SynthesisJournal of medical Internet research2022
Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review.
Synthesis in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
39 citing papers in PubMed, 4 syntheses or guidelines pooled it, 55 citations in OpenAlex.
- Performance of AI-Based Screening Tools for Obstructive Sleep Apnea Across Apnea-Hypopnea Index Thresholds: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Accuracy of Machine Learning Algorithms Based on Electroencephalogram in Sleep Apnea Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Application of Machine Learning for Patients With Cardiac Arrest: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Detection of Sleep Apnea Using Wearable AI: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- 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
- Development and validation of machine learning models for obstructive sleep apnea risk stratification in high-risk pregnant women.BMC pregnancy and childbirth · 2026Observational
- Early Screening of Sleep-Disordered Breathing Using Metaheuristic-Optimized Extreme Learning Machines.Diagnostics (Basel, Switzerland) · 2026Article
- Machine learning for scalable obstructive sleep apnea risk screening using digital phenotyping from wearable devices and clinical scales.Scientific reports · 2026Article
- Development and internal validation of a prediction model for sleep apnea syndrome treated with continuous positive airway pressure based on claims and health checkup data linked to personal health records.Sleep & breathing = Schlaf & Atmung · 2026Article
- Obstructive Sleep Apnea in Critically Ill Patients: A Structured Narrative Review of Prevalence, Diagnostic Barriers, and Clinical Implications in the ICU.Clocks & sleep · 2026Review
- Machine Learning-Based Multidimensional Oximetry for Obstructive Sleep Apnea Screening: Development and External Validation.JMIR medical informatics · 2026Article
- Fine-Grained and Lightweight OSA Detection: A CRNN-Based Model for Precise Temporal Localization of Respiratory Events in Sleep Audio.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- Status and Opportunities of Machine Learning Applications in Obstructive Sleep Apnea: A Narrative Review.medRxiv : the preprint server for health sciences · 2025Article
- Revealing sleep and pain reciprocity with wearables and machine learning.Communications medicine · 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
- Development and validation of a new nomogram for self-reported OA based on machine learning: a cross-sectional study.Scientific reports · 2025Article
- Status and opportunities of machine learning applications in obstructive sleep apnea: A narrative review.Computational and structural biotechnology journal · 2025Review
- AI-driven bone mineral density prediction from chest x-rays and its association with obstructive sleep apnea.PloS one · 2025Article
- Establishment and Validation of a Predictive Model in Female Patients with Obstructive Sleep Apnea.Women's health reports (New Rochelle, N.Y.) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAmerican Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used to screen patients with obstructive sleep apnea (OSA) without replacing polysomnography, the gold standard.
objectiveWe aimed to identify, gather, and analyze existing machine learning approaches that are being used for disease screening in adult patients with suspected OSA.
methodsWe searched the MEDLINE, Scopus, and ISI Web of Knowledge databases to evaluate the validity of different machine learning techniques, with polysomnography as the gold standard outcome measure and used the Prediction Model Risk of Bias Assessment Tool (Kleijnen Systematic Reviews Ltd) to assess risk of bias and applicability of each included study.
resultsOur search retrieved 5479 articles, of which 63 (1.15%) articles were included. We found 23 studies performing diagnostic model development alone, 26 with added internal validation, and 14 applying the clinical prediction algorithm to an independent sample (although not all reporting the most common discrimination metrics, sensitivity or specificity). Logistic regression was applied in 35 studies, linear regression in 16, support vector machine in 9, neural networks in 8, decision trees in 6, and Bayesian networks in 4. Random forest, discriminant analysis, classification and regression tree, and nomogram were each performed in 2 studies, whereas Pearson correlation, adaptive neuro-fuzzy inference system, artificial immune recognition system, genetic algorithm, supersparse linear integer models, and k-nearest neighbors algorithm were each performed in 1 study. The best area under the receiver operating curve was 0.98 (0.96-0.99) for age, waist circumference, Epworth Somnolence Scale score, and oxygen saturation as predictors in a logistic regression.
conclusionsAlthough high values were obtained, they still lacked external validation results in large cohorts and a standard OSA criteria definition.
trial registrationPROSPERO CRD42021221339; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221339.
Indexed as
Identifiers
What OpenQuestion holds
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.