American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics · 2016
New approach for the diagnosis of extractions with neural network machine learning.
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Jung Seok-Ki, Kim Tae-Woo
Аннотация
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.
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.