ArticleNature and science of sleep2025
AI‑Enhanced Smartwatch AHI Estimation and AI‑Scored Polysomnography for Obstructive Sleep Apnea: Real‑World Validation.
Article in Nature and science of sleep, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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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
4 citing papers in PubMed, 1 synthesis or guideline 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
- Smartwatch-based detection of moderate-to-severe and high-risk obstructive sleep apnea.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2026Observational
- Artificial intelligence for sleep-disordered breathing: a taxonomy of methods, data modalities, and clinical applications.Frontiers in sleep · 2026Review
- Multicenter Validation of an AI-Based Snoring Model vs. Wearables for Diagnosing Sleep Apnea.Nature and science of sleep · 2026Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study validated the accuracy of an artificial‑intelligence (AI) smartwatch algorithm that directly estimates the apnea-hypopnea index (AHI) by comparing its performance with AI-scored Level 1 polysomnography (PSG) in Korean adults. The model was trained in South‑American cohorts, allowing inter‑ethnic validation. Methods: A total of 90 adults underwent simultaneous Level 1 PSG and smartwatch recording. Fifty‑three datasets with ≥ 3 hours of valid watch data were analyzed. AHI values were obtained as follows: expert‑scored PSG (pAHI), AI‑scored PSG (aiAHI), and smartwatch output (eAHI). Agreement was assessed with Spearman correlation, intraclass correlation coefficients, and receiver‑operating‑characteristic curves. Results: eAHI correlated strongly with aiAHI ( Conclusion: This study demonstrates that the evaluated smartwatch-based AHI estimation algorithm shows high concordance with PSG-derived values, particularly for the detection and classification of moderate to severe OSA. However, it should be noted that this smartwatch algorithm tends to underestimate the AHI of OSA due to limitations in scoring unit and recording duration calculation. These findings support the clinical utility of wearable technology as a practical and scalable tool for early identification and longitudinal monitoring of OSA in real-world environments, while highlighting the need for further optimization to accurately detect mild cases.
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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.