A novel expert system for objective masticatory efficiency assessment

PLOS ONE, Nov 2019

Most of the tools and diagnosis models of Masticatory Efficiency (ME) are not well documented or severely limited to simple image processing approaches. This study presents a novel expert system for ME assessment based on automatic recognition of mixture patterns of masticated two-coloured chewing gums using a combination of computational intelligence and image processing techniques. The hypotheses tested were that the proposed system could accurately relate specimens to the number of chewing cycles, and that it could identify differences between the mixture patterns of edentulous individuals prior and after complete denture treatment. This study enrolled 80 fully-dentate adults (41 females and 39 males, 25 ± 5 years of age) as the reference population; and 40 edentulous adults (21 females and 19 males, 72 ± 8.9 years of age) for the testing group. The system was calibrated using the features extracted from 400 samples covering 0, 10, 15, and 20 chewing cycles. The calibrated system was used to automatically analyse and classify a set of 160 specimens retrieved from individuals in the testing group in two appointments. The ME was then computed as the predicted number of chewing strokes that a healthy reference individual would need to achieve a similar degree of mixture measured against the real number of cycles applied to the specimen. The trained classifier obtained a Mathews Correlation Coefficient score of 0.97. ME measurements showed almost perfect agreement considering pre- and post-treatment appointments separately (κ ≥ 0.95). Wilcoxon signed-rank test showed that a complete denture treatment for edentulous patients elicited a statistically significant increase in the ME measurements (Z = -2.31, p < 0.01). We conclude that the proposed expert system proved able and reliable to accurately identify patterns in mixture and provided useful ME measurements.

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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)


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Gustavo Vaccaro, José Ignacio Peláez, José Antonio Gil-Montoya. A novel expert system for objective masticatory efficiency assessment, PLOS ONE, 2018, Volume 13, Issue 1, DOI: 10.1371/journal.pone.0190386