Assessment of chewing efficiency with artificial intelligence

Abstract

BACKGROUND: Artificial intelligence (AI) is a system based on machine learning of neural networks. AI structure resembles nerve tissue, having the so called “neurons”, i.e. mathematical codes. A neural network has three levels, including the input layer (information enters the system), the hidden layer (multidimensional data is analyzed), and the output layer (the system generates conclusions). Current neural networks use a “perceptron”, i.e. a neuron consisting of a large number of interconnected input and hidden layers, which makes the system capable of self-learning and analysis of non-linear data and generalization and processing of incomplete information, including the method of projection onto latent structures.

AIM: To develope a program based on multivariate data analysis to determine the chewing efficiency at the stages of prosthetics.

METHODS: In 2016, the Department of Orthopedic Dentistry and Orthodontics developed and tested a program for determining chewing efficiency based on the analysis of digital occlusiograms obtained by scanning dental prints on a wax plate. The results were processed by mathematical methods of multivariate data analysis using the projection on latent structures (the partial least-squares [PLS-2] model), which allowed assessing the relationship between the value of chewing efficiency and the area and brightness characteristics of the areas corresponding to occlusal contacts. The program compared the measurement results with the reference occlusiograms in the database and gave a conclusion. The experiments yielded statistically significant results for the efficiency of the program compared to traditional chewing tests. Due to the relevance of implementing AI in orthopedic treatment, the decision was made to improve the training methods of the program to update the existing array of reference data. Starting in 2019, 24 occlusiograms were added to the previously received data with dental defects from 9 to 12 teeth. Using an expanded database, the program, when analyzing the occlusiogram of a new patient, allowed to consider changes in chewing efficiency obtained earlier with the Trezubov chewing test for various defects of the dentition and compare the reference and minimally achievable values of chewing efficiency. The program algorithm was verified by the researchers using the classical Trezubov chewing test. Chewing efficiency was measured as a percentage.

RESULTS: The obtained combinations of the digital algorithm parameters for assessing chewing efficiency resulted in increased accuracy in the range of 4%–6% compared to the traditional Rubinov and Ryakhovsky chewing tests.

CONCLUSIONS: A digital algorithm for assessing chewing efficiency allows for quick and accurate assessment without the use of time-consuming analog tests.

Keywords

chewing efficiency / occlusiogram / analysis / multidimensional scaling / machine intelligence / dentistry / artificial intelligence

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