ArticleNature and science of sleep2021
Artificial Intelligence Analysis of Mandibular Movements Enables Accurate Detection of Phasic Sleep Bruxism in OSA Patients: A Pilot Study.
Article in Nature and science of sleep, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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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
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Home-testing devices for diagnosing obstructive sleep apnoea hypopnoea syndrome - a systematic review and economic evaluation.Health technology assessment (Winchester, England) · 2026Pooled it
- Artificial Intelligence for Sleep Bruxism Detection: A Technical Pipeline Synthesis.Diagnostics (Basel, Switzerland) · 2026Review
- Sleep Bruxism: A Narrative Review of Current Concepts, Mechanisms, and Clinical Implications.Journal of the Royal Society of New Zealand · 2026Review
- A new approach for the field detection of sleep bruxism based on inertial sensor data and machine learning classification.Scientific reports · 2026Article
- Comparison between two different registration protocols for the count of sleep bruxism events in a sample of healthy individuals.Clinical oral investigations · 2025Article
- The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview. Part 1: Fundamentals of AI, and its Contemporary Applications in Dentistry.International dental journal · 2025Review
- Understanding the clinical management of co-occurring sleep-related bruxism and obstructive sleep apnea in adults: A narrative and critical review.Journal of prosthodontics : official journal of the American College of Prosthodontists · 2025Review
- Ambulatory devices to detect sleep bruxism: a narrative review.Australian dental journal · 2024Review
- Insights on mandibular jaw movements during polysomnography in obstructive sleep apnea.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2024Article
- Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive Study.Sensors (Basel, Switzerland) · 2023Review
- Diagnostic Accuracy of a Portable Electromyography and Electrocardiography Device to Measure Sleep Bruxism in a Sleep Apnea Population: A Comparative Study.Clocks & sleep · 2023Article
- Diagnostic accuracy of ambulatory polysomnography with electroencephalogram for detection of sleep bruxism-related masticatory muscle activity.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2023Article
- Mandibular jaw movements as a non-invasive measure of respiratory effort during sleep: application in clinical practice.Frontiers in sleep · 2023Review
- Symptoms of Nocturnal Masticatory Muscle Activity among Women of Different Age Groups and Their Association to Obstructive Sleep Apnea-A Cross Sectional Study.Journal of clinical medicine · 2022Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 4 countries.
Funding
Abstract
purposeSleep bruxism (SBx) activity is classically identified by capturing masseter and/or temporalis masticatory muscles electromyographic activity (EMG-MMA) during in-laboratory polysomnography (PSG). We aimed to identify stereotypical mandibular jaw movements (MJM) in patients with SBx and to develop rhythmic masticatory muscles activities (RMMA) automatic detection using an artificial intelligence (AI) based approach. PATIENTS AND
methodsThis was a prospective, observational study of 67 suspected obstructive sleep apnea (OSA) patients in whom PSG with masseter EMG was performed with simultaneous MJM recordings. The system used to collect MJM consisted of a small hardware device attached on the chin that communicates to a cloud-based infrastructure. An extreme gradient boosting (XGB) multiclass classifier was trained on 79,650 10-second epochs of MJM data from the 39 subjects with a history of SBx targeting 3 labels: RMMA episodes (n=1072), micro-arousals (n=1311), and MJM occurring at the breathing frequency (n=77,267).
resultsValidated on unseen data from 28 patients, the model showed a very good epoch-by-epoch agreement (Kappa = 0.799) and balanced accuracy of 86.6% was found for the MJM events when using RMMA standards. The RMMA episodes were detected with a sensitivity of 84.3%. Class-wise receiver operating characteristic (ROC) curve analysis confirmed the well-balanced performance of the classifier for RMMA (ROC area under the curve: 0.98, 95% confidence interval [CI] 0.97-0.99). There was good agreement between the MJM analytic model and manual EMG signal scoring of RMMA (median bias -0.80 events/h, 95% CI -9.77 to 2.85).
conclusionSBx can be reliably identified, quantified, and characterized with MJM when subjected to automated analysis supported by AI technology.
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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.