Healthc Inform Res.  2017 Oct;23(4):255-261. 10.4258/hir.2017.23.4.255.

Clinical Decision Support Model to Predict Occlusal Force in Bruxism Patients

• Thanathornwong B
• Suebnukarn S

• KMID: 2396416
• DOI: http://doi.org/10.4258/hir.2017.23.4.255

Abstract

OBJECTIVES
The aim of this study was to develop a decision support model for the prediction of occlusal force from the size and color of articulating paper markings in bruxism patients.
METHODS
We used the information from the datasets of 30 bruxism patients in which digital measurements of the size and color of articulating paper markings (12-µm Hanel; Coltene/Whaledent GmbH, Langenau, Germany) on canine protected hard stabilization splints were measured in pixels (P) and in red (R), green (G), and blue (B) values using Adobe Photoshop software (Adobe Systems, San Jose, CA, USA). The occlusal force (F) was measured using T-Scan III (Tekscan Inc., South Boston, MA, USA). The multiple regression equation was applied to predict F from the P and RGB. Model evaluation was performed using the datasets from 10 new patients. The patient's occlusal force measured by T-Scan III was used as a "˜gold standard' to compare with the occlusal force predicted by the multiple regression model.
RESULTS
The results demonstrate that the correlation between the occlusal force and the pixels and RGB of the articulating paper markings was positive (F = 1.62×P + 0.07×R -0.08×G + 0.08×B + 4.74; R 2 = 0.34). There was a high degree of agreement between the occlusal force of the patient measured using T-Scan III and the occlusal force predicted by the model (kappa value = 0.82).
CONCLUSIONS
The results obtained demonstrate that the multiple regression model can predict the occlusal force using the digital values for the size and color of the articulating paper markings in bruxism patients.

Keyword

Bite Force; Occlusal Splints; Bruxism; Decision Making; Logistic Models

MeSH Terms

Bite Force*
Bruxism*
Dataset
Decision Making
Decision Support Systems, Clinical*
Decision Support Techniques
Humans
Logistic Models
Occlusal Splints
Splints

Figure

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  Figure 1 The digital measurements of the size in pixels and the colors in red (R), green (G), and blue (B) of the articulating paper markings on canine-protected hard stabilization splints were recognized using Adobe Photoshop software (Adobe Systems, San Jose, CA, USA).

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  Figure 2 Occlusal force distribution of one participant from the T-Scan III system (Tekscan Inc., South Boston, MA, USA).

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  Figure 3 The regression lines between the occlusal force values (%) measured using the T-Scan III system (Tekscan Inc., South Boston, MA, USA) and the size values in pixels, the color values in red (R), the color values in green (G), and the color values in blue (B) for the articulating paper marks using Adobe Photoshop software (Adobe Systems, San Jose, CA, USA).

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  Figure 4 The occlusal force prediction of one articulation paper marking using the digital values of size in pixels and color in the RGB mode.

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