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.
METHODS: This was a cross-sectional study conducted over a period of 18 months. A self-administered questionnaire assessing knowledge and perception regarding neonatal pain was used.
RESULTS: Twenty-four hospitals participated in the study, with 423 respondents. The response rate was 85%. One hundred and ninety-seven respondents (47%) were aware of tools for neonatal pain assessment, but only 6% used them in daily practice. Doctors with >4 years of experience in neonatal care had better awareness of available pain assessment tools (59.4% vs 40.9%, P = 0.001). Sixteen statements regarding knowledge were assessed. Mean score obtained was 10.5 ± 2.5. Consultants/specialists obtained a higher mean score than medical officers (11.9 vs 10.4, P < 0.001). More than 80% of respondents were able to discriminate painful from non-painful procedures.
CONCLUSION: Clinicians involved in neonatal care, especially those with longer experience were knowledgeable about neonatal pain. Gaps between knowledge and its application, however, remain. Implementation of clinical guidelines to improve the quality of assessment and adequate pain management in neonates is recommended.