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Hindi A Novel Advance for Orthodontic Landmarks Recognition Using an Artificial Neural Network
*Corresponding Author: Ali Mohammad Saghiri, Adjunct Assistant Professor, DHAL Research Institute, Tehran, Iran, Email: amsaghiriit@gmail.comReceived Date: Nov 06, 2020 / Accepted Date: Nov 13, 2020 / Published Date: Nov 27, 2020
Citation: Saghiri AM (2020) A New Approach for Orthodontic Landmarks Identiication using an Artiicial Neural Network. J Oral Hyg Health 8: 266
Copyright: © 2020 Ali Mohammad S. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background: Cephalometric analysis is the clinical application of dental cephalometry. It is investigation of the dental and skeletal connections of a human skull. Cephalometric analysis is one of most difficult part for orthodontic and orthogenetic surgical treatments. Most of time landmark identifications is time consuming and has high dependency to operator. the aim of current investigation is to find a new approach for orthodontic landmarks identification using an artificial neural network to enhance identification of cephalometric landmarks.
Materials and Methods: 110 lateral cephalograms were randomly selected from orthodontic private office and spited in two parts, First for training artificial neural network (ANN) and the remain cephalograms used for the evaluation of the software. In blind manner we asked three orthodontists to locate 5 landmarks on software and used these information for training. After that, our algorithm identified 5 landmarks on rest of cephalograms automatically. Eventually the result of both Algorithm evaluation and orthodontists landmarks for second part were compared with each other by "paired T test".
Results: Current Investigation showed, in four points out of five, the mean average distance between the point determined by ANN and the orthodontist’s points, was less than 1mm accuracy for all four landmarks.
Conclusion: With the limitation of this study, the results confirmed that that the landmark locating errors by ANN algorithms has near enough accuracy to realization, therefore it could be a proper substitute for manual method.
