ReviewMedical & biological engineering & computing2024
Obstructive sleep apnea detection during wakefulness: a comprehensive methodological review.
Review in Medical & biological engineering & computing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed, 6 citations in OpenAlex.
- Revolutionizing Sleep Medicine: The Impact of Machine Learning on Diagnosis, Treatment, and Personalized Care.Health science reports · 2026Article
- A Two-Level Ensemble Machine Learning Framework for OSA Classification Whilst Awake from Noisy Tracheal Breathing Sounds.Sensors (Basel, Switzerland) · 2026Article
- Awake Insights for Obstructive Sleep Apnea: Severity Detection Using Tracheal Breathing Sounds and Meta-Model Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Interpretable Acoustic Features from Wakefulness Tracheal Breathing for OSA Severity Assessment.Journal of clinical medicine · 2026Article
- Assessing Obstructive Sleep Apnea Severity During Wakefulness via Tracheal Breathing Sound Analysis.Sensors (Basel, Switzerland) · 2025Article
- Maxillary Expansion in the Management of Obstructive Sleep Apnea: A Comprehensive Review.Dentistry journal · 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
Obstructive sleep apnea (OSA) is a chronic condition affecting up to 1 billion people, globally. Despite this spread, OSA is still thought to be underdiagnosed. Lack of diagnosis is largely attributed to the high cost, resource-intensive, and time-consuming nature of existing diagnostic technologies during sleep. As individuals with OSA do not show many symptoms other than daytime sleepiness, predicting OSA while the individual is awake (wakefulness) is quite challenging. However, research especially in the last decade has shown promising results for quick and accurate methodologies to predict OSA during wakefulness. Furthermore, advances in machine learning algorithms offer new ways to analyze the measured data with more precision. With a widening research outlook, the present review compares methodologies for OSA screening during wakefulness, and recommendations are made for avenues of future research and study designs.
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.