Seok-Ki Jung1, Tae-Woo Kim2. 1. Clinical instructor, Department of Orthodontics, Korea University Ansan Hospital, Ansan, Korea. 2. Professor, Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, Seoul, Korea. Electronic address: taewoo@snu.ac.kr.
Abstract
INTRODUCTION: The decision to extract teeth for orthodontic treatment is important and difficult because it tends to be based on the practitioner's experiences. The purposes of this study were to construct an artificial intelligence expert system for the diagnosis of extractions using neural network machine learning and to evaluate the performance of this model. METHODS: The subjects included 156 patients. Input data consisted of 12 cephalometric variables and an additional 6 indexes. Output data consisted of 3 bits to divide the extraction patterns. Four neural network machine learning models for the diagnosis of extractions were constructed using a back-propagation algorithm and were evaluated. RESULTS: The success rates of the models were 93% for the diagnosis of extraction vs nonextraction and 84% for the detailed diagnosis of the extraction patterns. CONCLUSIONS: This study suggests that artificial intelligence expert systems with neural network machine learning could be useful in orthodontics. Improved performance was achieved by components such as proper selection of the input data, appropriate organization of the modeling, and preferable generalization.
INTRODUCTION: The decision to extract teeth for orthodontic treatment is important and difficult because it tends to be based on the practitioner's experiences. The purposes of this study were to construct an artificial intelligence expert system for the diagnosis of extractions using neural network machine learning and to evaluate the performance of this model. METHODS: The subjects included 156 patients. Input data consisted of 12 cephalometric variables and an additional 6 indexes. Output data consisted of 3 bits to divide the extraction patterns. Four neural network machine learning models for the diagnosis of extractions were constructed using a back-propagation algorithm and were evaluated. RESULTS: The success rates of the models were 93% for the diagnosis of extraction vs nonextraction and 84% for the detailed diagnosis of the extraction patterns. CONCLUSIONS: This study suggests that artificial intelligence expert systems with neural network machine learning could be useful in orthodontics. Improved performance was achieved by components such as proper selection of the input data, appropriate organization of the modeling, and preferable generalization.
Authors: Najla Al Turkestani; Jonas Bianchi; Romain Deleat-Besson; Celia Le; Li Tengfei; Juan Carlos Prieto; Marcela Gurgel; Antonio C O Ruellas; Camila Massaro; Aron Aliaga Del Castillo; Karine Evangelista; Marilia Yatabe; Erika Benavides; Fabiana Soki; Winston Zhang; Kayvan Najarian; Jonathan Gryak; Martin Styner; Jean-Christophe Fillion-Robin; Beatriz Paniagua; Reza Soroushmehr; Lucia H S Cevidanes Journal: Orthod Craniofac Res Date: 2021-05-24 Impact factor: 1.826
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