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A novel expert system for objective masticatory efficiency assessment

[PDF] Vaccaro_Masticatory.pdf (9.428Mb)
Identificadores
URI: http://hdl.handle.net/10481/49589
DOI: 10.1371/journal.pone.0190386
ISSN: 1932-6203
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Autor
Vaccaro, Gustavo; Peláez, José Ignacio; Gil Montoya, José Antonio
Editorial
Public Library Science
Materia
Eating
 
Entropy
 
Stroke
 
Image processing
 
Digital imaging
 
Principal component analysis
 
Pattern recognition receptors
 
Ecuador
 
Fecha
2018-01-31
Referencia bibliográfica
Vaccaro, G.; Peláez, J.I.; Gil Montoya, J.A. A novel expert system for objective masticatory efficiency assessment. Plos One, 13(1): e0190386 (2018). [http://hdl.handle.net/10481/49589]
Patrocinador
This work was funded by the Secretaria Nacional de Educación, Ciencia y Teconología (SENESCYT) of the Government of Ecuador, with budget allocation No. 0099-SPP, http://www.educacionsuperior.gob.ec.
Resumen
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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