A novel expert system for objective masticatory efficiency assessment

Abstract

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.

The influence of habitual consumption of chewing gums in the outcome of masticatory performance tests using two-coloured chewing gums

“…First, an operator instructed the patient to chew on a test-food sample for 20 chewing strokes, on the preferred mastication side and at a comfortable speed 12,16,17 . Secondly, the masticated bolus was retrieved and placed between two transparent plastic sheets.…”

“…Thirdly, the wafer composed of the chewing-gum and plastic sheets was pressed to a 1 mm thick using a screw-driven press 12 . The influence of pressing the masticated chewing gums to a 1 mm thick wafer has been addressed in previous studies 12,13,17,24 ; although there are no studies that focus solely on this topic, the overall results of previous studies suggest that accurate mixture information can be extracted from a 1 mm thick wafer if the pressing procedure was conducted using a calibrated press. Fourthly, the flattened wafer was scanned on both sides using a Canoscan Lide 220® flatbed scanner (300 dpi, standard calibration parameters for colour digitalization).…”

“…Afterwards, the MP of the sample was analysed by computing the Variance of the Histogram of the Hue channel of the HSI colour space (VhH). The VhH has been used previously as a proxy measure of the mixture of two-coloured chewing-gums 12,17 .…”

“…One of the fastest and easiest routines for objective MP assessment is the quantification of the mixture of a two-coloured chewing-gum specimen subjected to mastication 4,12–14 . Several studies proposed digital image analysis approaches for mixture quantification, ranging from simple feature extraction procedures 13,15 , to complex multi-feature comparisons 4,12,16 and pattern recognition using computational intelligence 17 . The two-coloured chewing-gum mixture quantification procedures are simple and easy to reproduce, as they do not require specialized equipment or training.…”

“…The two-coloured chewing-gum mixture quantification procedures are simple and easy to reproduce, as they do not require specialized equipment or training. However, MP measures obtained this way are prone to high standard deviations regardless of the image processing approach 12,17 . Therefore, current research in this area aims to enhance the accuracy of the procedure.…”