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Related Concept Videos

Sleep Apnea01:21

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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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Airway management is a key skill in emergency and critical care settings, as maintaining a clear airway is essential for adequate oxygenation and ventilation.Head Tilt-Chin Lift TechniqueThe head tilt-chin lift maneuver is an essential technique primarily used in patients without suspected cervical spine injuries. To perform this maneuver, one hand is placed on the patient’s forehead, and gentle pressure is applied backward to tilt the head. The fingertips of the other hand are positioned...
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Related Experiment Video

Updated: Jan 11, 2026

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Artificial intelligence in imaging for obstructive sleep apnea: A comprehensive review.

Xiaoxuan Zhang1, Zhenliang Xiong2, Yinglin Zhou3

  • 1Medical College, Guizhou University, Guizhou, 550000, China; Department of Nuclear Medicine, Guizhou Provincial People's Hospital, Guizhou, 550002, China.

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|November 13, 2025
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Summary

Artificial intelligence (AI) in medical imaging offers a promising alternative to polysomnography for diagnosing Obstructive Sleep Apnea (OSA). This review compares AI applications across imaging techniques, proposing a framework for future use.

Keywords:
Artificial intelligenceDeep learningMedical imagingObstructive sleep apneaUpper airway

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Sleep Medicine

Background:

  • Obstructive Sleep Apnea (OSA) is common, but polysomnography (PSG) is expensive and complex.
  • Artificial intelligence (AI) in medical imaging presents a viable alternative for analyzing OSA's anatomical causes.
  • A systematic comparison of AI across different imaging modalities for OSA is currently lacking.

Purpose of the Study:

  • To systematically review and evaluate AI applications in various medical imaging techniques for OSA diagnosis and management.
  • To compare the strengths and limitations of different AI-enhanced imaging modalities for OSA.
  • To propose a framework to guide the selection of imaging modalities in clinical practice.

Main Methods:

  • A comprehensive narrative review of original research was conducted.
  • Six major scientific databases were searched (PubMed, Web of Science, Scopus, IEEE Xplore, Embase, ScienceDirect) up to June 2025.
  • Fifty-four studies applying AI to medical images for OSA diagnosis, severity assessment, or phenotyping were included.

Main Results:

  • Computed tomography (CT) and magnetic resonance imaging (MRI) were the most common imaging modalities.
  • AI was extensively used for upper airway segmentation, disease prediction, severity assessment, and phenotyping.
  • AI significantly improved analytical efficiency and objectivity compared to manual methods.

Conclusions:

  • AI shows substantial promise for improving OSA diagnosis and management.
  • A novel five-layer decision-making framework is proposed to guide modality selection for diverse clinical scenarios.
  • Future advancements require large, standardized datasets, enhanced model explainability, and rigorous clinical validation.