Effectiveness of Artificial Intelligence Applications Designed for Endodontic Diagnosis, Decision-making, and Prediction of Prognosis: A Systematic Review.

AIM

With advancements in science and technology, there has been phenomenal developments in the application of neural networks in dentistry. This systematic review aimed to report on the effectiveness of artificial intelligence (AI) applications designed for endodontic diagnosis, decision-making, and prediction of prognosis.

MATERIALS AND METHODS

Studies reporting on AI applications in endodontics were identified from the electronic databases such as PubMed, Medline, Embase, Cochrane, Google Scholar, Scopus, and Web of Science, for original research articles published from January 1, 2000, to June 1, 2020. A total of 10 studies that met our eligibility criteria were further analyzed for qualitative data. QUADAS-2 was applied for synthesis of the quality of the studies included.

RESULTS

A wide range of AI applications have been implemented in endodontics. The neural networks employed were mostly based on convolutional neural networks (CNNs) and artificial neural networks (ANNs) in their neural architectures. These AI models have been used for locating apical foramen, retreatment predictions, prediction of periapical pathologies, detection and diagnosis of vertical root fractures, and assessment of root morphologies.

CONCLUSION

These studies suggest that the neural networks performed similar to the experienced professionals in terms of accuracy and precision. In some studies, these models have even outperformed the specialists.

CLINICAL SIGNIFICANCE

These models can be of greater assistance as an expert opinion for less experienced and nonspecialists.