CBCT Convergence With Artificial Intelligence: Evaluating Novel Technologies In Endodontic Therapy

Aim: The use of Artificial Intelligence (AI) models is on the rise for the analysis of Cone Beam Computed Tomography (CBCT) images. This extensive review seeks to evaluate the diagnostic and prognostic accuracy of AI applications in CBCT imaging within endodontic practices, emphasizing its potential to improve clinical outcomes.Methodology: A thorough literature review was performed utilizing major electronic databases such as Google Scholar, PubMed, Medline, and Scopus. The search covered publications from ۱۹۹۹ to the present and employed specific keywords, including “AI,” “machine learning,” “deep learning,” “endodontics,” “CBCT,” “advanced analytics,” “computational linguistics,” “automation,” “intelligent agents,” “probabilistic reasoning,” “CNN,” and “ANN.”Results: The initial search yielded ۲۵۴۲ articles, with ۳۴ meeting the strict inclusion criteria for this review. The analysis highlighted a significant focus on critical areas within endodontics, such as verifying working lengths, detecting and projecting periapical pathologies, assessing root canal morphologies, and predicting root fractures. Recent research underscores the increasing emphasis on enhancing the accuracy and efficiency of endodontic diagnostics through AI-integrated CBCT applications.Conclusion: This review's findings highlight the essential role of AI-enhanced CBCT imaging in improving diagnostic accuracy and prognostic assessments in endodontics. The integration of AI with CBCT signifies a transformative advancement in endodontic care, enabling more efficient workflows and standardized diagnostic practices while minimizing human error

کلیدواژه ها:

CBCT ، Convolutional neural networks ، Deep learning models ، Endodontic prognosis ، Machine learning