Analysis of craniometric variables with Paraconsistent Logic

This work develops a model for analysis of craniometric variables that uses the Paraconsistent Artificial Neural Networks, based on the Annotated Paraconsistent Logic of two values. Such logic has the ability to measure uncertainty, inconsistency and paracompleteness. Paraconsistent Logic has been employed in several applications subject to these situations, constituting a new mathematical tool in Artificial Intelligence. The cephalometric analysis proposed here consists of quantifying skeletal and dental discrepancies under Paraconsistent Logic. The use of Paraconsistent Artificial Neural Networks allows the method to add a factor of uncertainty, respecting traditional orthodontic diagnosis, while contextualizing different craniofacial regions. The result of the analysis consists of the degrees of skeletal, anteroposterior and vertical discrepancy, and degrees of dental discrepancy, relative to the lower and upper incisors.