ArticleSleep2025
Artificial intelligence or sleep experts: comparing polysomnographic sleep staging in children and adolescents.
Article in Sleep, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- Fully-automated sleep staging for Parkinson's disease and isolated REM sleep behavior disorder.NPJ digital medicine · 2026Article
- Harnessing artificial intelligence for pediatric health: Current trends and future opportunities.iScience · 2026Review
- Article
- Article
- The Role of the Sleep Centre in the Future of Sleep Medicine. How Should It Be Organised? Will There Still Be Sleep Inpatients in the Future?Journal of sleep research · 2025Review
- Article
- Validation of the Somnolyzer 24×7 automatic scoring system in children with suspected obstructive sleep apnea.Frontiers in medicine · 2025Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
objectivesThe manual annotation of polysomnography (PSG) hypnograms is difficult and time-consuming. U-Sleep is an alternative, fast, and publicly available, automated sleep staging system evaluated in adult PSGs. In this study, we compare the staging done by sleep experts and U-sleep in a pediatric sample.
methodsPSGs from 56 children aged 6-17 years old (healthy or with a chronic disease) were compared manually annotated with the result of U-sleep. The two outcomes were compared using F1 overlap scores, accuracy, Cohen's kappa, and correlation coefficients. A qualitative analysis of the most significant systematic differences between the manual and automated scoring was performed.
resultsU-sleep matched the manually scored hypnograms with an overall mean F1 score (predicted performance) of 0.75 and reached an accuracy of 83.9% and an overall kappa value of 0.77. The stage-wise F1 scores, U-sleep achieved an F1 score of 0.79 in stage wake, 0.40 in N1, 0.86 in N2, 0.84 in N3, and 0.86 in REM. The correlation between U-sleep and the manual scorer was moderately or very strong in all sleep stages (r = .57-.81).
conclusionsOverall, there is a high degree of agreement between manual and automatic scoring. This suggests that U-sleep is a valid and effective method for identifying sleep stages based on normal PSGs in a pediatric population. The disagreement was within what is expected for interscorer variation. Further evaluation needs of AI sleep-scoring models include analysis of outliers and pathological sleep staging-which is also a challenge in manual annotation.
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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.