ArticleSleep & breathing = Schlaf & Atmung2021
Machine learning for image-based detection of patients with obstructive sleep apnea: an exploratory study.
Article in Sleep & breathing = Schlaf & Atmung, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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Who cites it
20 citing papers in PubMed, 35 citations in OpenAlex.
- Three-class obstructive sleep apnea severity assessment: a parallel AHI and ODI explainable artificial intelligence framework using craniofacial-enriched clinical data.Sleep & breathing = Schlaf & Atmung · 2026Article
- An Exploration of Craniofacial Features in Obstructive Sleep Apnea Using the Explainable Deep Learning Approaches.Nature and science of sleep · 2026Article
- Clinical decisions in Orthodontics using x-ray-based images and artificial intelligence approaches: a scoping review.Dental press journal of orthodontics · 2026Article
- Interpretable two-stage deep learning for pediatric obstructive sleep apnea diagnosis using lateral cephalograms.Frontiers in pediatrics · 2026Article
- Integrating physiological signals for enhanced sleep apnea diagnosis with SleepNet.Scientific reports · 2025Article
- Oxidative Stress and Inflammation in Hypoxemic Respiratory Diseases and Their Comorbidities: Molecular Insights and Diagnostic Advances in Chronic Obstructive Pulmonary Disease and Sleep Apnea.Antioxidants (Basel, Switzerland) · 2025Review
- Ensemble-based eye disease detection system utilizing fundus and vascular structures.Scientific reports · 2025Article
- Artificial Intelligence Applications in Pediatric Craniofacial Surgery.Diagnostics (Basel, Switzerland) · 2025Review
- The Impact of Diagnostic Imaging on Obstructive Sleep Apnea: Feedback from a Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Advancements in Obstructive Sleep Apnea Diagnosis and Screening Through Artificial Intelligence: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- AI-driven bone mineral density prediction from chest x-rays and its association with obstructive sleep apnea.PloS one · 2025Article
- 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
- Development and Validation of a Nomogram for Predicting Obstructive Sleep Apnea Severity in Children.Nature and science of sleep · 2024Article
- Screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram.Scientific reports · 2023Article
- Machine Learning Identification of Obstructive Sleep Apnea Severity through the Patient Clinical Features: A Retrospective Study.Life (Basel, Switzerland) · 2023Article
- Automated deep neural network analysis of lateral cephalogram data can aid in detecting 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
- Automated Evaluation of Upper Airway Obstruction Based on Deep Learning.BioMed research international · 2023Article
- The Predictive Role of the Upper-Airway Adipose Tissue in the Pathogenesis of Obstructive Sleep Apnoea.Life (Basel, Switzerland) · 2022Article
- Review
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeIn 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.
methodsA 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.
resultsThe 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.
conclusionsA 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.