Critical Analysis of Artificial Intelligence in Endodontics: A Scoping Review

Introduction: Artificial intelligence (AI) comprises computational models that mimic the human brain to perform various diagnostic tasks in clinical practice. The aim of this scoping review was to systematically analyze the AI algorithms and models used in endodontics and identify the source quality and type of evidence.

Methods: A literature search was conducted in October 2020 to identify the relevant literature in English language in the 4 major health sciences databases, ie, MEDLINE, Dentistry & Oral Science, CINAHL Plus, and Cochrane Library. Our review questions were the following: what are the different AI algorithms and models used in endodontics?, what are the datasets being used?, what type of performance metrics were reported?, and what diagnostic performance measures were used?. The quality of the included studies was evaluated by a modified Quality Assessment of Studies of Diagnostic Accuracy risk (QUADAS) tool.

Results: Out of 300 studies, 12 articles met our inclusion criteria and were subjected to final analysis. Among the included studies, 6 studies focused on periapical pathology, and 3 studies investigated vertical root fractures. Most studies (n = 10) used neural networks, among which convolutional neural networks were commonly used. The datasets that were mostly studied were radiographs. Out of 12 studies, only 3 studies achieved a high score according to the modified QUADAS tool.

Conclusions: AI models had acceptable performance, ie, accuracy >90% in executing various diagnostic tasks. The scientific reporting of AI-related research is irregular. The endodontic community needs to implement recommended guidelines to improve the weaknesses in the current planning and reporting of AI-related research to improve its scientific vigor.