Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review

Abstract

Purpose. The study explored the clinical influence, effectiveness, limitations, and human comparison outcomes of machine learning in diagnosing (1) dental diseases, (2) periodontal diseases, (3) trauma and neuralgias, (4) cysts and tumors, (5) glandular disorders, and (6) bone and temporomandibular joint as possible causes of dental and orofacial pain. 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.

Trends in Scope of Practice for Oral Health Care: Future Transformative Effects

“…Several applications of artificial intelligence and machine learning in dentistry have already been reported in the literature: radiographic interpretation (Chen et al 2021), diagnosis (Alabi et al 2021; Farook et al 2021), oral and maxillofacial surgery (Ayoub and Pulijala 2019; Chen et al 2021; Liu et al 2021), and cephalometric image analyses (Silva et al 2021; Woodsend et al 2022). These technological accomplishments target repetitive procedures in dentistry.…”

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

“…There were 14 systematic reviews published in the last two years focused on AI in dentistry [ 119 , 198 , 199 , 200 , 201 ]. Only three of them were focused on dentistry with a general scope.…”

“…The second systematic review, by Ahmed et al [ 119 ], analyzes 38 articles and comes to similar conclusions about the current utilization advantages and the future potential of AI in dentistry. The third systematic review, from Farook et al [ 201 ], evaluates machine learning with a broad dental and orofacial healthcare scope with regard to diagnosing diseases with symptomatic pain.…”

Artificial intelligence to support early diagnosis of temporomandibular disorders: A preliminary case study

“…In our big data era, artificial intelligence (AI) and specifically machine learning (ML) techniques, used to extract, combine and understand hidden information, 8 are regarded as ways to improve the diagnostic process of TMDs 9 . In fact, in the last 2 years, ML‐AI techniques, which are extensively used in healthcare, faced an increasing adoption also in dentistry, 9 and more specifically in TMD diagnosis 10–14 …”