A novel neuro-fuzzy assessment index for orthodontics

Authors:
Anahita Zarei;Michael Hairfield;Seyed S. Mirsaeidghazi
Affiliations:
School of Engineering and Computer Science, University of the Pacific, Stockton, CA;Department of Orthodontics, University of Washington, Seattle, WA;Department of Electrical Engineering, University of Texas, San Antonio
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

Orthodontists have developed several occlusal indices during the past few decades to evaluate treatment success. These indices suffer from several limitations including a crisp decision making criterion that sometimes contradicts human intuition. The objective of this study is to develop an expert system that represent orthodontists' visual perception in assessing patients, using adaptive neuro-fuzzy approach and improving the quality of earlier indices. We examined the reliability of our model by comparing the results with the opinion of a panel of expert orthodontists. Our index has shown a greater consistency with practitioners' clinical judgments compare to previous indices. We conclude that utilization of neural network in this design has led to a superior performance.