Predicting sleep apnea from three-dimensional face photography

Two-dimensional facial photography for assessment of craniofacial morphology in sleep breathing disorders: a systematic review

PURPOSE

Craniofacial morphology is integral to Sleep Breathing Disorders (SBD), particularly Obstructive Sleep Apnea (OSA), informing treatment strategies. This review assesses the utility of two-dimensional (2D) photogrammetry in evaluating these metrics among OSA patients.

METHODS

Following PRISMA guidelines, a systematic review was conducted. PubMed, Embase, and Lilacs databases were systematically searched for studies utilizing 2D photography in SBD. Findings were narratively synthesized.

RESULTS

Thirteen studies involving 2,328 patients were included. Significant correlations were found between craniofacial measurements-specifically neck parameters and facial width-and OSA severity, even after BMI adjustment. Ethnic disparities in craniofacial morphology were observed, with photogrammetry effective in predicting OSA in Caucasians and Asians, though data for other ethnicities were limited. Pediatric studies suggest the potential of craniofacial measurements as predictors of childhood OSA, with certain caveats.

CONCLUSION

2D photogrammetry emerges as a practical and non-invasive tool correlating with OSA severity across diverse populations. However, further validation in various ethnic cohorts is essential to enhance the generalizability of these findings.

Correlation analysis of airway-facial phenotype in Crouzon syndrome by geometric morphometrics: A promising method for non-radiation airway evaluation

AIM

This study aimed to verify the correlation of the airway-facial phenotype and visualize the morphological variation in Crouzon syndrome patients. Additionally, to develop a non-radiation methodology for airway assessments.

METHOD

In this study, 22 patients diagnosed with Crouzon syndrome (Age: 7.80 ± 5.63 years; Gender distribution: 11 females and 11 males) were analysed. The soft tissue surface and airway were three-dimensionally reconstructed, and the entire facial phenotype was topologized and converted into spatial coordinates. Geometric morphometrics was employed to verify the correlation and visualize dynamic phenotypic variation associated with airway volume. A total of 276 linear variables were automatically derived from 24 anatomical landmarks, and principal component analysis (PCA) identified the 20 most significant parameters for airway evaluation. Correlation analyses between parameters and airway volume were performed. Then, patients were classified into three groups based on airway volume, and the differences among the groups were compared for evaluating the differentiating effectiveness of parameters.

RESULTS

The facial phenotype was strongly correlated with the airway (coefficient: 0.758). Morphological variation was characterized by (i) mandibular protrusion and anticlockwise rotation; (ii) midface retrusion; (iii) supraorbital frontward and (iv) lengthening of the facial height. All the anthropometric parameters were strongly associated with the airway, and the differences among the groups were statistically significant.

CONCLUSION

This study confirmed the strong correlation between facial phenotype and airway parameters in Crouzon syndrome patients. Despite the development of the airway, pathological midface retrusion was still aggravated, suggesting that surgical intervention was inevitable. Three-dimensional facial anthropometry has potential as a non-radiation examination for airway evaluation.

A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators

Objective

Obstructive sleep apnea (OSA) is a common and potentially fatal sleep disorder. The purpose of this study was to construct an objective and easy-to-promote model based on common clinical biochemical indicators and demographic data for OSA screening.

Methods

The study collected the clinical data of patients who were referred to the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University from December 1, 2020, to July 31, 2023, including data for demographics, polysomnography (PSG), and 30 biochemical indicators. Univariate and multivariate analyses were performed to compare the differences between groups, and the Boruta method was used to analyze the importance of the predictors. We selected and compared 10 predictors using 4 machine learning algorithms which were "Gaussian Naive Bayes (GNB)", "Support Vector Machine (SVM)", "K Neighbors Classifier (KNN)", and "Logistic Regression (LR)". Finally, the optimal algorithm was selected to construct the final prediction model.

Results

Among all the predictors of OSA, body mass index (BMI) showed the best predictive efficacy with an area under the receiver operating characteristic curve (AUC) = 0.699; among the predictors of biochemical indicators, triglyceride-glucose (TyG) index represented the best predictive performance (AUC = 0.656). The LR algorithm outperformed the 4 established machine learning (ML) algorithms, with an AUC (F1 score) of 0.794 (0.841), 0.777 (0.827), and 0.732 (0.788) in the training, validation, and testing cohorts, respectively.

Conclusion

We have constructed an efficient OSA screening tool. The introduction of biochemical indicators in ML-based prediction models can provide a reference for clinicians in determining whether patients with suspected OSA need PSG.

Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review

Obstructive sleep apnea (OSA) affects almost a billion people worldwide and is associated with a myriad of adverse health outcomes. Among the most prevalent and morbid are cardiovascular diseases (CVDs). Nonetheless, randomized controlled trials (RCTs) of OSA treatment have failed to show improvements in CVD outcomes. A major limitation in our field is the lack of precision in defining OSA and specifically subgroups with the potential to benefit from therapy. Further, this has called into question the validity of using the time-honored apnea-hypopnea index as the ultimate defining criteria for OSA. Recent applications of advanced statistical methods and machine learning have brought to light a variety of OSA endotypes and phenotypes. These methods also provide an opportunity to understand the interaction between OSA and comorbid diseases for better CVD risk stratification. Lastly, machine learning and specifically heterogeneous treatment effects modeling can help uncover subgroups with differential outcomes after treatment initiation. In an era of data sharing and big data, these techniques will be at the forefront of OSA research. Advanced data science methods, such as machine-learning analyses and artificial intelligence, will improve our ability to determine the unique influence of OSA on CVD outcomes and ultimately allow us to better determine precision medicine approaches in OSA patients for CVD risk reduction. In this narrative review, we will highlight how team science via machine learning and artificial intelligence applied to existing clinical data, polysomnography, proteomics, and imaging can do just that.

Frontiers in Three-Dimensional Surface Imaging Systems for 3D Face Acquisition in Craniofacial Research and Practice: An Updated Literature Review

Digitalizing all aspects of dental care is a contemporary approach to ensuring the best possible clinical outcomes. Ongoing advancements in 3D face acquisition have been driven by continuous research on craniofacial structures and treatment effects. An array of 3D surface-imaging systems are currently available for generating photorealistic 3D facial images. However, choosing a purpose-specific system is challenging for clinicians due to variations in accuracy, reliability, resolution, and portability. Therefore, this review aims to provide clinicians and researchers with an overview of currently used or potential 3D surface imaging technologies and systems for 3D face acquisition in craniofacial research and daily practice. Through a comprehensive literature search, 71 articles meeting the inclusion criteria were included in the qualitative analysis, investigating the hardware, software, and operational aspects of these systems. The review offers updated information on 3D surface imaging technologies and systems to guide clinicians in selecting an optimal 3D face acquisition system. While some of these systems have already been implemented in clinical settings, others hold promise. Furthermore, driven by technological advances, novel devices will become cost-effective and portable, and will also enable accurate quantitative assessments, rapid treatment simulations, and improved outcomes.

Predicting obstructive sleep apnea hypopnea syndrome using three-dimensional optical devices: A systematic review

Purpose

As a global health concern, the diagnosis of obstructive sleep apnea hypopnea syndrome (OSAHS), characterized by partial reductions and complete pauses in ventilation, has garnered significant scientific and public attention. With the advancement of digital technology, the utilization of three-dimensional (3D) optical devices demonstrates unparalleled potential in diagnosing OSAHS. This study aimed to review the current literature to assess the accuracy of 3D optical devices in identifying the prevalence and severity of OSAHS.

Methods

A systematic literature search was conducted in the Web of Science, Scopus, PubMed/MEDLINE, and Cochrane Library databases for English studies published up to April 2024. Peer-reviewed researches assessing the diagnostic utility of 3D optical devices for OSAHS were included. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) guideline was employed to appraise the risk of bias.

Results

The search yielded 3216 results, with 10 articles meeting the inclusion criteria for this study. Selected studies utilized structured light scanners, stereophotogrammetry, and red, green, blue-depth (RGB-D) cameras. Stereophotogrammetry-based 3D optical devices exhibited promising potential in OSAHS prediction.

Conclusions

The utilization of 3D optical devices holds considerable promise for OSAHS diagnosis, offering potential improvements in accuracy, cost reduction, and time efficiency. However, further clinical data are essential to assist clinicians in the early detection of OSAHS using 3D optical devices.

Deep learning technique to detect craniofacial anatomical abnormalities concentrated on middle and anterior of face in patients with sleep apnea

OBJECTIVES

The aim of this study is to propose a deep learning-based model using craniofacial photographs for automatic obstructive sleep apnea (OSA) detection and to perform design explainability tests to investigate important craniofacial regions as well as the reliability of the method.

METHODS

Five hundred and thirty participants with suspected OSA are subjected to polysomnography. Front and profile craniofacial photographs are captured and randomly segregated into training, validation, and test sets for model development and evaluation. Photographic occlusion tests and visual observations are performed to determine regions at risk of OSA. The number of positive regions in each participant is identified and their associations with OSA is assessed.

RESULTS

The model using craniofacial photographs alone yields an accuracy of 0.884 and an area under the receiver operating characteristic curve of 0.881 (95% confidence interval, 0.839-0.922). Using the cutoff point with the maximum sum of sensitivity and specificity, the model exhibits a sensitivity of 0.905 and a specificity of 0.941. The bilateral eyes, nose, mouth and chin, pre-auricular area, and ears contribute the most to disease detection. When photographs that increase the weights of these regions are used, the performance of the model improved. Additionally, different severities of OSA become more prevalent as the number of positive craniofacial regions increases.

CONCLUSIONS

The results suggest that the deep learning-based model can extract meaningful features that are primarily concentrated in the middle and anterior regions of the face.

Can smartphones be used for routine dental clinical application? A validation study for using smartphone-generated 3D facial images

OBJECTIVES

To compare the accuracy of smartphone-generated three-dimensional (3D) facial images to that of direct anthropometry (DA) and 3dMD with the aim of assessing the validity and reliability of smartphone-generated 3D facial images for routine clinical applications.

MATERIALS AND METHODS

Twenty-five anthropometric soft-tissue facial landmarks were labelled manually on 22 orthognathic surgery patients (11 males and 11 females; mean age 26.2 ± 5.3 years). For each labelled face, two imaging operations were performed using two different surface imaging systems: 3dMDface and Bellus3D FaceApp. Next, 42 inter-landmark facial measurements amongst the identified facial landmarks were measured directly on each labelled face and also digitally on 3D facial images. The measurements obtained from smartphone-generated 3D facial images (SGI) were statistically compared with those from DA and 3dMD.

RESULTS

SGI had slightly higher measurement values than DA and 3dMD, but there was no statistically significant difference between the mean values of inter-landmark measures across the three methods. Clinically acceptable differences (≤3 mm or ≤5°) were observed for 67 % and 74 % of measurements with good agreement between DA and SGI, and 3dMD and SGI, respectively. An overall small systematic bias of ± 0.2 mm was observed between the three methods. Furthermore, the mean absolute difference between DA and SGI methods was highest for linear (1.41 ± 0.33 mm) as well as angular measurements (3.07 ± 0.73°).

CONCLUSIONS

SGI demonstrated fair trueness compared to DA and 3dMD. The central region and flat areas of the face in SGI are more accurate. Despite this, SGI have limited clinical application, and the panfacial accuracy of the SGI would be more desirable from a clinical application standpoint.

CLINICAL SIGNIFICANCE

The usage of SGI in clinical practice for region-specific macro-proportional facial assessment involving central and flat regions of the face or for patient education purposes, which does not require accuracy within 3 mm and 5° can be considered.

Characterization of craniofacial-based clinical phenotypes in children with suspected obstructive sleep apnea

STUDY OBJECTIVES

We conducted this study to identify phenotypes of obstructive sleep apnea (OSA) in children based on lifestyle, sleep habits, age, obesity, sex, soft tissue facial features, and specific craniofacial abnormalities.

METHODS

Seventy-three children with symptoms of pediatric OSA who underwent overnight observed polysomnography participated in this study. Soft tissue facial features were assessed using a 3-dimensional stereophotogrammetric system. Craniofacial abnormalities were evaluated based on the most common facial features associated with orthodontic treatment needs. Data regarding lifestyle, sleep habits, age, obesity, and sex were also collected. To identify phenotypes of OSA, a sequential analysis was then performed on categories of variables using fuzzy clustering with medoids.

RESULTS

Craniofacial abnormalities and soft tissue facial features defined clusters. Three clusters were identified. Cluster 1 comprised a group of younger children (5.9 ± 3.8 years) without obesity, without craniofacial abnormalities, and with smaller soft tissue facial features dimensions. Cluster 2 comprised a group of older children (9.6 ± 3.9 years) without obesity and with larger mandibular dimensions and mildly arched palates (71.4%). Cluster 3 comprised a group of older children (9.2 ± 3.9 years) with obesity and a history of health issues (68.4%), excessive lower facial height (63.2%), and midface deficiency (73.7%). No differences were observed across clusters regarding sleep features. A moderate severity of obstructive and mixed respiratory events was observed in all 3 clusters.

CONCLUSIONS

The study results did not identify distinct phenotypes of pediatric OSA based on soft tissue facial features or craniofacial abnormalities alone. Age and body mass index likely modify the effect of soft tissue facial features and craniofacial abnormalities as risk factors for OSA in children.

CITATION

Fernandes Fagundes NC, Loliencar P, MacLean JE, Flores-Mir C, Heo G. Characterization of craniofacial-based clinical phenotypes in children with suspected obstructive sleep apnea. J Clin Sleep Med. 2023;19(11):1857-1865.

[Digital technology and children's maxillofacial management]

The maxillofacial region has multiple functions such as breathing, language, and facial expressions. Children's maxillofacial development is a complex and long process, which is affected by many factors such as genetics, diseases, bad habits and trauma. Early detection, early diagnosis, and early treatment are important concepts in children's maxillofacial management. Digital technology medicine is an emerging technology based on medical imaging and anatomy that has emerged in recent years. The application of this technology in the field of clinical medicine will undoubtedly bring great benefits to children's maxillofacial management. This article summarizes the research on digital technology in children's maxillofacial management, and focuses on the research on children's malocclusion, children's OSA, cleft lip and palate and other related diseases.

3D facial analysis for rare disease diagnosis and treatment monitoring: Proof-Of-Concept plan for hereditary angioedema

Rare diseases pose a diagnostic conundrum to even the most experienced clinicians around the world. The technology could play an assistive role in hastening the diagnosis process. Data-driven methodologies can identify distinctive disease features and create a definitive diagnostic spectrum. The healthcare professionals in developed and developing nations would benefit immensely from these approaches resulting in quicker diagnosis and enabling early care for the patients. Hereditary Angioedema is one such rare disease that requires a lengthy diagnostic cascade ensuing massive patient inconvenience and cost burden on the healthcare system. It is hypothesized that facial analysis with advanced imaging and algorithmic association can create an ideal diagnostic peer to the clinician while assimilating signs and symptoms in the hospital. 3D photogrammetry has been applied to diagnose rare diseases in various cohorts. The facial features are captured at a granular level in utmost finer detail. A validated and proven algorithm-powered software provides recommendations in real-time. Thus, paving the way for quick and early diagnosis to well-trained or less trained clinicians in different settings around the globe. The generated evidence indicates the strong applicability of 3 D photogrammetry in association with proprietary Cliniface software to Hereditary Angioedema for aiding in the diagnostic process. The approach, mechanism, and beneficial impact have been sketched out appropriately herein. This blueprint for hereditary angioedema may have far-reaching consequences beyond disease diagnosis to benefit all the stakeholders in the healthcare arena including research and new drug development.

Three-dimensional orthodontic imaging in children across the age spectrum and correlations with obstructive sleep apnea

STUDY OBJECTIVES

To determine baseline facial convexity measurements in children with obstructive sleep apnea (OSA) across the age spectrum.

METHODS

Polysomnogram, stereophotogrammetry, and biometric data were collected from children aged 0-18 years who were being investigated for OSA. Analyses evaluated differences in facial convexity according to OSA severity and other sleep parameters, while adjusting for age, ethnicity, and sex.

RESULTS

Ninety-one children, aged 0.05-16.02 years, met the inclusion criteria for this study. Initial analysis showed that the logarithm of age had a significant effect on facial convexity (P = 8.3·10-7) with significant effects of sex (P = 1.3·10-2), while excluding OSA. Ordinal logistic regression taking into consideration age, sex, weight, height, and ethnicity with OSA severity categorized as obstructive apnea-hypopnea index negative, mild, moderate, or severe showed that facial convexity was associated with OSA severity (P = 2.2·10-3); an increasing obtuse angle of convexity increased the tendency to be classified as having severe OSA.

CONCLUSIONS

Using three-dimensional imaging, we found an added impact of infancy on changes of facial convexity with age. While modeling could describe facial convexity without any OSA-associated sleep parameters, differences in facial convexity were present among groups with different levels of OSA severity adjusted for growth (age, weight, and height), sex, and ethnicity. The method provides a safer and cheaper alternative to other medical imaging techniques in children and holds potential for future use in studies of craniofacial structure.

CITATION

Tyler G, Machaalani R, Waters KA. Three-dimensional orthodontic imaging in children across the age spectrum and correlations with obstructive sleep apnea. J Clin Sleep Med. 2023;19(2):275-282.

Predicting obstructive sleep apnea severity from craniofacial images using ensemble machine learning models

Obstructive sleep apnea (OSA) is a prevalent disease affecting 10 to 15% of Americans and nearly one billion people worldwide. It leads to multiple symptoms including daytime sleepiness; snoring, choking, or gasping during sleep; fatigue; headaches; non-restorative sleep; and insomnia due to frequent arousals. Although polysomnography (PSG) is the gold standard for OSA diagnosis, it is expensive, not universally available, and time-consuming, so many patients go undiagnosed due to lack of access to the test. Given the incomplete access and high cost of PSG, many studies are seeking alternative diagnosis approaches based on different data modalities. Here, we propose a machine learning model to predict OSA severity from 2D frontal view craniofacial images. In a cross-validation study of 280 patients, our method achieves an average AUC of 0.780. In comparison, the craniofacial analysis model proposed by a recent study only achieves 0.638 AUC on our dataset. The proposed model also outperforms the widely used STOP-BANG OSA screening questionnaire, which achieves an AUC of 0.52 on our dataset. Our findings indicate that deep learning has the potential to significantly reduce the cost of OSA diagnosis.

Prediction of obstructive sleep apnea using deep learning in 3D craniofacial reconstruction

Background

Obstructive sleep apnea (OSA) is a common sleep disorder. However, current diagnostic methods are labor-intensive and require professionally trained personnel. We aimed to develop a deep learning model using upper airway computed tomography (CT) to predict OSA and to warn the medical technician if a patient has OSA while the patient is undergoing any head and neck CT scan, even for other diseases.

Methods

A total of 219 patients with OSA [apnea-hypopnea index (AHI) ≥10/h] and 81 controls (AHI <10/h) were enrolled. We reconstructed each patient's CT into 3 types (skeletal structures, external skin structures, and airway structures) and captured reconstructed models in 6 directions (front, back, top, bottom, left profile, and right profile). The 6 images from each patient were imported into the ResNet-18 network to extract features and output the probability of OSA using two fusion methods: Add and Concat. Five-fold cross-validation was used to reduce bias. Finally, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated.

Results

All 18 views with Add as the feature fusion performed better than did the other reconstruction and fusion methods. This gave the best performance for this prediction method with an AUC of 0.882.

Conclusions

We present a model for predicting OSA using upper airway CT and deep learning. The model has satisfactory performance and enables CT to accurately identify patients with moderate to severe OSA.

Facial Scanners in Dentistry: An Overview

Purpose: This narrative review aims to explore the current status of facial scanning technology in the dental field; outlining the history, mechanisms, and current evidence regarding its use and limitations within digital dentistry. Methods: Subtopics within facial scanner technology in dentistry were identified and divided among four reviewers. Electronic searches of the Medline (PubMed) database were performed with the following search terms: facial scanner, dentistry, prosthodontics, virtual patient, sleep apnea, maxillofacial prosthetics, accuracy. For this review only studies or review papers evaluating facial scanning technology for dental or medical applications were included. A total of 44 articles were included. Due to the narrative nature of this review, no formal evidence-based quality assessment was performed and the search was limited to the English language. No further restrictions were applied. Results: The significance, applications, limitations, and future directions of facial scanning technology were reviewed. Specific subtopics include significant history of facial scanner use and development for dentistry, different types and mechanisms used in facial scanning technology, accuracy of scanning technology, use as a diagnostic tool, use in creating a virtual patient, virtual articulation, smile design, diagnosing and treating obstructive sleep apnea, limitations of scanning technology, and future directions with artificial intelligence. Conclusions: Despite limitations in scan quality and software operation, 3D facial scanners are rapid and non-invasive tools that can be utilized in multiple facets of dental care. Facial scanners can serve an invaluable role in the digital workflow by capturing facial records to facilitate interdisciplinary communication, virtual articulation, smile design, and obstructive sleep apnea diagnosis and treatment. Looking into the future, facial scanning technology has promising applications in the fields of craniofacial research, and prosthodontic diagnosis and treatment planning.

The Predictive Role of the Upper-Airway Adipose Tissue in the Pathogenesis of Obstructive Sleep Apnoea

This study aimed to analyse the thickness of the adipose tissue (AT) around the upper airways with anthropometric parameters in the prediction and pathogenesis of OSA and obstruction of the upper airways using artificial intelligence. One hundred patients were enrolled in this prospective investigation, who were divided into control (non-OSA) and mild, moderately severe, and severe OSA according to polysomnography. All participants underwent drug-induced sleep endoscopy, anthropometric measurements, and neck MRI. The statistical analyses were based on artificial intelligence. The midsagittal SAT, the parapharyngeal fat, and the midsagittal tongue fat were significantly correlated with BMI; however, no correlation with AHI was observed. Upper-airway obstruction was correctly categorised in 80% in the case of the soft palate, including parapharyngeal AT, sex, and neck circumference parameters. Oropharyngeal obstruction was correctly predicted in 77% using BMI, parapharyngeal AT, and abdominal circumferences, while tongue-based obstruction was correctly predicted in 79% using BMI. OSA could be predicted with 99% precision using anthropometric parameters and AT values from the MRI. Age, neck circumference, midsagittal and parapharyngeal tongue fat values, and BMI were the most vital parameters in the prediction. Basic anthropometric parameters and AT values based on MRI are helpful in predicting OSA and obstruction location using artificial intelligence.

Classification of Alar Dynamic Aesthetic in an Asian Female Population: Experts or Automatic Algorithms?

AIM

To provide referenced classifications of alar dynamic aesthetics from both subjective and objective perspectives for determining proper surgical strategies in alarplasty.

METHODS

A total of 150 healthy Asian female participants were instructed to perform two standardized facial movements including a resting pose and a maximum smile while taking care not to show their teeth. The participants were recorded using a dynamic three-dimensional surface imaging system. Frames depicting the resting position and the alar maximum enlargement during the smile were exported separately for anthropometric analysis and classification. The alar dynamic aesthetic was assessed through measurement of the anthropomorphic changes comparing the resting and maximum smile statuses and then transformed into quantitative analysis through the algorithm [Formula: see text]. Subjective classification and evaluation of the subject cosmetic deficiencies and proposals for therapeutic interventions to improve the subjects' alar dynamic aesthetic were performed by three senior plastic surgeons through visualization of the resting and smiling images. The surgeons were asked to divide and classify the subjects into three groups (Class I, Class II and Class III) according to the surgeons' perceptions of degree of the subjects' deficiencies in alar dynamic aesthetic. The more deficiency there was in the aesthetic, the higher the class that the subject was assigned into. The surgeons were presented with the full set of images of the patients on two separate occasions each three months apart, to assess interobserver reliability. Clustering analysis, which is based on machine learning, was applied for objective classification of the images.

RESULTS

According to the senior plastic surgeon experts' subjective classification, the subjects' alar flaring mobility was judged as follows: Class I (6.78 ± 3.84%), Class II (10.35 ± 4.18%), and Class III (18.68 ± 4.15%), while alar base mobility was judged as Class I (12.71 ± 7.57%), Class II (20.06 ± 10.06%), and Class III (30.86 ± 13.20%). By clustering analysis, alar flaring mobility was determined to be Class I (7.01 ± 3.51%), Class II (11.18 ± 4.76%), and Class III (12.72 ± 5.66%), while alar base mobility was Class I (9.07 ± 4.23%), Class II (21.88 ± 4.25%), and Class III (38.59 ± 7.08%). No statistical significance was found in the distribution and assignment of classes between the two methodologies.

CONCLUSION

Classifications of alar dynamic aesthetics could arouse attention to facial dynamic aesthetics and provide referenced quantitative parameters for plastic surgeons to determine appropriate treatments for alarplasty. For patients with Class I mobility, treatments are not recommended, while minimally invasive treatments can be deemed to be optional for patients with Class II alar mobility to potentially improve alar dynamic aesthetics. For patients with Class III alar mobility, surgical treatments are strongly recommended as options. Combing subjective classification with automated algorithms can provide a novel perspective and improve reliability for facial aesthetic classification analysis.

LEVEL OF EVIDENCE IV

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors   www.springer.com/00266 .

Therapeutic effect of laparoscopic sleeve gastrectomy on obstructive sleep apnea and relationship of type 2 diabetes in Japanese patients with severe obesity

AIMS/INTRODUCTION

Obstructive sleep apnea (OSA) is among the most important obesity-related diseases, and offers the potential for accelerated the early onset and progression of Type 2 diabetes (T2D). The aim of the present study was to clarify the therapeutic effect of laparoscopic sleeve gastrectomy (LSG) on OSA in severely obese Japanese patients and to find correlations between OSA improvements and β cell function (BCF).

MATERIALS AND METHODS

Between September 2013 and December 2019, 61 patients who underwent LSG were enrolled. The apnea-hypopnea index (AHI) was used to diagnose OSA. The tongue area (TA), uvula area (UA), and other parameters were measured using cone-beam computed tomography. Regarding BCF parameters, the homeostasis model assessment of beta-cell function (HOMA-β), insulinogenic, Matsuda, and disposition indexes were used to evaluate the improvement in BCF. Improvement of OSA was defined as AHI < 15.

RESULTS

The improvement rate of OSA was 51.8% (29/56). The change in AHI was significantly correlated with the excess weight-loss percentage (ρ = 0.501), changes in TA (ρ = 0.350), and UA (ρ = 0.341). Multivariate analysis revealed that preoperative AHI and postoperative HbA1c were independent prognostic factors of OSA non-improvement. HOMA-β (P < 0.001), the insulinogenic index (P < 0.001), and the disposition index (P = 0.019) of patients with AHI of < 15 were significantly higher than those in patients with AHI of ≥ 15.

CONCLUSIONS

LSG is a promising procedure for severely obese patients with OSA. BCF recovery was found to be significantly higher in patients with OSA improvement.

Predicting the presence and severity of obstructive sleep apnea based on mandibular measurements using quantitative analysis of facial profiles via three-dimensional photogrammetry

BACKGROUND

In obstructive sleep apnea (OSA), the upper airway is obstructed during sleep due to obesity and/or posterior collapse of the tongue root. Maxillofacial morphological abnormalities increase the risk of OSA in the Asian population. This study sought to elucidate whether three-dimensional (3D) photogrammetry measurements correlate with the severity of OSA irrespective of sex and degree of obesity.

METHODS

A prospective pilot study was performed, in which 37 consecutive adult patients (M/F = 28/9) underwent polysomnography and 3D photogrammetry in the supine position for the diagnosis of OSA. Measurements obtained from 3D photogrammetry included mandibular width (Mw), mandibular length (Ml), mandibular depth (Md), mandibular width-length angle (Mwla), and mandibular area (Ma). The effects of sex and body mass index (BMI) on the measurements and their association with the apnea-hypopnea index (AHI) were statistically analyzed. The inter-rater reliability of the measurements was evaluated using intraclass correlation coefficients (ICC).

RESULTS

Mwla (R = 0.73, p < 0.01), Mw (R = 0.39, p < 0.05), and Md (R = -0.34, p < 0.05) were significantly correlated with the severity of OSA. On multivariate analysis, Mwla (p < 0.01) and Md (p < 0.05) remained independent factors for AHI after adjusting for sex, age, BMI, and neck circumference. In addition, diagnosability analysis revealed that Mwla was useful for identifying the presence of OSA (AHI ≥5) (cutoff: 78.6°, sensitivity: 0.938, specificity: 0.800, area under the curve: 0.931). The ICC was >0.9, showing high reliability.

CONCLUSIONS

This study suggests that Mwla measured using 3D photogrammetry can predict the presence of OSA and correlates with the severity of OSA, independent of obesity and sex.

Artificial intelligence and sleep: Advancing sleep medicine

Artificial intelligence (AI) allows analysis of "big data" combining clinical, environmental and laboratory based objective measures to allow a deeper understanding of sleep and sleep disorders. This development has the potential to transform sleep medicine in coming years to the betterment of patient care and our collective understanding of human sleep. This review addresses the current state of the field starting with a broad definition of the various components and analytic methods deployed in AI. We review examples of AI use in screening, endotyping, diagnosing, and treating sleep disorders and place this in the context of precision/personalized sleep medicine. We explore the opportunities for AI to both facilitate and extend providers' clinical impact and present ethical considerations regarding AI derived prognostic information. We cover early adopting specialties of AI in the clinical realm, such as radiology and pathology, to provide a road map for the challenges sleep medicine is likely to face when deploying this technology. Finally, we discuss pitfalls to ensure clinical AI implementation proceeds in the safest and most effective manner possible.

Evaluation and Management of Adults with Obstructive Sleep Apnea Syndrome

Obstructive sleep apnea syndrome (OSAS) is a common and underdiagnosed medical condition characterized by recurrent sleep-dependent pauses and reductions in airflow. While a narrow, collapsible oropharynx plays a central role in the pathophysiology of OSAS, there are other equally important nonanatomic factors including sleep-stage dependent muscle tone, arousal threshold, and loop gain that drive obstructive apneas and hypopneas. Through mechanisms of intermittent hypoxemia, arousal-related sleep fragmentation, and intrathoracic pressure changes, OSAS impacts multiple organ systems. Risk factors for OSAS include obesity, male sex, age, specific craniofacial features, and ethnicity. The prevalence of OSAS is rising due to increasing obesity rates and improved sensitivity in the tools used for diagnosis. Validated questionnaires have an important but limited role in the identification of patients that would benefit from formal testing for OSA. While an in-laboratory polysomnography remains the gold standard for diagnosis, the widespread availability and accuracy of home sleep apnea testing modalities increase access and ease of OSAS diagnosis for many patients. In adults, the most common treatment involves the application of positive airway pressure (PAP), but compliance continues to be a challenge. Alternative treatments including mandibular advancement device, hypoglossal nerve stimulator, positional therapies, and surgical options coupled with weight loss and exercise offer possibilities of an individualized personal approach to OSAS. Treatment of symptomatic patients with OSAS has been found to be beneficial with regard to sleep-related quality of life, sleepiness, and motor vehicle accidents. The benefit of treating asymptomatic OSA patients, particularly with regard to cardiovascular outcomes, is controversial and more data are needed.

Machine learning for image-based detection of patients with obstructive sleep apnea: an exploratory study

PURPOSE

In 2-dimensional lateral cephalometric radiographs, patients with severe obstructive sleep apnea (OSA) exhibit a more crowded oropharynx in comparison with non-OSA. We tested the hypothesis that machine learning, an application of artificial intelligence (AI), could be used to detect patients with severe OSA based on 2-dimensional images.

METHODS

A deep convolutional neural network was developed (n = 1258; 90%) and tested (n = 131; 10%) using data from 1389 (100%) lateral cephalometric radiographs obtained from individuals diagnosed with severe OSA (n = 867; apnea hypopnea index > 30 events/h sleep) or non-OSA (n = 522; apnea hypopnea index < 5 events/h sleep) at a single center for sleep disorders. Three kinds of data sets were prepared by changing the area of interest using a single image: the original image without any modification (full image), an image containing a facial profile, upper airway, and craniofacial soft/hard tissues (main region), and an image containing part of the occipital region (head only). A radiologist also performed a conventional manual cephalometric analysis of the full image for comparison.

RESULTS

The sensitivity/specificity was 0.87/0.82 for full image, 0.88/0.75 for main region, 0.71/0.63 for head only, and 0.54/0.80 for the manual analysis. The area under the receiver-operating characteristic curve was the highest for main region 0.92, for full image 0.89, for head only 0.70, and for manual cephalometric analysis 0.75.

CONCLUSIONS

A deep convolutional neural network identified individuals with severe OSA with high accuracy. Future research on this concept using AI and images can be further encouraged when discussing triage of OSA.

The faces of sleep apnea in the age of machine learning

Jacobowitz O, MacKay S. The faces of sleep apnea in the age of machine learning. J Clin Sleep Med . 2020;16(4):469–470.