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Updated: Sep 7, 2025

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Assessing masticatory performance with a colour-mixing ability test using smartphone camera images.

Martin Schimmel1,2, Elias Rachais1, Nadin Al-Haj Husain1

  • 1Department of Reconstructive Dentistry and Gerodontology, School of Dental Medicine, University of Bern, Bern, Switzerland.

Journal of Oral Rehabilitation
|June 22, 2022
PubMed
Summary

Smartphone images reliably assess masticatory performance using colour-mixing tests, though they may slightly underestimate results compared to scanners. Centralized analysis of images can improve diagnostic reliability.

Keywords:
ViewGumcolour-mixing abilityhue-check gummasticatory performancesmartphone

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Area of Science:

  • Dental Science
  • Biomedical Engineering
  • Diagnostic Imaging

Background:

  • Colour-mixing ability tests are standard for evaluating masticatory performance.
  • Current image acquisition methods can be complex and sensitive to technique.

Purpose of the Study:

  • To assess the reliability of smartphone camera images for masticatory performance evaluation.
  • To compare smartphone image acquisition with traditional flatbed scanners.

Main Methods:

  • Three participant groups based on dental status (fully dentate, removable partial denture, complete denture) were studied.
  • Hue-Check Gum specimens were imaged using smartphones and a flatbed scanner.
  • Images were analyzed subjectively (SA) and opto-electronically (VoH), with inter- and intra-rater reliability assessed.

Main Results:

  • All imaging methods differentiated masticatory performance across dental states.
  • Subjective assessment showed good to excellent reliability; opto-electronic analysis with smartphones was reliable but underestimated performance versus scanners.
  • Specimen aging up to seven days did not significantly impact results.

Conclusions:

  • The Hue-Check Gum© test is a reliable method for assessing masticatory performance.
  • Smartphone imaging is a viable, though potentially underestimating, alternative to scanners.
  • Centralized analysis of smartphone images can enhance diagnostic consistency.