ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2023
Automated deep neural network analysis of lateral cephalogram data can aid in detecting obstructive sleep apnea.
Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- An Exploration of Craniofacial Features in Obstructive Sleep Apnea Using the Explainable Deep Learning Approaches.Nature and science of sleep · 2026Article
- Interpretable two-stage deep learning for pediatric obstructive sleep apnea diagnosis using lateral cephalograms.Frontiers in pediatrics · 2026Article
- Advancements in Obstructive Sleep Apnea Diagnosis and Screening Through Artificial Intelligence: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
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10 authors.
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Abstract
STUDY
objectivesInformation on obstructive sleep apnea (OSA) is often latently detected in diagnostic tests conducted for other purposes, providing opportunities for maximizing value. This study aimed to develop a convolutional neural network (CNN) to identify the risk of OSA using lateral cephalograms.
methodsThe lateral cephalograms of 5,648 individuals (mean age, 49.0 ± 15.8 years; men, 62.3%) with or without OSA were collected and divided into training, validation, and internal test datasets in a 5:2:3 ratio. A separate external test dataset (n = 378) was used. A densely connected CNN was trained to diagnose OSA using a cephalogram. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Gradient-weighted class activation mapping (Grad-CAM) was used to evaluate the region of focus, and the relationships between the model outputs, anthropometric characteristics, and OSA severity were evaluated.
resultsThe AUROC of the model for the presence of OSA was 0.82 (95% confidence interval, 0.80-0.84) and 0.73 (95% confidence interval, 0.65-0.81) in the internal and external test datasets, respectively. Grad-CAM demonstrated that the model focused on the area of the tongue base and oropharynx in the cephalogram. Sigmoid output values were positively correlated with OSA severity, body mass index, and neck and waist circumference.
conclusionsDeep learning may help develop a model that classifies OSA using a cephalogram, which may be clinically useful in the appropriate context. The definition of ground truth was the main limitation of this study. CITATION: Jeong H-G, Kim T, Hong JE, et al. Automated deep neural network analysis of lateral cephalogram data can aid in detecting obstructive sleep apnea.
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