Predicting sleep apnea from three-dimensional face photography

Abstract

Study Objectives: Craniofacial anatomy is recognized as an important predisposing factor in the pathogenesis of obstructive sleep apnea (OSA). This study used three-dimensional (3D) facial surface analysis of linear and geodesic (shortest line between points over a curved surface) distances to determine the combination of measurements that best predicts presence and severity of OSA. Methods: 3D face photographs were obtained in 100 adults without OSA (apnea-hypopnea index [AHI] < 5 events/h), 100 with mild OSA (AHI 5 to < 15 events/h), 100 with moderate OSA (AHI 15 to < 30 events/h), and 100 with severe OSA (AHI ≥ 30 events/h). Measurements of linear distances and angles, and geodesic distances were obtained between 24 anatomical landmarks from the 3D photographs. The accuracy with which different combinations of measurements could classify an individual as having OSA or not was assessed using linear discriminant analyses and receiver operating characteristic analyses. These analyses were repeated using different AHI thresholds to define presence of OSA. Results: Relative to linear measurements, geodesic measurements of craniofacial anatomy improved the ability to identify individuals with and without OSA (classification accuracy 86% and 89% respectively, P < .01). A maximum classification accuracy of 91% was achieved when linear and geodesic measurements were combined into a single predictive algorithm. Accuracy decreased when using AHI thresholds ≥ 10 events/h and ≥ 15 events/h to define OSA although greatest accuracy was always achieved using a combination of linear and geodesic distances. Conclusions: This study suggests that 3D photographs of the face have predictive value for OSA and that geodesic measurements enhance this capacity.

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

“…We used cephalography and cone‐beam computed tomography (Kavo op 3D Vision V17; KaVo Dental Systems, Tokyo, Japan) for imaging modalities. Afterward, we measured the tongue area (TA), uvula area (UA), uvula length (UL), posterior airway space and airway diameter at the tip of the epiglottis using a computer workstation (SYNAPSE VINCENT; FUJIFILM, Tokyo, Japan; Figure 1 ) 11 , 12 . We carried out these imaging examinations at the same time as FN‐PSG, and expressed cm 2 for TA and UA, cm for UL, and mm for posterior airway space and airway diameter at the tip of the epiglottis.…”

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

“…Every cephalometric image was taken of the natural head posture determined by visual feedback in a mirror in accordance with the established method [ 17 ]. Among the total 1389 images, 1251 (90%) images were used for training (i.e., learning images; 781 OSA images, and 470 non-OSA images) and the remaining 138 (10%) images were used for testing (i.e., testing images, 86 OSA images, and 52 control images) in accordance with recent reports [ 18 – 21 ]. To investigate which part of the original image is focused upon when using DCNN, three data sets were prepared by changing the area of interest (Fig.…”

Usage of RGB-D Multi-Sensor Imaging System for Medical Applications

“…The features were processed by support vector machine (SVM) classifier and get the resulting accuracy of 80% correct OSAS detections. In Ref [17], the authors use the training set of 3D images of 400 patients. All of them were identified with PSG, AHI index and were divided into 4 groups.…”