Predicting patient experience of Invisalign treatment: An analysis using artificial neural network

Abstract

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.

Keywords: Aligners; Compliance; Computer algorithm; Pain.

Figure 1

Flow diagram of the construction of artificial neural networks. The three artificial neural networks are fully connected and includes two hidden layers with a hidden size of nine.

Figure 2

Prediction performance of the artificial neural networks (ANNs). The learning curves of ANNs for pain (A), anxiety (B), and quality of life (C). Red lines represent train loss curve; purple lines, validation loss curve. Arrows indicate the lowest point of validation loss curve, which means the training procedure for pain, anxiety, and quality of life are stopped at 25, 24, and 22 epochs, respectively. The ROC curves of ANNs for pain (D), anxiety (E), and quality of life (F). The optimum diagnostic cutoff value is marked as purple points, where the sensitivity and specificity are shown upon the arrows. ROC, receiver operating characteristic; AUC, area under the curve.

Figure 3

Total contribution of the 17 input features in descending order.