The use of CBCT in orthodontics with special focus on upper airway analysis in patients with sleep-disordered breathing.

Applications of cone-beam CT (CBCT) in orthodontics have been increasingly discussed and evaluated in science and practice over the last two decades. The present work provides a comprehensive summary of current consolidated practice guidelines, cutting-edge innovative applications, and future outlooks about potential use of CBCT in orthodontics with a special focus on upper airway analysis in patients with sleep-disordered breathing. The present scoping review reveals that clinical applications of CBCT in orthodontics are broadly supported by evidence for the diagnosis of dental anomalies, temporomandibular joint disorders, and craniofacial malformations. On the other hand, CBCT imaging for upper airway analysis-including soft tissue diagnosis and airway morphology-needs further validation in order to provide better understanding regarding which diagnostic questions it can be expected to answer. Internationally recognized guidelines for CBCT use in orthodontics are existent, and similar ones should be developed to provide clear indications about the appropriate use of CBCT for upper airway assessment, including a list of specific clinical questions justifying its prescription.

Wavelet transform and deep learning-based obstructive sleep apnea detection from single-lead ECG signals.

Sleep apnea is a common sleep disorder. Traditional testing and diagnosis heavily rely on the expertise of physicians, as well as analysis and statistical interpretation of extensive sleep testing data, resulting in time-consuming and labor-intensive processes. To address the problems of complex feature extraction, data imbalance, and low model capacity, we proposed an automatic sleep apnea classification model (CA-EfficientNet) based on the wavelet transform, a lightweight neural network, and a coordinated attention mechanism. The signal is converted into a time-frequency image by wavelet transform and put into the proposed model for classification. The effects of input time window, wavelet transform type and data balancing on the classification performance are considered, and a cost-sensitive algorithm is introduced to more accurately distinguish between normal and abnormal breathing events. PhysioNet apnea ECG database was used for training and evaluation. The 3-min Frequency B-Spline wavelets transform of ECG signal was carried out, and Dice Loss was used to train the classification model of sleep breathing. The classification accuracy was 93.44%, sensitivity was 88.9%, specificity was 96.2% and most indexes were better than other related work.

Associations between body mass index, sleep-disordered breathing, brain structure, and behavior in healthy children.

Pediatric overweight/obesity can lead to sleep-disordered breathing (SDB), abnormal neurological and cognitive development, and psychiatric problems, but the associations and interactions between these factors have not been fully explored. Therefore, we investigated the associations between body mass index (BMI), SDB, psychiatric and cognitive measures, and brain morphometry in 8484 children 9-11 years old using the Adolescent Brain Cognitive Development dataset. BMI was positively associated with SDB, and both were negatively correlated with cortical thickness in lingual gyrus and lateral orbitofrontal cortex, and cortical volumes in postcentral gyrus, precentral gyrus, precuneus, superior parietal lobule, and insula. Mediation analysis showed that SDB partially mediated the effect of overweight/obesity on these brain regions. Dimensional psychopathology (including aggressive behavior and externalizing problem) and cognitive function were correlated with BMI and SDB. SDB and cortical volumes in precentral gyrus and insula mediated the correlations between BMI and externalizing problem and matrix reasoning ability. Comparisons by sex showed that obesity and SDB had a greater impact on brain measures, cognitive function, and mental health in girls than in boys. These findings suggest that preventing childhood obesity will help decrease SDB symptom burden, abnormal neurological and cognitive development, and psychiatric problems.

Association of Polysomnographic Sleep Parameters With Neuroimaging Biomarkers of Cerebrovascular Disease in Older Adults With Sleep Apnea.

BACKGROUND AND OBJECTIVES: Our objective was to determine whether polysomnographic (PSG) sleep parameters are associated with neuroimaging biomarkers of cerebrovascular disease (CVD) related to white matter (WM) integrity in older adults with obstructive sleep apnea (OSA). METHODS: From the population-based Mayo Clinic Study of Aging, we identified participants without dementia who underwent at least 1 brain MRI and PSG. We quantified 2 CVD biomarkers: WM hyperintensities (WMHs) from fluid-attenuated inversion recovery (FLAIR)-MRI, and fractional anisotropy of the genu of the corpus callosum (genu FA) from diffusion MRI. For this cross-sectional analysis, we fit linear models to assess associations between PSG parameters (NREM stage 1 percentage, NREM stage 3 percentage [slow-wave sleep], mean oxyhemoglobin saturation, and log of apnea-hypopnea index [AHI]) and CVD biomarkers (log of WMH and log of genu FA), respectively, while adjusting for age (at MRI), sex, APOE ε4 status, composite cardiovascular and metabolic conditions (CMC) score, REM stage percentage, sleep duration, and interval between MRI and PSG. RESULTS: We included 140 participants with FLAIR-MRI (of which 103 had additional diffusion MRI). The mean ± SD age was 72.7 ± 9.6 years at MRI with nearly 60% being men. The absolute median (interquartile range [IQR]) interval between MRI and PSG was 1.74 (0.9-3.2) years. 90.7% were cognitively unimpaired (CU) during both assessments. For every 10-point decrease in N3%, there was a 0.058 (95% CI 0.006-0.111, p = 0.030) increase in the log of WMH and 0.006 decrease (95% CI -0.012 to -0.0002, p = 0.042) in the log of genu FA. After matching for age, sex, and N3%, participants with severe OSA had higher WMH (median [IQR] 0.007 [0.005-0.015] vs 0.006 [0.003-0.009], p = 0.042) and lower genu FA (median [IQR] 0.57 [0.55-0.63] vs 0.63 [0.58-0.65], p = 0.007), when compared with those with mild/moderate OSA. DISCUSSION: We found that reduced slow-wave sleep and severe OSA were associated with higher burden of WM abnormalities in predominantly CU older adults, which may contribute to greater risk of cognitive impairment, dementia, and stroke. Our study supports the association between sleep depth/fragmentation and intermittent hypoxia and CVD biomarkers. Longitudinal studies are required to assess causation.

Comparison of the pharyngeal airway in snoring and non-snoring patients based on the lateral cephalometric study: A case-control study.

BACKGROUND: Normal airways are a key factor during the craniofacial growth of the young. Therefore, sleep-disordered breathing (SDB) without treatment can have harmful consequences for development and health. OBJECTIVES: This study aimed to evaluate the cephalometric characteristics in non-snoring individuals and snoring subjects, and investigate differences in the pharyngeal airway space between the 2 groups. MATERIAL AND METHODS: This case-control study included 70 patients aged over 18 years, selected from a radiology center. The patients were divided into 2 groups: case (35 patients with a history of habitual snoring); and control (35 healthy patients). The Berlin sleep questionnaire was administered to the parents of the patients. The nasopharyngeal airway was measured according to the analysis of Linder-Aronson (1970), and 4 indices were measured and analyzed in each of the lateral cephalometric radiographs. RESULTS: No statistically significant differences were observed in the pharyngeal measurements between the 2 groups, although all means in the control group were higher than in the experimental group. However, there was a significant relationship between gender and the Ba-S-PNS and PNS-AD2 indices. CONCLUSIONS: Although the patients with nocturnal snoring had smaller airway dimensions, their pharyngeal measurements were not significantly different from the control group.

Diagnosis of Heart Failure Complicated with Sleep Apnea Syndrome by Thoracic Computerized Tomography under Artificial Intelligence Algorithm.

The aim of this study was to explore the application effect of thoracic computerized tomography (CT) under single threshold segmentation algorithm in the diagnosis of heart failure (HF) complicated with sleep apnea syndrome. 30 patients diagnosed with HF complicated with sleep apnea syndrome were chosen for the research. Another 30 patients without sleep apnea syndrome were selected as the control group, whose age, height, and weight were similar to those of the experimental group. Then, a model for thoracic CT image segmentation was proposed under the single threshold segmentation algorithm, and the faster region convolutional neural network (Faster RCNN) was applied to label the thoracic respiratory lesions. All the patients underwent thoracic CT examination, and the obtained images were processed using the algorithm model above. After that, the morphology of the patient's respiratory tract after treatment was observed. The results suggested that the improved single threshold segmentation algorithm was effective for the image segmentation of patient lesions, and the Faster RCNN could effectively finish the labeling of the lesion area in the CT image. The classification accuracy of the Faster RCNN was about 0.966, and the loss value was about 0.092. With CT scanning under the algorithm, it was found that the airway collapse of the posterior palatal area, retrolingual area, and laryngopharyngeal area of the sleep apnea syndrome patients was significantly greater than that of the control group (P < 0.05). But there was no significant difference of the collapse of the nasopharyngeal area between the two groups (P > 0.05). The single threshold segmentation algorithm had a better segmentation accuracy for thoracic CT images in patients with HF and sleep apnea syndrome, so it had a highly promising application prospect in the diagnosis of the disease.

Airways and craniofacial assessment in children affected by achondroplasia with and without sleep-disordered breathing: quantitative magnetic resonance study.

PURPOSE: To identify MRI-based quantitative craniofacial variables linked to airways narrowing and obstructive sleep apnea (OSA) development in children with achondroplasia. METHODS: We evaluated skull base and midface MRI in two cohorts of children affected by achondroplasia, with (group 1) or without OSA (group 2). 3DFSPGR-T1weighted images were used to assess airways volume (nasopharynx, oropharynx, and laryngopharynx), jugular foramina (JF) and hypoglossal foramina (HF) areas, foramen magnum area, cervical cord area, and maxillary retrusion (SNA angle). RESULTS: Nineteen out of 27 children with achondroplasia exhibited different degrees of obstructive respiratory impairment (n.4 mild, n.8 moderate, n.7 severe), while 8 children did not show OSA. Each group was compared with age-matched controls without neuroimaging abnormalities. Both groups showed reduced nasopharynx volume, JF areas, and SNA angle, while group 1 showed also reduced oropharynx volume, ratio of FM/cervical cord areas, and HF areas (p < 0.05). A positive correlation between nasopharynx volume and SNA angle was found in both groups, while a positive correlation among upper airways volume, JF and HF areas was found only in group 1. No correlation between upper airways volume and OSA severity was found. CONCLUSION: In children with achondroplasia, multifaced craniofacial abnormalities contribute to airways volume reduction predisposing to sleep disordered breathing. MRI-based quantitative assessment allows the appraisal of craniofacial variables linked to the development of sleep-disordered breathing such as FM stenosis, jugular and hypoglossal foramina stenosis, and retruded maxillary position and may be a valuable tool for clinical surveillance.

Maternal sleep disordered breathing assessed by Epworth Sleepiness Scale and abnormal feto-placental Dopplers.

OBJECTIVE: Excessive daytime sleepiness is a frequently described phenomenon in pregnant women. The Epworth Sleepiness Scale (ESS) is a self reported standardized method of assessing sleep propensity and has been used extensively within pregnant populations. An elevated score is associated with sleep disordered breathing, as well as adverse obstetric and neonatal outcomes which may be indicative of a degree of placental dysfunction. Thus the aim of this study was to prospectively assess women using the Epworth questionnaire in conjunction with an ultrasound in both the second and third trimesters to determine if there was a difference in ESS scores across gestation and if a mid or late gestation assessment was correlated with Doppler ultrasound measures of fetal well-being. MATERIALS AND METHODS: Participants were prospectively recruited from a tertiary obstetric hospital and completed both an Epworth questionnaire and ultrasound examination in the second and third trimesters. RESULTS: A total of 302 women took part in this cohort study. There was a statistically significant (p = .02) increase in ESS score across gestation. There was however no correlation identified in either the second or third trimester between ESS score and umbilical artery pulsatility index, middle cerebral artery pulsatility index, cerebroplacental ratio, umbilical venous flow, uterine artery pulsatility index or estimated fetal weight. A higher birth weight is associated with a higher ESS score in the second trimester but not in the third trimester (p = .03). CONCLUSIONS: Maternal sleep disordered breathing assessed by the ESS score is only correlated with increased birth weight but not with fetal Doppler parameters in low risk pregnancies.

Sleep Duration, Sleep Apnea, and Gray Matter Volume.

This study aimed to evaluate the effect of sleep duration on brain structures in the presence versus absence of sleep apnea in middle-aged and older individuals. The study investigated a population-based sample of 2,560 individuals, aged 49-80 years. The presence of sleep apnea and self-reported sleep duration were examined in relation to gray matter volume (GMV) in total and lobar brain regions. We identified ranges of sleep duration associated with maximal GMV using quadratic regression and bootstrap sampling. A significant quadratic association between sleep duration and GMV was observed in total and lobar brain regions of men with sleep apnea. In the fully adjusted model, optimal sleep durations associated with peak GMV between brain regions ranged from 6.7 to 7.0 hours. Shorter and longer sleep durations were associated with lower GMV in total and 4 sub-regions of the brain in men with sleep apnea.

Predictors for carotid and femoral artery intima-media thickness in a non-diabetic sleep clinic cohort.

INTRODUCTION: The impact of sleep disordered breathing (SDB) on arterial intima-media thickness (IMT), a surrogate measure for cardiovascular disease, remains uncertain, in part because of the potential for non-SDB vascular risk factor interactions. In the present study, we determined predictors for common carotid (CCA) and femoral (CFA) artery IMT in an adult, sleep clinic cohort where non-SDB vascular risk factors (particularly diabetes) were eliminated or controlled. METHODS: We recruited 296 participants for polysomnography (standard SDB severity metrics) and CCA/CFA ultrasound examinations, followed by a 12 month vascular risk factor minimisation (RFM) and continuous positive pressure (CPAP) intervention for participants with a range of SDB severity (RFM Sub-Group, n = 157; apnea hyponea index [AHI]: 14.7 (7.2-33.2), median [IQR]). Univariable and multivariable linear regression models determined independent predictors for IMT. Linear mixed effects modelling determined independent predictors for IMT change across the intervention study. P<0.05 was considered significant. RESULTS: Age, systolic blood pressure and waist:hip ratio were identified as non-SDB predictive factors for CCA IMT and age, weight and total cholesterol:HDL ratio for CFA IMT. No SDB severity metric emerged as an independent predictor for either CCA or CFA IMT, except in the RFM Sub-Group, where a 2-fold increase in AHI predicted a 2.4% increase in CFA IMT. Across the intervention study, CCA IMT decreased in those who lost weight, but there was no CPAP use interaction. CFA IMT, however, decreased by 12.9% (95%CI 6.8, 18.7%, p = 0.001) in those participants who both lost weight and used CPAP > = 4hours/night. CONCLUSION: We conclude that SDB severity has little impact on CCA IMT values when non-SDB vascular risk factors are minimised or not present. This is the first study, however, to suggest a potential linkage between SDB severity and CFA IMT values. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry, ACTRN12611000250932 and ACTRN12620000694910.

Estimation of Apnea-Hypopnea Index Using Deep Learning On 3-D Craniofacial Scans.

Obstructive sleep apnea (OSA) is characterized by decreased breathing events that occur through the night, with severity reported as the apnea-hypopnea index (AHI), which is associated with certain craniofacial features. In this study, we used data from 1366 patients collected as part of Stanford Technology Analytics and Genomics in Sleep (STAGES) across 11 US and Canadian sleep clinics and analyzed 3D craniofacial scans with the goal of predicting AHI, as measured using gold standard nocturnal polysomnography (PSG). First, the algorithm detects pre-specified landmarks on mesh objects and aligns scans in 3D space. Subsequently, 2D images and depth maps are generated by rendering and rotating scans by 45-degree increments. Resulting images were stacked as channels and used as input to multi-view convolutional neural networks, which were trained and validated in a supervised manner to predict AHI values derived from PSGs. The proposed model achieved a mean absolute error of 11.38 events/hour, a Pearson correlation coefficient of 0.4, and accuracy for predicting OSA of 67% using 10-fold cross-validation. The model improved further by adding patient demographics and variables from questionnaires. We also show that the model performed at the level of three sleep medicine specialists, who used clinical experience to predict AHI based on 3D scan displays. Finally, we created topographic displays of the most important facial features used by the model to predict AHI, showing importance of the neck and chin area. The proposed algorithm has potential to serve as an inexpensive and efficient screening tool for individuals with suspected OSA.

Sleep-disordered breathing and its management in children with rare skeletal dysplasias.

Sleep-disordered breathing (SDB) is common in patients with skeletal dysplasias. The aim of our study was to analyze SDB and respiratory management in children with rare skeletal dysplasias. We performed a retrospective analysis of patients with spondyloepiphyseal dysplasia congenita (SEDC), metatropic dysplasia (MD), spondyloepimetaphyseal dysplasia (SEMD), acrodysostosis (ADO), geleophysic dysplasia (GD), acromicric dysplasia (AD), and spondylocostal dysplasia (SCD) between April 2014 and October 2020. Polygraphic data, clinical management, and patients' outcome were analyzed. Thirty-one patients were included (8 SEDC, 3 MD, 4 SEMD, 1 ADO, 4 GD, 3 AD, and 8 SCD). Sixteen patients had obstructive sleep apnea (OSA): 11 patients (2 with SEDC, 1 with SEMD, 1 with ADO, 1 with GD, 2 with AD, and 4 with SCD) had mild OSA, 2 (1 SEMD and 1 GD) had moderate OSA, and 3 (1 SEDC, 1 MD, 1 SEMD) had severe OSA. Adenotonsillectomy was performed in one patient with SCD and mild OSA, and at a later age in two other patients with ADO and AD. The two patients with moderate OSA were treated with noninvasive ventilation (NIV) because of nocturnal hypoxemia. The three patients with severe OSA were treated with adenotonsillectomy (1 SEDC), adeno-turbinectomy and continuous positive airway pressure (CPAP; 1 MD), and with NIV (1 SEMD) because of nocturnal hypoventilation. OSA and/or alveolar hypoventilation is common in patients with skeletal dysplasias, underlining the importance of systematic screening for SDB. CPAP and NIV are effective treatments for OSA and nocturnal hypoventilation/hypoxemia.

Decoding Causal Links Between Sleep Apnea and Alzheimer's Disease.

Obstructive sleep apnea (OSA) and Alzheimer's disease (AD) are two common chronic diseases with a well-documented association. Whether the association is causal has been highlighted by recent evidence reporting a neurobiological link between these disorders. This narrative review discusses the brain regions and networks involved in OSA as potential vulnerable areas for the development of AD neuropathology with a particular focus on gender-related implications. Using a neuroimaging perspective supported by neuropathological investigations, we provide a new model of neurodegeneration common to OSA and AD, that we have called OSA-AD neurodegeneration in order to decode the causal links between these two chronic conditions.

Relating three-dimensional airway measurements to the apnea-hypopnea index in pediatric sleep apnea patients.

OBJECTIVES: (a) To evaluate three-dimensional radiographic airway analysis as it relates to the pre-test probability for sleep apnea in pediatric patients, and (b) to develop cut-off values for measurements showing promising results. SETTING AND SAMPLE POPULATION: A consecutive series of pediatric patients between the ages of 7 and 17 years, referred for a sleep study were recruited. Cone beam computed tomography (CBCT) scans were acquired for 103 subjects within one month following the sleep study. METHODS: Three-dimensional airway analysis was performed including volumetric, area and linear measurements. Correlations with the apnea-hypopnea index (AHI) and receiver operating characteristic (ROC) curves were constructed. Sensitivity and specificity were calculated for prediction of AHI ≥ 5 and AHI ≥ 10. RESULTS: 99 CBCT scans were included (median age = 11 years). The nasopharyngeal volume (NPV) significantly correlated with AHI (rho≈-0.4, P < .05). In subjects aged 7-11 years, proposed cut-off values for NPV are 2400mm3 and 1600mm3 for AHI ≥ 5 and AHI ≥ 10, respectively. In subjects aged 12-17 years, proposed cut-off values for NPV are 3500mm3 and 2700mm3 for AHI ≥ 5 and AHI ≥ 10, respectively. Oropharyngeal cross-sectional area (OCSA) demonstrated significant predictive value in ROC curve analysis, and cut-off values for this airway measure are also proposed. CONCLUSIONS: Contrary to findings in adults, the NPV shows promise when screening for sleep apnea in children when CBCT scans are available. The OCSA might also be of value when screening for sleep apnea especially in older children.

A radiologic parameter that could be applied in the development of sleep apnea in rheumatoid arthritis patients undergoing occipitocervical fusion.

BACKGROUND: The prevalence of sleep apnea in rheumatoid arthritis (RA) patients with occipitocervical lesions was 79%. Occipitocervical fusion (OCF) could incur sleep apnea or worsen this condition. Recent studies reported that this complication is caused by stenosis of the oropharyngeal airway accompanying a decrease in the occipitoaxial angle (O-C2a). However, there are several limitations to the application of the O-C2a, which decreases its effectiveness. Therefore, we aimed to evaluate the association between a new radiologic parameter, the CVT/NSL angle (CVT: craniocervical inclination in the second and fourth vertebrae; NSL: Nasion-Sella line), and sleep apnea in RA patients accepting OCF. METHODS: A total of 35 patients who underwent OCF due to upper cervical lesions secondary to RA and had sleep apnea before surgery were analyzed. Those who have a postoperative apnea-hypopnea index (AHI) < 15 and a ΔAHI ≥50% were considered "responders"; patients were otherwise considered "non-responders." They were analyzed whether pre- and postoperative radiologic parameters and their differences in plain lateral radiographs were correlated to the parameter related to sleep apnea. RESULTS: The included patients have a mean AHI of 21.9 (range, 10 to 52) before surgery. The mean postoperative CVT/NSLa, ΔCVT/NSLa, andΔO-C2a in complete responders were significantly greater compared with non-responders (p < 0.05). Both the changes in the CVT/NSLa and O-C2a were linearly correlated within patients. However, the R2 value for the CVT/NSLa was greater compared with the O-C2a (0.403 vs. 0.203). CONCLUSIONS: The usefulness of the new craniovertebral angle, CVT/NSLa, as an intraoperative indicator during OCF, is more valuable in comparison with the conventional method of measuring the O-C2a. Measuring the craniovertebral angle is extremely important in the planning of surgical treatment for the development of sleep apnea in rheumatoid arthritis patients undergoing occipitocervical fusion.