Paraconsistent Artificial Neural Network as Auxiliary in Cephalometric Diagnosis

Abstract

This work presents an application of the paraconsistent artificial neural network (PANN) in the analysis of cephalometric variables and provides an orthodontic diagnosis. An expert's analysis is subject to the inherent imprecision of measurements, registers, and individual variability of physician visual analysis. Patient input cephalometric values are compared with means drawn from individuals considered normal in the cephalometric point of view by the PANN. This reference is constituted by individuals from 6 to 18 years old, both genders. The applied cephalometric analysis was targeted to measure skeletal and dental discrepancies and established a cephalometric diagnosis. The analysis results in degrees of skeletal, anteroposterior, and dental discrepancy, pertinent to upper and lower incisors. A sample of 120 orthodontic patients was processed by the proposed model and three orthodontic experts. Comparisons between the model and the human expert's performance provided kappa indexes that varied from moderate to almost perfect agreement. The agreement between the model and specialist's performance was equivalent. In addition, the model pointed out contradictions presented in the data that were not noticed by the orthodontists, which highlight the contribution that this kind of system could carry out in the orthodontics decision support.

Paraconsistency in hybrid logic

“…They use the information provided by cephalometric analysis to perform pattern recognition then, from determined parameters, it is established the best treatment for the patient, narrated in [2], and [75].…”

The Artificial Intelligence Approach for Diagnosis, Treatment and Modelling in Orthodontic

“…The precision of the system increase as much as data is added. Another work was made by Mario et al [75] to overcome these interpretational shortcomings, once again suggesting the contribution of Mathematics to Biology, better translating natural phenomena. Moreover, single correlations are insufficient for the assessment of facial patterns as many variables must be simultaneously considered in order to establish patterns.…”

“…Abe [74] and Mario et al [75]mentioned important limitations that conventional cephalometric holds, mostly due to the fact that the cephalometric variables are not assessed under a contextualized scope and carry on important variation when compared to samples norms. Because of that, its clinical application is subjective.…”

“…The model suggested by Mario et al [75] utilize selected set of cephalometric variables based on expertise (Figs. 9 a and b).…”

“…And once again the paraconsistent logic suggested as a model for detection and treatment of contradictions, enriching the use of soft mathematics tools in biology. research intends to test such model, it is reasonable to expect that the proposed model can well detect inconsistencies and better interpret craniofacial morphology [75], the cephalometric diagnostic model used logical states which represented in figure 7. In the PANN, the main aim is to know how to determine the certainty degree concerning a proposition, if it is False or True.…”