ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2020
Predicting sleep apnea from three-dimensional face photography.
Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Two-dimensional facial photography for assessment of craniofacial morphology in sleep breathing disorders: a systematic review.Sleep & breathing = Schlaf & Atmung · 2024Pooled it
- Cardiovascular Disease, Sleep-Disordered Breathing, and Artificial Intelligence: From Neutral Trials to Precision Sleep Cardiology.Reviews in cardiovascular medicine · 2026Review
- Mapping the Fat: How Childhood Obesity and Body Composition Shape Obstructive Sleep Apnoea.Children (Basel, Switzerland) · 2025Review
- A novel machine learning model for screening the risk of obstructive sleep apnea using craniofacial photography with questionnaires.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- Ascending aorta dilatation indicates obstructive sleep apnea in Chinese patients with cardiovascular disease.Sleep & breathing = Schlaf & Atmung · 2025Article
- Utilizing a Wireless Radar Framework in Combination With Deep Learning Approaches to Evaluate Obstructive Sleep Apnea Severity in Home-Setting Environments.Journal of multidisciplinary healthcare · 2025Article
- Smartphone-generated 3D facial images: reliable for routine assessment of the oronasal region of patients with cleft or mere convenience? A validation study.BMC oral health · 2024Article
- Accuracy Assessment of EM3D App-Based 3D Facial Scanning Compared to Cone Beam Computed Tomography.Dentistry journal · 2024Article
- Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review.Journal of clinical medicine · 2024Review
- Frontiers in Three-Dimensional Surface Imaging Systems for 3D Face Acquisition in Craniofacial Research and Practice: An Updated Literature Review.Diagnostics (Basel, Switzerland) · 2024Review
- A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators.Nature and science of sleep · 2024Article
- Characterization of craniofacial-based clinical phenotypes in children with suspected obstructive sleep apnea.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2023Article
- [Digital technology and children's maxillofacial management].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2023Article
- Classification of Alar Dynamic Aesthetic in an Asian Female Population: Experts or Automatic Algorithms?Aesthetic plastic surgery · 2023Article
- 3D facial analysis for rare disease diagnosis and treatment monitoring: Proof-Of-Concept plan for hereditary angioedema.PLOS digital health · 2023Article
- Predicting obstructive sleep apnea severity from craniofacial images using ensemble machine learning models.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Three-dimensional orthodontic imaging in children across the age spectrum and correlations with obstructive sleep apnea.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2023Article
- Prediction of obstructive sleep apnea using deep learning in 3D craniofacial reconstruction.Journal of thoracic disease · 2023Article
- Detecting obstructive sleep apnea by craniofacial image-based deep learning.Sleep & breathing = Schlaf & Atmung · 2022Article
- The Predictive Role of the Upper-Airway Adipose Tissue in the Pathogenesis of Obstructive Sleep Apnoea.Life (Basel, Switzerland) · 2022Article
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8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
objectivesCraniofacial 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.
methods3D 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.
resultsRelative 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.
conclusionsThis study suggests that 3D photographs of the face have predictive value for OSA and that geodesic measurements enhance this capacity.
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