ArticleNature communications2023
Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Diagnosis of Obstructive Sleep Apnea Using Overnight Pulse Oximetry: A Systematic Review and Bayesian Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- SApneaNet: Adaptive Squeeze-and-Excitation-Based CNN-Transformer Network with AGFF for Sleep Apnea Event Detection Using ECG Images Under IoMT.Sensors (Basel, Switzerland) · 2026Article
- 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
- Cardiovascular Disease, Sleep-Disordered Breathing, and Artificial Intelligence: From Neutral Trials to Precision Sleep Cardiology.Reviews in cardiovascular medicine · 2026Review
- Engineering biomarker representations of vital signs data enhances deep learning mortality prediction.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Non-contact on-device detection of obstructive sleep apnea from infrared video.Nature communications · 2026Article
- Machine Learning-Based Multidimensional Oximetry for Obstructive Sleep Apnea Screening: Development and External Validation.JMIR medical informatics · 2026Article
- Spectral analysis of ECG and SpO₂ for machine learning classification of Sleep-Disordered breathing.Sleep & breathing = Schlaf & Atmung · 2026Article
- Expert-level probabilistic breathing event detector informs phenotyping of sleep apnea.Nature communications · 2026Article
- Clinical Validation of Artificial Intelligence Algorithms for the Diagnosis of Adult Obstructive Sleep Apnea and Sleep Staging From Oximetry and Photoplethysmography-SleepAI.Journal of sleep research · 2026Article
- Machine learning optimization of obstructive sleep apnea screening: development and validation of a gradient boosting prediction model with a clinical implementation framework.Frontiers in medicine · 2026Article
- The importance of dopamine levels and single-nucleotide polymorphism withinAnnals of medicine · 2025Article
- AI-enhanced flexible ECG patch for accurate heart disease diagnosis, optimal wear positioning, and interactive medical consultation.National science review · 2025Article
- Practical AI application in psychiatry: historical review and future directions.Molecular psychiatry · 2025Review
- CAISR: achieving human-level performance in automated sleep analysis across all clinical sleep metrics.Sleep · 2025Article
- Transparent artificial intelligence-enabled interpretable and interactive sleep apnea assessment across flexible monitoring scenarios.Nature communications · 2025Article
- Transforming label-efficient decoding of healthcare wearables with self-supervised learning and "embedded" medical domain expertise.Communications engineering · 2025Article
- Artificial intelligence in pediatric otolaryngology: A state-of-the-art review of opportunities and pitfalls.International journal of pediatric otorhinolaryngology · 2025Review
- A Comprehensive Drift-Adaptive Framework for Sustaining Model Performance in COVID-19 Detection From Dynamic Cough Audio Data: Model Development and Validation.Journal of medical Internet research · 2025Article
- A Dual-Modal Wearable Pulse Detection System Integrated with Deep Learning for High-Accuracy and Low-Power Sleep Apnea Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Obstructive sleep apnea (OSA) is a serious medical condition with a high prevalence, although diagnosis remains a challenge. Existing home sleep tests may provide acceptable diagnosis performance but have shown several limitations. In this retrospective study, we used 12,923 polysomnography recordings from six independent databases to develop and evaluate a deep learning model, called OxiNet, for the estimation of the apnea-hypopnea index from the oximetry signal. We evaluated OxiNet performance across ethnicity, age, sex, and comorbidity. OxiNet missed 0.2% of all test set moderate-to-severe OSA patients against 21% for the best benchmark.
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Registered trials
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.