Predicting sleep apnea from 3 dimensional face photography.
Study Objectives Craniofacial anatomy is recognised as an important predisposing factor in the pathogenesis of obstructive sleep apnea (OSA) This study used 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/hr) 100 with mild OSA (5<=AHI<15 events/hr) 100 with moderate OSA (15<=AHI<30 events/hr) and 100 with severe OSA (AHI>=30 events/hr) 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 Operator 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<0.01) 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/hr and =15 events/hr to define OSA although greatest accuracy was always achieved using a combination of linear and geodesic distances. Conclusions This study suggest that 3D photographs of the face have predictive value for OSA and that geodesic measurements enhance this capacity.
Journal of clinical sleep medicine JCSM official publication of the American Academy of Sleep Medicine