SynthesisJournal of medical systems2025
Wearable Sensors and Artificial Intelligence for Sleep Apnea Detection: A Systematic Review.
Synthesis in Journal of medical systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy of Artificial Intelligence in Diagnosing Obstructive Sleep Apnea Using Photoplethysmography: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Diagnostic value of wearable and contactless technologies for detecting obstructive sleep apnea: a systematic review and meta-analysis.Sleep & breathing = Schlaf & Atmung · 2025Pooled it
- The Global Initiative for Obstructive Sleep Apnea (GIFOSA): Rationale, Strategic Framework, and Call to Action.Pulmonary therapy · 2026Review
- The Global Initiative for Obstructive Sleep Apnea (GIFOSA): Rationale, Strategic Framework, and Call to Action.Pulmonary therapy · 2026Review
- Review
- Wearable Devices for Monitoring and Management of Comorbid Obstructive Sleep Apnea and Hypertension: Scoping Review.JMIR mHealth and uHealth · 2026Article
- Explainable CNN-BiLSTM Framework for Multi-Class Sleep Apnea Severity Detection Using Single-Lead ECG Signals: A Comprehensive Machine Learning Approach.Diagnostics (Basel, Switzerland) · 2026Article
- Obstructive Sleep Apneas and Cardiovascular Diseases.Clocks & sleep · 2026Review
- Non-Contact Detection of Apnea-like Breathing Cessations Using Laser Speckle Pattern Analysis.Sensors (Basel, Switzerland) · 2026Article
- Distributed Precision Stroke Care: Artificial Intelligence-Driven Stroke Management Using Multimodal Sensor Data.Stroke · 2026Review
- Convergent Sensing: Integrating Biometric and Environmental Monitoring in Next-Generation Wearables.Biosensors · 2026Review
- EvoApneaFormer: an IoT and prognostic evolutionary deep learning-based framework for real-time multi-event sleep apnea disorder detection and remote monitoring.Frontiers in bioengineering and biotechnology · 2026Article
- Diagnosis of sleep-disordered breathing using few-shot learning.Frontiers in public health · 2026Article
- Artificial intelligence in surgical planning and outcome prediction for obstructive sleep apnea: emerging hype or the future standard?Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Review
- Sensors, technologies, and classification algorithms for monitoring and diagnosis of sleep apnea.Digital healthReview
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sleep apnea, a prevalent disorder affecting millions of people worldwide, has attracted increasing attention in recent years due to its significant impact on public health and quality of life. The integration of wearable devices and artificial intelligence technologies has revolutionized the treatment and diagnosis of sleep apnea. Leveraging the portability and sensors of wearable devices, coupled with AI algorithms, has enabled real-time monitoring and accurate analysis of sleep patterns, facilitating early detection and personalized interventions for people suffering from sleep apnea. This article presents a systematic review of the current state of the art in identifying the latest artificial intelligence techniques, wearable devices, data types, and preprocessing methods employed in the diagnosis of sleep apnea. Four databases were used and the results before screening report 249 studies published between 2020 and 2024. After screening, 28 studies met the inclusion criteria. This review reveals a trend in recent years where methodologies involving patches, clocks and rings have been increasingly integrated with convolutional neural networks, producing promising results, particularly when combined with transfer learning techniques. We observed that the outcomes of various algorithms and their combinations also rely on the quantity and type of data utilized for training. The findings suggest that employing multiple combinations of different neural networks with convolutional layers contributes to the development of a more precise system for early diagnosis of sleep apnea.
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.