A novel expert system for objective masticatory efficiency assessment
January
A novel expert system for objective masticatory efficiency assessment
Gustavo Vaccaro 1 2
Jose Ignacio Pela ez 0 2
Jose Antonio Gil-Montoya 2
0 Department of Languages and Computer Sciences, University of Malaga , Malaga , Spain , 3 Prometeo Project, National Secretary of Higher Education, Science, Technology and Innovation (SENESCYT), University of Guayaquil, Guayaquil, Ecuador, 4 Gerodontology Department, School of Dentistry, Granada University , Granada , Spain
1 International Postgraduate School, School of Dentistry, Granada University , Granada , Spain
2 Editor: Marco Magalhaes, University of Toronto , CANADA
OPEN ACCESS Funding: This work was funded by the Secretaria Nacional de EducacioÂn, Ciencia y TeconologÂõa (SENESCYT) of the Government of Ecuador, with budget allocation No. 0099-SPP, http://www. educacionsuperior.gob.ec.
-
Data Availability Statement: All source code files
and MEPAT dataset will be available at the GitHub
repository at: https://github.com/fabianvaccaro/
perceptodent.
Competing interests: The authors have declared
that no competing interests exist.
Introduction
Objective evaluation of the masticatory function
Health care services for the elderly and physically disabled population are ever-increasing
challenges where practitioners are required to evaluate the functional impairments of individuals
in faster and more accurate ways while using less invasive methods. One approach to this
matter is the objective evaluation of the human mastication, which is a complex biomechanical
process that involves coordinated movements of the jaw, tongue, lips, and cheek; and one of
the main functions of the stomatognathic system. [
1
].
Objective mastication assessment can be performed in two ways: firstly, by quantifying the
changes that the food has suffered during mastication, i.e. the Masticatory Performance; and
secondly, by calculating the number of chewing strokes that would be required to achieve a
certain degree of food degradation, i.e. the Masticatory Efficiency [
2
]. The Masticatory
Performance (MP) has been defined as: a measure of the comminution of food attainable under
standardized testing conditions [
3
]; and is considered an objective indicator of oral functional
capabilities, widely used to measure the impact of dental treatments, to assess levels of
disability and orofacial functional impairments following stroke [
4,5
], and has also been associated
with malnutrition risk [6]. On the other hand, the Masticatory Efficiency (ME) has been
originally defined as the number of extra chewing strokes needed by the patient to achieve the same
pulverization as the standard person [
7
]; however, the strict measurement of the ME is
presently in disuse, mainly because patients with impaired mastication would need to masticate for
very large periods of time. Furthermore, it is important to notice that several studies used the
terms MP and ME interchangeably while referring exclusively to the MP.
Current MP assessment techniques are based on the objective quantification of the
degradation of a test-food subjected to mastication. The degradation level is determined by
measuring a property (colour, weight, median particle size, chemical concentration, etc.) of a piece of
natural or artificial food (e.g. Optosil/OptocalTM, peanuts, ham, chewing gums, paraffin
blocks, carrots, jelly gums, etc.), where the property is prone to changes related to the number
of chewing strokes.
The fastest and easiest routine for objective MP assessment is the mixture quantification of
a two-coloured cohesive specimen subjected to mastication [8±10]. In a mixing test a test-food
specimen is formed by two differently-coloured layers of chewing gum or paraffin stacked
together. Previous studies suggest that there are similarities among the visual characteristics of
chewing gums masticated for the same number of chewing strokes when considering young
and healthy human subjects [
11
]. These similarities have allowed experts to subjectively
identify the mixture using comparison tables. An example set of masticated specimens for 3, 9, 15,
and 25 chewing cycles is shown in Fig 1; where it is possible to notice that the red and white
layers are mixed, to some extent, in a regular fashion. The amount of mixture reached with
each chewing cycle would depend on the masticatory function of the individual and on the
structural characteristics of the specimen such as the size, thickness, density, hardness,
viscosity, and tinctures used for colouring.
Several studies have proposed simple digital image analysis approaches for mixture
quantification that are more precise than visual inspection techniques, and modern mixing tests
currently focus on these kinds of procedures [
8
]. The first attempt to measure the mixture of food
using digital image analysis employed several custom-made algorithms, but these were not
fully described, hence not possible to replicate [
12
]. Later on, the magi (...truncated)