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KJO Korean Journal of Orthodontics

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pISSN 2234-7518
eISSN 2005-372X
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Korean J Orthod

Published online March 7, 2022

Copyright © The Korean Association of Orthodontists.

Prediction of patient experience of Invisalign treatment using artificial neural networks

Lin Xu1, Li Mei2, Ruiqi Lu3, Yuan Li1, Hanshi Li1, Yu Li1*

1State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
2Discipline of Orthodontics, Department of Oral Sciences, Sir John Walsh Research Institute, Faculty of Dentistry, University of Otago, Dunedin, New Zealand.
3Department of Electronic Engineering, Tsinghua University, Beijing, China.

Correspondence to:Yu Li, Department of Orthodontics, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, 14#, 3rd Section, South Renmin Road, Chengdu 610041, China. E-mail: yuli@scu.edu.cn. Phone number: 028-85503645.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objectives: Poor experience of Invisalign treatment will affect patient compliance, and thus the treatment outcome. Knowing potential discomfort level for a specific case in advance can help orthodontists better prepare for the patient to overcome the tough stage. The aim of this study was to construct artificial neural networks (ANNs) for predicting patient experience in the early stage of Invisalign treatment.
Methods: A total of 196 patients were included in the study. Data collection included the questionnaires of pain, anxiety, and quality of life (QoL). Four-layer fully connected multilayer perceptions (MLP) with three back propagation were constructed for predicting patient experience of the treatment. Input data consisted of 17 clinical features. The partial derivatives method was used to calculate relative contributions of each input in ANNs.
Results: The success rates of prediction were 87.7% for pain, 93.4% for anxiety, and 92.4% for QoL. The ANNs of predicting pain, anxiety, and QoL yielded the area under the curve of 0.963, 0.992, and 0.982, respectively. The number of teeth with lingual attachments was found to be the most important factor affecting the outcome of negative experience, followed by the number of lingual buttons, and upper incisors with attachments.
Conclusions: The preliminary study has constructed ANNs which show good accuracy in predicting patient experience (i.e. pain, anxiety and QoL) of Invisalign treatment, bearing a potential for clinical use with further enlarged learning in future.

Keywords: Computer algorithm, Pain, Compliance, Aligners.

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Korean J Orthod

Published online March 7, 2022

Copyright © The Korean Association of Orthodontists.

Prediction of patient experience of Invisalign treatment using artificial neural networks

Lin Xu1, Li Mei2, Ruiqi Lu3, Yuan Li1, Hanshi Li1, Yu Li1*

1State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
2Discipline of Orthodontics, Department of Oral Sciences, Sir John Walsh Research Institute, Faculty of Dentistry, University of Otago, Dunedin, New Zealand.
3Department of Electronic Engineering, Tsinghua University, Beijing, China.

Correspondence to:Yu Li, Department of Orthodontics, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, 14#, 3rd Section, South Renmin Road, Chengdu 610041, China. E-mail: yuli@scu.edu.cn. Phone number: 028-85503645.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objectives: Poor experience of Invisalign treatment will affect patient compliance, and thus the treatment outcome. Knowing potential discomfort level for a specific case in advance can help orthodontists better prepare for the patient to overcome the tough stage. The aim of this study was to construct artificial neural networks (ANNs) for predicting patient experience in the early stage of Invisalign treatment.
Methods: A total of 196 patients were included in the study. Data collection included the questionnaires of pain, anxiety, and quality of life (QoL). Four-layer fully connected multilayer perceptions (MLP) with three back propagation were constructed for predicting patient experience of the treatment. Input data consisted of 17 clinical features. The partial derivatives method was used to calculate relative contributions of each input in ANNs.
Results: The success rates of prediction were 87.7% for pain, 93.4% for anxiety, and 92.4% for QoL. The ANNs of predicting pain, anxiety, and QoL yielded the area under the curve of 0.963, 0.992, and 0.982, respectively. The number of teeth with lingual attachments was found to be the most important factor affecting the outcome of negative experience, followed by the number of lingual buttons, and upper incisors with attachments.
Conclusions: The preliminary study has constructed ANNs which show good accuracy in predicting patient experience (i.e. pain, anxiety and QoL) of Invisalign treatment, bearing a potential for clinical use with further enlarged learning in future.

Keywords: Computer algorithm, Pain, Compliance, Aligners.