DATA SOURCE: Medline, Embase, CINAHL PLUS with Full text, Cochrane Library Trials, Web of Science, and Scopus.
REVIEW METHODS: A data search (last update, July 1, 2022) and a manual search were performed (October 5, 2022). Trials involving adults with orofacial pain receiving electrotherapy compared with any other type of treatment were included. The main outcome was pain intensity; secondary outcomes were mouth opening and tenderness. The reporting was based on the new PRISMA Guidelines.
RESULTS: From the electronics databases and manual search 43 studies were included. Although this study was open to including any type of orofacial pain, only studies that investigated temporomandibular disorders were found. The overall quality of the evidence for pain intensity was very low. Although the results should be carefully used, transcutaneous electric nerve stimulation therapy showed to be clinically superior to placebo for reducing pain after treatment (2.63 [-0.48; 5.74]) and at follow-up (0.96 [-0.02; 1.95]) and reduce tenderness after treatment (1.99 [-0.33; 4.32]) and at follow-up (2.43 [-0.24; 5.10]) in subjects with mixed temporomandibular disorders.
CONCLUSION: The results of this systematic review support the use of transcutaneous electric nerve stimulation therapy for patients with mixed temporomandibular disorders to improve pain intensity, and tenderness demonstrating that transcutaneous electric nerve stimulation is superior to placebo. There is inconsistent evidence supporting the superiority of transcutaneous electric nerve stimulation against other therapies.
Method: Scopus, PubMed, and Web of Science (all databases) were searched by 2 reviewers until 29th October 2020. Articles were screened and narratively synthesized according to PRISMA-DTA guidelines based on predefined eligibility criteria. Articles that made direct reference test comparisons to human clinicians were evaluated using the MI-CLAIM checklist. The risk of bias was assessed by JBI-DTA critical appraisal, and certainty of the evidence was evaluated using the GRADE approach. Information regarding the quantification method of dental pain and disease, the conditional characteristics of both training and test data cohort in the machine learning, diagnostic outcomes, and diagnostic test comparisons with clinicians, where applicable, were extracted.
Results: 34 eligible articles were found for data synthesis, of which 8 articles made direct reference comparisons to human clinicians. 7 papers scored over 13 (out of the evaluated 15 points) in the MI-CLAIM approach with all papers scoring 5+ (out of 7) in JBI-DTA appraisals. GRADE approach revealed serious risks of bias and inconsistencies with most studies containing more positive cases than their true prevalence in order to facilitate machine learning. Patient-perceived symptoms and clinical history were generally found to be less reliable than radiographs or histology for training accurate machine learning models. A low agreement level between clinicians training the models was suggested to have a negative impact on the prediction accuracy. Reference comparisons found nonspecialized clinicians with less than 3 years of experience to be disadvantaged against trained models.
Conclusion: Machine learning in dental and orofacial healthcare has shown respectable results in diagnosing diseases with symptomatic pain and with improved future iterations and can be used as a diagnostic aid in the clinics. The current review did not internally analyze the machine learning models and their respective algorithms, nor consider the confounding variables and factors responsible for shaping the orofacial disorders responsible for eliciting pain.