Non-pharmacological pain relief during orthodontic treatment

Nociceptor mechanisms underlying pain and bone remodeling via orthodontic forces: toward no pain, big gain

Orthodontic forces are strongly associated with pain, the primary complaint among patients wearing orthodontic braces. Compared to other side effects of orthodontic treatment, orthodontic pain is often overlooked, with limited clinical management. Orthodontic forces lead to inflammatory responses in the periodontium, which triggers bone remodeling and eventually induces tooth movement. Mechanical forces and subsequent inflammation in the periodontium activate and sensitize periodontal nociceptors and produce orthodontic pain. Nociceptive afferents expressing transient receptor potential vanilloid subtype 1 (TRPV1) play central roles in transducing nociceptive signals, leading to transcriptional changes in the trigeminal ganglia. Nociceptive molecules, such as TRPV1, transient receptor potential ankyrin subtype 1, acid-sensing ion channel 3, and the P2X3 receptor, are believed to mediate orthodontic pain. Neuropeptides such as calcitonin gene-related peptides and substance P can also regulate orthodontic pain. While periodontal nociceptors transmit nociceptive signals to the brain, they are also known to modulate alveolar bone remodeling in periodontitis. Therefore, periodontal nociceptors and nociceptive molecules may contribute to the modulation of orthodontic tooth movement, which currently remains undetermined. Future studies are needed to better understand the fundamental mechanisms underlying neuroskeletal interactions in orthodontics to improve orthodontic treatment by developing novel methods to reduce pain and accelerate orthodontic tooth movement-thereby achieving "big gains with no pain" in clinical orthodontics.

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

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.