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Related Concept Videos

Assessment of the Mouth01:26

Assessment of the Mouth

A thorough mouth assessment, including inspection and palpation of the lips, gums, tongue, tonsils, uvula, and pharynx, is crucial in detecting potential health issues. Diseases ranging from oral cancer to systemic conditions like diabetes could be identified early through careful oral examination. This article provides a detailed guide on conducting a comprehensive mouth assessment.
Mouth Inspection
The inspection begins with visually examining the mouth for symmetry, color, and size.
Assessment of Ventilation I: Respiratory Rate01:20

Assessment of Ventilation I: Respiratory Rate

Assessment of Ventilation
A Ventilation assessment is critical for monitoring a patient's health status. Respiration, one of the most accessible vital signs, provides insights into the function of numerous body systems and can indicate serious health issues, such as brainstem injuries from head trauma.
Critical Guidelines for Assessing Ventilation:
Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

Assessment of Ventilation II: Respiratory Depth and Rhythm

Respiratory Depth
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
To assess respiratory depth, observe the degree of chest excursion or movement:

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Related Experiment Video

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Quantification of Orofacial Phenotypes in Xenopus
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Classification of postural profiles among mouth-breathing children by learning vector quantization.

F Mancini1, F S Sousa, A D Hummel

  • 1Department of Health Informatics, Federal University of São Paulo (UNIFESP), São Paulo, Brazil.

Methods of Information in Medicine
|September 28, 2010
PubMed
Summary

Learning Vector Quantization (LVQ) identified five distinct postural profiles in children who are mouth breathers. This AI model aids in understanding and classifying abnormal posture related to chronic mouth breathing.

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Area of Science:

  • Musculoskeletal health
  • Artificial Intelligence in Medicine
  • Child Posturology

Background:

  • Mouth breathing is a chronic condition linked to musculoskeletal and postural changes in children.
  • Identifying characteristic postural patterns in mouth breathers presents a significant challenge.
  • Learning Vector Quantization (LVQ) offers a potential AI solution for analyzing these complex postural changes.

Purpose of the Study:

  • To apply LVQ for identifying characteristic postural profiles in mouth-breathing children.
  • To enhance the understanding of abnormal posture associated with mouth breathing.
  • To develop a classification system for mouth breather postural abnormalities.

Main Methods:

  • Utilized postural training and validation datasets from 52 and 32 children, respectively (differentiating mouth and nose breathers).
  • Compared LVQ performance against various machine learning models including self-organizing maps, back-propagation, Bayesian networks, and k-nearest-neighbor.
  • Assessed classifier accuracy using leave-one-out cross-validation, Area Under the ROC Curve (AUC), and Kappa statistics.

Main Results:

  • The LVQ model successfully identified five distinct postural profiles specific to mouth-breathing children.
  • LVQ demonstrated high classification accuracy for differentiating mouth and nose breathers, with sensitivity and specificity rates of 0.90-0.95.
  • The model achieved robust performance on both training and validation datasets.

Conclusions:

  • The five identified postural profiles for mouth breathers were integrated into software.
  • This application aids in classifying the severity of abnormal posture in children with chronic mouth breathing.
  • LVQ proves effective in characterizing and classifying postural deviations in mouth-breathing populations.