Reduced Tongue Mobility: An Unrecognised Risk Factor of Childhood Obstructive Sleep Apnoea

Study Objectives Childhood obstructive sleep apnoea (OSA) is an important and prevalent disease. A short lingual frenulum is a risk factor for OSA, but whether tongue mobility also plays a role in OSA aetiology remains unknown. This study aimed to examine tongue mobility in children with and without OSA. We hypothesized that reduced tongue mobility was associated with OSA. We also evaluated the relationship between tongue mobility and craniofacial profile. Methods This was a cross-sectional case-control study. Prepubertal Chinese children aged 5-12 years, suspected to have OSA were recruited from our sleep disorder clinic. All subjects underwent overnight polysomnography (PSG). The lingual frenulum was evaluated based on tongue mobility and free tongue length. Craniofacial measurements were assessed by lateral cephalometry. Results Eighty-two subjects (mean age: 8.32 ± 1.70 years, 57 males) were recruited. The mean tongue mobility was 58.2 (±19)% and 67.4 (±15)% (p=0.019) in subjects with and without OSA, respectively. Tongue mobility was inversely correlated with OAHI (r=-0.218, p=0.049). In multivariate logistic regression, low tongue mobility was independently associated with a higher risk of OSA after adjustment for age, sex, BMI z-score, presence of large tonsils and turbinates and nocturnal oral breathing (odds ratio=3.65, 95% CI= 1.22-11.8). Tongue mobility was found to correlate with the cranial base angle (Ba-S-N) (r=0.262, p=0.018), which determines the relative position of the mandible. Conclusions In pre-pubertal children, reduced tongue mobility is associated with the occurrence and severity of OSA. Assessing tongue mobility is recommended in childhood OSA management.

Automated Adenoid Hypertrophy Assessment with Lateral Cephalometry in Children Based on Artificial Intelligence

Adenoid hypertrophy may lead to pediatric obstructive sleep apnea and mouth breathing. The routine screening of adenoid hypertrophy in dental practice is helpful for preventing relevant craniofacial and systemic consequences. The purpose of this study was to develop an automated assessment tool for adenoid hypertrophy based on artificial intelligence. A clinical dataset containing 581 lateral cephalograms was used to train the convolutional neural network (CNN). According to Fujioka’s method for adenoid hypertrophy assessment, the regions of interest were defined with four keypoint landmarks. The adenoid ratio based on the four landmarks was used for adenoid hypertrophy assessment. Another dataset consisting of 160 patients’ lateral cephalograms were used for evaluating the performance of the network. Diagnostic performance was evaluated with statistical analysis. The developed system exhibited high sensitivity (0.906, 95% confidence interval [CI]: 0.750–0.980), specificity (0.938, 95% CI: 0.881–0.973) and accuracy (0.919, 95% CI: 0.877–0.961) for adenoid hypertrophy assessment. The area under the receiver operating characteristic curve was 0.987 (95% CI: 0.974–1.000). These results indicated the proposed assessment system is able to assess AH accurately. The CNN-incorporated system showed high accuracy and stability in the detection of adenoid hypertrophy from children’ lateral cephalograms, implying the feasibility of automated adenoid hypertrophy screening utilizing a deep neural network model.

Evaluation of accuracy and reliability of OneCeph digital cephalometric analysis in comparison with manual cephalometric analysis—a cross-sectional study

Introduction Lateral cephalometric analysis continues to be one of the gold standard diagnostic aids in orthodontics, with various software available to enhance this. Aim This study was done to evaluate the accuracy and reliability of linear and angular measurements obtained from OneCeph digital cephalometric tracing and manual tracings in lateral cephalometry. Methodology This is a cross-sectional study done on twenty pre-treatment lateral cephalometric radiographs of subjects who reported to the postgraduate orthodontic clinic for orthodontic treatment over one month. Cephalometric tracings were done using OneCeph digital software and manual tracing method to evaluate nine parameters of Steiner’s cephalometric analysis. An Independent T-sample test was done between the mean values of manual and OneCeph tracing. Intra operator reliability was evaluated by paired T-test after a week. Results No significant statistical difference was observed as the p-value was greater than 0.05 for all the parameters in the two groups. Conclusion The reliability and accuracy of OneCeph software application was found to be at par with manual cephalometric tracing

Lateral Cephalometric Measurement of Skeletal Class III malocclusion Patients with Uncertainty

The purpose of this study was to calculate uncertainty of orthodontic measurement in skeletal class III malocclusion children using lateral cephalometry analysis software which obtained traceability in previous study. Using this data, standard reference of measurement value for skeletal class III malocclusion was obtained.<br/>Lateral cephalometric data was collected from 144 children who visited Pediatric Dentistry from 2017 to 2020 for orthodontic treatment. Orthodontic measurement was analyzed with software which obtained traceability. Type A evaluation of uncertainty and type B evaluation of uncertainty was calculated to obtain combined standard uncertainty and expanded uncertainty. Standard reference of skeletal class III children was compared to standard reference of skeletal class I children.<br/>Distribution range for skeletal class III malocclusion children aged 6 to 10 with 95% confidence interval was provided using calculated uncertainty of orthodontic measurement value.

Description of the sagittal jaw relation in cephalometric analysis - a review of the literature

Introductionː Lateral cephalometry is commonly used to analyse craniofacial morphology, soft tissue profile and the direction of facial growth in an attempt to predict the possibilities and limits of orthodontic therapy.The aim of the study was to present the most frequently used cephalometric measurements to assess the skeletal class on a lateral cephalometric headfilm.Materials and methods: A Pubmed search was undertaken to systematize information on ANB angle, WITS appraisal, APDI and Harvold measurements. The keywords used were “cephalometry AND evaluation of sagittal malocclusion AND reference lines AND accuracy OR ANB angle OR WITS appraisal ORAPDI”. A manual search was performed from the reference lists of studies found in order to identify and include pioneer studies.Results: A total number of 1258 records were found and finally 23 studies were included in the review. Four of them were descriptive, the other 19 were randomized control trials.Conclusionsː 1. ANB angle cannot be used as the only indicator of sagittal skeletal discrepancy. 2. WITS appraisal is independent of the variability of cranial base structures, thus may be an important supplement to the diagnosis, although it depends on the variability of the occlusal plane. 3. APDI can reliably distinguish between class I, II and III malocclusions.Keywords: ANB; WITS; AF–BF; AH–BH; APDI.

Effect of Location of Genioplasty Osteotomy Line on Hard- and Soft-Tissue Contour of the Chin and Related Complications

AIM: This study aimed to assess the effect of the location of the genioplasty osteotomy line on hard- and soft-tissue contour of the chin and the occurrence of irregularity in the inferior border of the mandible. METHODS: In this retrospective cohort study, 20 patients who had undergone osseous genioplasty were divided into two groups with (A) osteotomy line at the premolar site and (B) osteotomy line at the molar site and were evaluated for irregularity in the inferior border of the mandible. Assessments were made using lateral cephalometry, panoramic radiography, and clinical examinations. Patients were also asked about their level of satisfaction with the outcome. RESULTS: Radiographically, 70% of patients in Group A and 40% of those in Group B had an irregularity in the inferior border of the mandible (p > 0.05). Palpation revealed that 70% in Group A and 60% in Group B had an irregularity in the inferior border of the mandible (p > 0.05). Inspection revealed such irregularity in 80% of patients in Group A and 20% in Group B (p < 0.05). Overall, 60% in Group A and 90% in Group B were satisfied with the results of genioplasty and did not mention anything about the occurrence of this complication (p > 0.05). CONCLUSIONS: Genioplasty with an osteotomy line at the molar site (compared to premolar site) had a lower incidence of the irregularity of the inferior border of the mandible and yielded higher patient satisfaction with the outcome. PRACTICAL IMPLICATIONS: Irregularity in the inferior border of mandible following conventional genioplasty is a common finding on radiographic and clinical examinations.

Automated Skeletal Classification with Lateral Cephalometry Based on Artificial Intelligence

Lateral cephalometry has been widely used for skeletal classification in orthodontic diagnosis and treatment planning. However, this conventional system, requiring manual tracing of individual landmarks, contains possible errors of inter- and intravariability and is highly time-consuming. This study aims to provide an accurate and robust skeletal diagnostic system by incorporating a convolutional neural network (CNN) into a 1-step, end-to-end diagnostic system with lateral cephalograms. A multimodal CNN model was constructed on the basis of 5,890 lateral cephalograms and demographic data as an input. The model was optimized with transfer learning and data augmentation techniques. Diagnostic performance was evaluated with statistical analysis. The proposed system exhibited >90% sensitivity, specificity, and accuracy for vertical and sagittal skeletal diagnosis. Clinical performance of the vertical classification showed the highest accuracy at 96.40 (95% CI, 93.06 to 98.39; model III). The receiver operating characteristic curve and the area under the curve both demonstrated the excellent performance of the system, with a mean area under the curve >95%. The heat maps of cephalograms were also provided for deeper understanding of the quality of the learned model by visually representing the region of the cephalogram that is most informative in distinguishing skeletal classes. In addition, we present broad applicability of this system through subtasks. The proposed CNN-incorporated system showed potential for skeletal orthodontic diagnosis without the need for intermediary steps requiring complicated diagnostic procedures.

Gender-specific cephalometric features related to obesity in sleep apnea patients: trilogy of soft palate-mandible-hyoid bone

Background The aim of this study is to investigate the relationship between gender-specific and obesity-related airway anatomy in patients with obstructive sleep apnea (OSA) by using cephalometric analyses. Methods We retrospectively evaluated 206 patients with suspected OSA undergoing polysomnography and anthropometric measurements such as body mass index, neck circumference, and waist-hip ratio. We checked lateral cephalometry to measure tissue landmarks including angle from A point to nasion to B point (ANB), soft palate length (SPL), soft palate thickness (SPT), retropalatal space (RPS), retrolingual space (RLS), and mandibular plane to hyoid (MPH). Results Male with OSA showed significantly increased SPL (P = .006) compared with controls. SPL and MPH had significant correlation with apnea-hypopnea index (AHI) and central obesity. Female with OSA showed significantly increased ANB (P = .013) and SPT (P = .004) compared with controls. The receiver operating characteristic curves revealed that SPT in male and ANB and SPT in female were significant in model 1 (AHI ≥ 5) and model 2 (AHI ≥ 15). MPH was also significant for male in model 2. Conclusion Male and female with OSA had distinct anatomic features of the upper airway and different interactions among soft palate, mandible, and hyoid bone.