SynthesisBMC medical informatics and decision making2025
Diagnostic accuracy of artificial intelligence for obstructive sleep apnea detection: a systematic review.
Synthesis in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses 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
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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
- Advancements in pediatric obstructive sleep apnea: cognitive implications and the role of AI in precision medicine.Frontiers in medicine · 2025Pooled it
- Machine Learning-Based Multidimensional Oximetry for Obstructive Sleep Apnea Screening: Development and External Validation.JMIR medical informatics · 2026Article
- Artificial intelligence in sleep diagnostics for children under 2 years of age: state of the evidence and future directions.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- Electrocardiographic Alterations in Obstructive Sleep Apnea Syndrome: Mechanisms and Clinical Relevance.Life (Basel, Switzerland) · 2026Review
- Diagnosis of sleep-disordered breathing using few-shot learning.Frontiers in public health · 2026Article
- A Nomogram Model for Predicting Moderate to Severe OSA in Western China: A Retrospective Analysis.Nature and science of sleep · 2026Article
- Unlocking the heterogeneity of pediatric obesity: a phenotypic subtype-based paradigm for precision management.Frontiers in endocrinology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundObstructive sleep apnea (OSA) is a highly prevalent sleep disorder. Misdiagnosis might lead to several systemic conditions, including hypertension, vascular damage, and cognitive impairment. The gold-standard diagnostic tool for OSA is polysomnography, which is expensive, time-consuming, and not accessible everywhere. Artificial intelligence (AI) algorithms can facilitate diagnosis by detecting patients' signs and symptoms. In this systematic review, we evaluated the diagnostic accuracy of AI models in detecting sleep apnea.
methodsWe searched six major databases, PubMed
resultsThirteen studies were included in our final analysis. The AI models consisted of deep learning, machine learning, and hybrid models with various architectures. The reported accuracy of studies ranged from 67.03 to 98.6%, with the highest being related to hybrid and deep learning models. Risk of bias assessment showed that 7 of the studies had a low risk of bias, indicating high reliability.
conclusionsAI-driven models, particularly deep learning and hybrid architectures, show significant promise in diagnosing obstructive sleep apnea. However, challenges such as transparency, explainability, and variability in performance necessitate diverse training datasets to improve generalizability for clinical adoption. REGISTRATION OF SYSTEMATIC REVIEWS: The protocol of this systematic review was registered in PROSPERO (CRD42023453789), available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023453789 .
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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.