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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.
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Related Experiment Video

Updated: Jul 2, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Breath-based detection of oral diseases using sensors and machine learning.

Maisa Haiek1, Yoav Broza2, Adi Farber3

  • 1Institute of Dental Sciences, Hadassah School of Dental Medicine, The Hebrew University Jerusalem, 91120, Israel; Department of Prosthodontics, Hadassah School of Dental Medicine, The Hebrew University, Jerusalem, 91120, Israel.

Journal of Dentistry
|June 30, 2026
PubMed
Summary

Breath volatile organic compounds (VOCs) show promise for diagnosing oral inflammatory conditions like periodontitis. Nanosensor arrays and machine learning accurately classified inflammatory states, though early disease detection remains challenging.

Keywords:
BiomarkerBreathomicsCariesDiagnosisGas chromatography-mass spectrometryGingivitisImplantMachine learningPeri-implantitisPeriodontitisVolatile organic compound

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Area of Science:

  • Oral medicine and diagnostics
  • Biomarker discovery
  • Volatolomics

Background:

  • Clinical diagnosis of oral conditions like caries, gingivitis, periodontitis, and peri-implantitis can be subjective.
  • Objective diagnostic tools are needed to improve accuracy and reduce variability.
  • Exhaled volatile organic compounds (VOCs) offer a non-invasive medium reflecting metabolic activity.

Purpose of the Study:

  • To evaluate the feasibility of using nanosensor array outputs to classify oral phenotypes based on breath VOC profiles.
  • To assess the diagnostic potential of exhaled VOCs for distinguishing between caries, gingivitis, periodontitis, and peri-implantitis.

Main Methods:

  • Analysis of exhaled VOCs from 346 participants using Gas Chromatography-Mass Spectrometry (GC-MS) and a 40-sensor nanoarray.
  • Statistical analysis including age adjustment, filtering, log transformation, Kruskal-Wallis tests, and Bonferroni correction.
  • Application of Principal Component Analysis (PCA), Canonical Discriminant Analysis (CDA), and supervised Machine Learning (ML) for data interpretation.

Main Results:

  • GC-MS identified specific VOCs differentiating periodontitis from caries and gingivitis, and others distinguishing implant-related conditions.
  • Nanoarray CDA revealed significant functions explaining substantial variance in the data.
  • A 10-sensor ML model achieved moderate overall accuracy (58.8% validation), with high sensitivity for periodontitis/healthy implants and high specificity across conditions.

Conclusions:

  • Nanosensor-based volatilomic analysis shows potential for non-invasive oral disease diagnostics, particularly for inflammatory conditions like periodontitis and peri-implantitis.
  • Current limitations include variable performance across all disease stages, especially early or non-inflammatory conditions.
  • Further advancements in sensor technology and ML are needed to improve model robustness and clinical applicability.