SynthesisPediatric pulmonology2022
Reliability of machine learning to diagnose pediatric obstructive sleep apnea: Systematic review and meta-analysis.
Synthesis in Pediatric pulmonology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of artificial intelligence for obstructive sleep apnea detection: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Bridging Data, Semantics, and Clinical Reasoning: A Knowledge Graph Framework for Pediatric Obstructive Sleep Apnea.Children (Basel, Switzerland) · 2026Article
- Combined explainable deep learning model to predict pediatric sleep apnea from ECG and SpOMeasurement : journal of the International Measurement Confederation · 2026Article
- A subject-based association network defines new pediatric sleep apnea phenotypes with different odds of recovery after treatment.Computer methods and programs in biomedicine · 2026Article
- Noninvasive prescreening of pediatric adenoid hypertrophy using quantified MFCC statistics and clinical features: development and external validation.Frontiers in bioengineering and biotechnology · 2026Article
- A 28-Day Ecological Momentary Assessment of Mental Health Among Psychiatric Outpatients With Suicidal Ideation.Journal of advanced nursing · 2026Observational
- Nocturnal blood pressure burden: towards a better understanding of the OSA-hypertension relationship.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- Article
- Artificial intelligence based platform for the automatic and simultaneous explainable detection of apnoea, oxygen desaturation, and artefacts in paediatric polygraphy exams (REST).Scientific reports · 2025Article
- SleepECG-Net: Explainable Deep Learning Approach With ECG for Pediatric Sleep Apnea Diagnosis.IEEE journal of biomedical and health informatics · 2025Article
- An Explainable Deep-Learning Approach to Detect Pediatric Sleep Apnea From Single-Channel Airflow.IEEE journal of translational engineering in health and medicine · 2025Article
- SLA-MLP: Enhancing Sleep Stage Analysis from EEG Signals Using Multilayer Perceptron Networks.Diagnostics (Basel, Switzerland) · 2024Article
- Deep learning approaches for assessing pediatric sleep apnea severity through SpO2 signals.Scientific reports · 2024Article
- Development and application of a machine learning-based predictive model for obstructive sleep apnea screening.Frontiers in big data · 2024Article
- Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis.Frontiers in pediatrics · 2024Article
- Development and Validation of a Nomogram for Predicting Obstructive Sleep Apnea Severity in Children.Nature and science of sleep · 2024Article
- ECG-based convolutional neural network in pediatric obstructive sleep apnea diagnosis.Computers in biology and medicine · 2023Article
- Paediatric sleep apnea event prediction using nasal air pressure and machine learning.Journal of sleep research · 2023Article
- Machine Learning Identification of Obstructive Sleep Apnea Severity through the Patient Clinical Features: A Retrospective Study.Life (Basel, Switzerland) · 2023Article
- Novel Indices to Improve the Diagnostic Ability of Nocturnal Oximetry in Children with OSAS.Children (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundMachine-learning approaches have enabled promising results in efforts to simplify the diagnosis of pediatric obstructive sleep apnea (OSA). A comprehensive review and analysis of such studies increase the confidence level of practitioners and healthcare providers in the implementation of these methodologies in clinical practice.
objectiveTo assess the reliability of machine-learning-based methods to detect pediatric OSA. DATA SOURCES: Two researchers conducted an electronic search on the Web of Science and Scopus using term, and studies were reviewed along with their bibliographic references. ELIGIBILITY CRITERIA: Articles or reviews (Year 2000 onwards) that applied machine learning to detect pediatric OSA; reported data included information enabling derivation of true positive, false negative, true negative, and false positive cases; polysomnography served as diagnostic standard. APPRAISAL AND SYNTHESIS
methodsPooled sensitivities and specificities were computed for three apnea-hypopnea index (AHI) thresholds: 1 event/hour (e/h), 5 e/h, and 10 e/h. Random-effect models were assumed. Summary receiver-operating characteristics (SROC) analyses were also conducted. Heterogeneity (I
resultsNineteen studies were finally retained, involving 4767 different pediatric sleep studies. Machine learning improved diagnostic performance as OSA severity criteria increased reaching optimal values for AHI = 10 e/h (0.652 sensitivity; 0.931 specificity; and 0.940 area under the SROC curve). Publication bias correction had minor effect on summary statistics, but high heterogeneity was observed among the studies.
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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.