Edition V16N02 | Year 2011 | Editorial Article | Pages 75 to 84
Introduction: Classical parametric assessments and isolated cephalometric variables may not provide the best information in craniofacial morphology. Rather, contextualized cephalometrics can be more promising, since it allows for integration among weighty cephalometric variables. Objective: The main purpose of this manuscript is to present the application of a non-trivial mathematical model in cephalometrics, providing data mining by filtering certainty and contradiction in each network ?node?. Methods: In the proposed ?neural network?, each ?cell? is connected to others ?cells? by ?synapses?. Such decision-making system is an artificial intelligence tool tailored to potentially increase the meaning of assessed data. Results: The comparison between the final diagnosis provided by the paraconsistent neural network with the opinions of three examiners was heterogeneous. Kappa agreement was fair for anteroposterior discrepancies, substantial or fair for vertical discrepancies and moderate for dental discrepancies. For the bimaxillary dental protrusion, the agreement was almost perfect. Similarly, the agreement among the three examiners, without any software aid, was just moderate for skeletal and dental discrepancies. An exception was dental protrusion, which agreement was almost perfect. Conclusions: In conclusion, the analysis of performance of the developed technology supports that the presented electronic tool might match human decisions in the most of the events. As an expected limitation, such mathematical-computational tool was less effective for skeletal discrepancies than for dental discrepancies.