway, usable in orthodontic and craniofacial clinical settings

BACKGROUND

Several semi-automatic software are available for the three-dimensional reconstruction of the airway from DICOM files. The aim of this study was to evaluate the accuracy of the segmentation of the upper airway testing four free source and one commercially available semi-automatic software. A total of 20 cone-beam computed tomography (CBCT) were selected to perform semi-automatic segmentation of the upper airway. The software tested were Invesalius, ITK-Snap, Dolphin 3D, 3D Slicer and Seg3D. The same upper airway models were manually segmented (Mimics software) and set as the gold standard (GS) reference of the investigation. A specific 3D imaging technology was used to perform the superimposition between the upper airway model obtained with semi-automatic software and the GS model, and to perform the surface-to-surface matching analysis. The accuracy of semi-automatic segmentation was evaluated calculating the volumetric mean differences (mean bias and limits of agreement) and the percentage of matching of the upper airway models compared to the manual segmentation (GS). Qualitative assessments were performed using color-coded maps. All data were statistically analyzed for software comparisons.

RESULTS

Statistically significant differences were found in the volumetric dimensions of the upper airway models and in the matching percentage among the tested software (p < 0.001). Invesalius was the most accurate software for 3D rendering of the upper airway (mean bias = 1.54 cm³; matching = 90.05%) followed by ITK-Snap (mean bias =  - 2.52 cm³; matching = 84.44%), Seg 3D (mean bias = 3.21 cm³, matching = 87.36%), 3D Slicer (mean bias =  - 4.77 cm³; matching = 82.08%) and Dolphin 3D (difference mean =  - 6.06 cm³; matching = 78.26%). According to the color-coded map, the dis-matched area was mainly located at the most anterior nasal region of the airway. Volumetric data showed excellent inter-software reliability (GS vs semi-automatic software), with coefficient values ranging from 0.904 to 0.993, confirming proportional equivalence with manual segmentation.

CONCLUSION

Despite the excellent inter-software reliability, different semi-automatic segmentation algorithms could generate different patterns of inaccuracy error (underestimation/overestimation) of the upper airway models. Thus, is unreasonable to expect volumetric agreement among different software packages for the 3D rendering of the upper airway anatomy.