대한치과교정학회지 (The korean journal of orthodontics)

  • 제52권4호
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  • Pages.268-277
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  • 2022
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  • 2234-7518(pISSN)
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  • 2005-372X(eISSN)
대한치과교정학회 (The Korean Association Of Orthodontists)
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Predicting patient experience of Invisalign treatment: An analysis using artificial neural network

  • Xu, Lin (Department of Orthodontics, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University) ;
  • Mei, Li (Discipline of Orthodontics, Department of Oral Sciences, Sir John Walsh Research Institute, Faculty of Dentistry, University of Otago) ;
  • Lu, Ruiqi (Department of Electronic Engineering, Tsinghua University) ;
  • Li, Yuan (Department of Orthodontics, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University) ;
  • Li, Hanshi (Department of Orthodontics, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University) ;
  • Li, Yu (Department of Orthodontics, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University)
  • 투고 : 2021.09.28
  • 심사 : 2022.03.02
  • 발행 : 2022.07.25
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초록

Objective: Poor experience with Invisalign treatment affects patient compliance and, thus, treatment outcome. Knowing the potential discomfort level in advance can help orthodontists better prepare the patient to overcome the difficult stage. This study aimed to construct artificial neural networks (ANNs) to predict patient experience in the early stages of Invisalign treatment. Methods: In total, 196 patients were enrolled. Data collection included questionnaires on pain, anxiety, and quality of life (QoL). A four-layer fully connected multilayer perception with three backpropagations was constructed to predict patient experience of the treatment. The input data comprised 17 clinical features. The partial derivative method was used to calculate the relative contributions of each input in the ANNs. Results: The predictive success rates for pain, anxiety, and QoL were 87.7%, 93.4%, and 92.4%, respectively. ANNs for predicting pain, anxiety, and QoL yielded areas under the curve of 0.963, 0.992, and 0.982, respectively. The number of teeth with lingual attachments was the most important factor affecting the outcome of negative experience, followed by the number of lingual buttons and upper incisors with attachments. Conclusions: The constructed ANNs in this preliminary study show good accuracy in predicting patient experience (i.e., pain, anxiety, and QoL) of Invisalign treatment. Artificial intelligence system developed for predicting patient comfort has potential for clinical application to enhance patient compliance.

키워드

과제정보

This work was supported by the National Natural Science Foundation of China (NSFC) (grant number 31971247). The repository of the source code underlying this article is available at https://github.com/lurqTHU/clinic.

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