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Related Concept Videos

Sleep Apnea01:21

Sleep Apnea

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Sleep apnea is a condition where breathing stops intermittently during sleep, often leading to significant health issues. Each episode can last from 10 to 20 seconds or more and is frequently accompanied by a brief arousal from sleep. This disturbance, largely unnoticed by the individual, can lead to severe daytime fatigue. Commonly, individuals seek help after being informed by their partners about loud snoring and noticeable breathing pauses during sleep.
The condition is more prevalent among...
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Noninvasive positive-pressure ventilation (NIPPV), continuous positive airway pressure (CPAP), and bilevel positive airway pressure (BiPAP) are essential methods in respiratory care. These ventilation techniques offer unique benefits for patients with various respiratory conditions, providing adequate support without requiring intubation. Let's explore how each method is crucial in improving patient outcomes and enhancing respiratory therapy.
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Related Experiment Video

Updated: May 20, 2025

Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea
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Artificial intelligence in obstructive sleep apnea: A bibliometric analysis.

Xing An1, Jie Zhou1, Qiang Xu2

  • 1Clinical Medical College, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.

Digital Health
|March 24, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) is increasingly used in obstructive sleep apnea (OSA) research, with significant growth observed since 2017. Future applications will focus on diagnosis, personalized treatment, and management, enhancing sleep health outcomes.

Keywords:
Artificial intelligencebibliometric analysisdeep learningmachine learningobstructive sleep apnea

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Area of Science:

  • Bibliometric analysis
  • Sleep medicine
  • Artificial intelligence

Background:

  • Obstructive sleep apnea (OSA) is a prevalent condition requiring advanced diagnostic and management strategies.
  • Artificial intelligence (AI) offers novel approaches to understanding and treating OSA.

Purpose of the Study:

  • To conduct a bibliometric analysis of AI applications in OSA research.
  • To explore current trends, key contributors, and future directions in AI for OSA.

Main Methods:

  • Bibliometric analysis of 867 articles from Web of Science (2011-2024).
  • Utilized VOSviewer and Citespace for visualization of countries, institutions, authors, and keywords.
  • Descriptive analysis performed using Microsoft Excel 2021.

Main Results:

  • Publication numbers increased significantly after 2017.
  • China led in publications; Universidad de Valladolid and IEEE Journal of Biomedical and Health Informatics were most productive.
  • Dominant keywords included OSA, machine learning, Electrocardiography, and deep learning.

Conclusions:

  • AI, especially deep learning, is crucial for OSA diagnosis, treatment, and management.
  • Future research should prioritize international collaboration and interdisciplinary communication.
  • AI advancements promise more precise, convenient, and personalized sleep health services.