Pharyngeal airspace of asthmatic individuals and those suffering from obstructive sleep apnea syndrome: Study by CBCT

Radiographic findings predictive of obstructive sleep apnea in adults: A systematic review and meta-analysis

Polysomnography remains the diagnostic gold standard for obstructive sleep apnea (OSA), but it cannot be easily performed in a timely fashion within the practice of a craniomaxillofacial surgeon. Hence, in this systematic review and meta-analysis, we aimed to identify radiographic indicators that could predict obstructive sleep apnea (OSA) diagnosis. We conducted a PRISMA-compliant systematic review and meta-analysis, including 109 studies with 9817 participants (3509 controls, 6308 OSA patients), predominantly male (79% controls, 85% OSA patients). The analysis focused on CT (36, 33%), MRI (23, 21%), and lateral cephalogram findings (50, 46%). The average age and BMI for the included patients were 44.4 ± 14.4 years and 26.4 ± 5.2 kg/m2 for controls, and 51.5 ± 40.4 years and 29.8 ± 6.4 kg/m2 for the OSA group. A random-effects model meta-analysis was conducted on the measurements that met our criteria. Due to measurement differences between studies, only lateral cephalogram measurements could be included in the meta-analysis: OSA correlated with increased soft palate length and thickness, increased mandibular plane to hyoid bone distance, and decreased SNA, SNB, BaSN, SN distance, and palatal length (ANS-PNS). Although the study underscores radiographic utility for screening, PSG is necessary to establish a diagnosis of OSA.

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.

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.

Asthma and obstructive sleep apnoea in adults and children - an up-to-date review

Obstructive sleep apnoea (OSA) and asthma are two common respiratory disorders in children and adults. Apart from common risk factors, such as obesity, gastroesophageal reflux disease and allergic rhinitis, emerging evidence suggest that the two diseases may complicate the clinical course of each other. On one hand, OSA modifies asthmatic airway inflammation and is associated with poor asthma control. On the other hand, asthma and its medications increase the collapsibility of the upper airways contributing to the development and worsening of OSA. The overnight respiratory symptoms of OSA and asthma are often similar, and an inpatient polysomnography is often necessary for a proper diagnosis, especially in children. Continuous positive pressure, the gold standard treatment for OSA can improve asthma control in patients suffering from both diseases. However, there is limited evidence how anti-asthma medications act in the same patients. Nevertheless, adenotonsillectomy seems to be effective in children with concomitant asthma and OSA. This review summarises the evidence for the bidirectional link between asthma and OSA, focuses on diagnostic and therapeutic challenges and highlights the need for further research.

Allergies/asthma and root resorption: a systematic review

Background

This review synthesizes the available evidence about the predisposition of individuals with asthma or allergies to orthodontically induced inflammatory root resorption (OIIRR) and possible factors related to root resorption that were investigated in the included studies, such as the type of malocclusion, duration of orthodontic treatment, and tooth units.

Material and methods

Six electronic databases and partial gray literature were searched without date or language restrictions until September 2020. Prospective and retrospective observational cohort and case-control studies were included. The risk of bias (RoB) was assessed using the checklists from the Joanna Briggs Institute and the certainty of the evidence using the GRADE tool. To complement the case-control studies, the odds ratio (OR) of the individuals with allergies/asthma to develop root resorption was calculated.

Results

Six studies were included. One study with low RoB, one with moderate, and one with high RoB stated that allergic patients did not report a greater chance of developing OIIRR (OR = 1.17 to 2.10, p = 0.1 to 1), while only one study with low RoB reported that individuals with allergies tend to develop root resorption (OR = 2.4, 95% CI = 1.08-5.37). Three studies with low RoB and one with moderate showed no significant association between asthma and OIIRR (OR = 1.05 to 3.42, p = 0.12 to 0.94). No association was identified between the type of malocclusion and the degree of OIIRR. Uniradicular dental units and a prolonged treatment time seem to be associated with an increased risk of resorption. The certainty of the evidence was considered low for both exposure factors.

Conclusion

Evidence with a low level of certainty indicates that individuals with allergies or asthma are not more predisposed to OIIRR. Uniradicular teeth and long-term orthodontic treatments are associated with a higher risk of OIIRR.

Systematic review registration

PROSPERO CRD42020188463

Use of cone beam computed tomography imaging for airway measurement to predict obstructive sleep apnea

Objective: This retrospective chart review examined whether airway parameters were correlated with scores on the STOP-Bang questionnaireMethods: Minimal upper airway area, upper airway volume, minimal retropalatal area, retropalatal volume, minimal retroglossal area, and retroglossal volume were calculated from cone beam computed tomography (CBCT) images. Patients were grouped based on their STOP-Bang scores (<3 or ≥3) for obstructive sleep apnea (OSA), and airway parameters were compared across the 2 groups.Results: Thirty-one (43%) of 72 patients with a minimal upper airway area of <110 mm² had STOP-Bang scores of ≥3. Most patients (90%) with STOP-Bang scores of ≥3 had minimal retropalatal areas of <110 mm². Differences were found between groups for minimal upper airway area (P=.03), upper airway volume (P=.04), and minimal retropalatal area (P=.001).Discussion: To assess OSA risk, dentists should compare CBCT images with STOP-Bang scores.

Three-Dimensional Assessment of Pharyngeal Volume on Computed Tomography Scans: Applications to Anesthesiology and Endoscopy

Pharyngeal volume is important in anesthesiology for correctly assessing intubation procedures. However, most of studies are based on patients in upright position and do not assess possible relationships between pharyngeal volume and cranial size. This study aims at measuring pharyngeal volume in CT-scans and to assess possible statistically significant differences according to sex.Eighty healthy subjects (40 males and 40 females) aged between 21 and 86 years were retrospectively chosen from a hospital database of maxillofacial CT-scans; 3D segmentation was performed separately for naso-, oro- and laryngopharyngeal portion through ITK-SNAP software, and their volume was calculated. Three cranial measurements were obtained: distance between anterior and posterior nasal spine, upper facial height (nasion-prosthion) and biorbital breadth (ectoconchion-ectoconchion distance).The effect of sex on volume for each pharyngeal portion was assessed through one-way ANCOVA test using each of the 3 cranial measurements as covariate (P < 0.05).On average, the volume of nasopharynx, oropharynx and laryngopharynx was 7.2 ± 2.7 cm, 7.5 ± 4.2 cm, 3.5 ± 2.2 cm respectively in males, and 6.4 ± 2.9 cm, 5.2 ± 2.1 cm, 3.0 ± 1.8 cm in females. Statistically significant differences according to sex were found only for oropharyngeal volume, independently from cranial measurements (P < 0.05).This study provides data concerning volume of pharyngeal air space in supine subjects: these reference standards can be useful for anaesthesiologic procedures.

Obstructive sleep apnea and comorbidities: a dangerous liaison

Obstructive sleep apnea (OSA) is a highly prevalent disease, and is traditionally associated with increased cardiovascular risk. The role of comorbidities in OSA patients has emerged recently, and new conditions significantly associated with OSA are increasingly reported. A high comorbidity burden worsens prognosis, but some data suggest that CPAP might be protective especially in patients with comorbidities. Aim of this narrative review is to provide an update on recent studies, with special attention to cardiovascular and cerebrovascular comorbidities, the metabolic syndrome and type 2 diabetes, asthma, COPD and cancer. Better phenotypic characterization of OSA patients, including comorbidities, will help to provide better individualized care. The unsatisfactory adherence to CPAP in patients without daytime sleepiness should prompt clinicians to examine the overall risk profile of each patient in order to identify subjects at high risk for worse prognosis and provide the optimal treatment not only for OSA, but also for comorbidities.