Background & Aims

Studies available in literature on the use of AI for the diagnosis of OFP disorders have used different patient selection criteria, disease classifications, input data, and outcome measures for evaluation. Consequently, the performance of the various AI models vary across different studies. To the best of our knowledge, there has been no systematic review till date that summarizes such findings. Therefore, this study aimed to systematically review the current literature on the use of sematic-based algorithms in AI for the diagnosis of various OFP disorders, evaluate the quality of these studies and assess the performance of existing AI models.

Methods

The systematic review and meta-analysis was conducted and reported according to the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) 2020 guidelines. PubMed, Embase, Web of Science and Science Direct databases were searched for relevant articles from database inception to January 2024. Studies that used semantic-based AI algorithms to diagnose at least one type of chronic OFP disorder and those that assessed the performance of these algorithms were included. Studies on OFP disorders associated with odontogenic causes of pain, as well as studies on radiomic-based AI algorithms, abstracts without full texts, case reports, case series, book chapters and review papers were excluded. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) was used to evaluate the quality of studies for which meta-analysis was performed.

Results

11 eligible articles that used semantic-based AI algorithms for the diagnosis of OFP disorders were included; they were retrospective studies and observational studies. The types of chronic OFP disorders were classified according to the International Classification of Orofacial pain (ICOP).

6 studies were subjected to a meta-analysis for diagnostic performance. According to GRADE, the certainty of evidence was very low. The accuracy of the AI models for the diagnosis of myofascial orofacial pain was 0.91-0.97, and the pooled accuracy was 0.94 (95% CI 0.92-0.96), I2=0%. For temporomandibular joint pain, the diagnostic accuracy of the AI models was 0.71-0.99 (95% CI 0.87-0.98), I2=88.75%. The performance of the AI models for the diagnosis of migraines was 0.86-1.00, and the pooled accuracy was 0.97 (95% CI 0.75-1.00), I2=92%.

Conclusions

Numerous AI models have been developed for diagnosis of chronic OFP disorders and may serve as an additional tool to aid in increasing diagnostic accuracy. The results of this study suggest that semantic-based AI algorithms developed for the diagnosis of chronic OFP disorders can be used as a decision support tool, especially in the outpatient clinic setting. However, a high risk of bias in patient selection was noted due to the inclusion of observational studies. In addition, a significant heterogeneity was observed among the studies included for meta-analysis of diagnostic accuracy. Therefore, the certainty of evidence was deemed as very low. Future research of higher quality is strongly recommended.

References

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Presenting Author

Eunice Lua

Poster Authors

Eunice Lua

OTHR

National University of Singapore

Lead Author

Bernadette Quah; BDS

MDS

National University of Singapore

Lead Author

Hee Hon Tan; BDS

MDS

National University of Singapore

Lead Author

Christopher C. Peck

National University of Singapore

Lead Author

Topics

  • Assessment and Diagnosis