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Related Concept Videos

Sleep Apnea01:21

Sleep Apnea

333
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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Pharynx01:20

Pharynx

4.7K
The pharynx, a tubular structure framed by skeletal muscle and lined with mucous membrane, extends continuously from the nasal cavities. It is segmented into three major areas: the nasopharynx, oropharynx, and laryngopharynx.
Nasopharynx
The nasopharynx, bordered by the conchae of the nasal cavity, serves exclusively as an air conduit. In its superior region, the pharyngeal tonsils or adenoids are located. These tonsils are clusters of lymphoid reticular tissue akin to a lymph node. The precise...
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Related Experiment Video

Updated: Nov 29, 2025

Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea
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Classification of Pharynx from MRI Using a Visual Analysis Tool to Study Obstructive Sleep Apnea.

Muhammad Laiq Ur Rahman Shahid1, Junaid Mir2, Furqan Shaukat3

  • 1Department of Computer Science and Electrical Engineering, Jacobs University, Bremen, Germany.

Current Medical Imaging
|November 20, 2020
PubMed
Summary
This summary is machine-generated.

A new classifier accurately identifies the pharynx in MRI scans, aiding obstructive sleep apnea (OSA) research. This automated method improves efficiency and provides insights into OSA pathogenesis.

Keywords:
MRIMachine learning algorithmOSAclassificationmedical image analysismultidimensional feature spacevisual analysis

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Sleep Medicine

Background:

  • Obstructive sleep apnea (OSA) is a prevalent chronic sleep disorder.
  • Understanding pharyngeal anatomy is key to OSA pathogenesis.
  • Pharyngeal classification is essential for OSA analysis.

Purpose of the Study:

  • To develop an automated classifier for pharyngeal segmentation from MRI data.
  • To improve the efficiency and accuracy of pharyngeal analysis in OSA research.

Main Methods:

  • A visual analysis-based classification pipeline was developed.
  • Stages include pre-processing, feature extraction (categorical and numerical), and supervised classification.
  • The classifier was trained using visual analytics and silhouette coefficient.

Main Results:

  • The automated classifier achieved an 86% Jaccard coefficient for pharyngeal classification on MRI datasets.
  • Expert knowledge can optimize feature selection and weighting during classifier training.

Conclusions:

  • The proposed classifier is accurate and computationally efficient.
  • It offers insights into individual and collective feature influences.
  • Applications include large-scale epidemiological studies for OSA analysis.