INTRODUCTION: Artificial intelligence (AI) is a relatively new technology that has widespread use in dentistry. The AI technologies have primarily been used in dentistry to diagnose dental diseases, plan treatment, make clinical decisions, and predict the prognosis. AI models like convolutional neural networks (CNN) and artificial neural networks (ANN) have been used in endodontics to study root canal system anatomy, determine working length measurements, detect periapical lesions and root fractures, predict the success of retreatment procedures, and predict the viability of dental pulp stem cells. Methodology. The literature was searched in electronic databases such as Google Scholar, Medline, PubMed, Embase, Web of Science, and Scopus, published over the last four decades (January 1980 to September 15, 2021) by using keywords such as artificial intelligence, machine learning, deep learning, application, endodontics, and dentistry.

RESULTS: The preliminary search yielded 2560 articles relevant enough to the paper's purpose. A total of 88 articles met the eligibility criteria. The majority of research on AI application in endodontics has concentrated on tracing apical foramen, verifying the working length, projection of periapical pathologies, root morphologies, and retreatment predictions and discovering the vertical root fractures.

METHODOLOGY: An electronic search was conducted in PubMed / Medline, Scopus, Google Scholar, and Web of Science databases until January 2023 to retrieve related studies. "Root canal morphology," "Saudi Arabia," "Micro-CT," and "cone-beam computed tomography" were used as keywords. A modified version of previously published risk of bias assessment tool was used to determine the quality assessment of included studies.

RESULTS: The literature search revealed 47 studies that matched the criteria for inclusion, out of which 44 studies used cone beam computed tomography (CBCT) and three were micro-computed tomography (micro-CT) studies. According to the modified version of risk of bias assessment tool, the studies were categorized as low, moderate, and high risk of bias. A total of 47,612 samples were included which comprised of either maxillary teeth (5,412), or mandibular teeth (20,572), and mixed teeth (21,327). 265 samples were used in micro-CT studies while 47,347 teeth samples were used in CBCT studies. Among the CBCT studies, except for three, all the studies were retrospective studies. Frequently used imaging machine and software were 3D Accuitomo 170 and Morita's i-Dixel 3D imaging software respectively. Minimum and maximum voxel sizes were 75 and 300 μm, Vertucci's classification was mostly used to classify the root canal morphology of the teeth. The included micro-CT studies were in-vitro studies where SkyScan 1172 X-ray scanner was the imaging machine with pixel size ranging between 13.4 and 27.4 μm. Vertucci, Ahmed et al. and Pomeranz et al. classifications were applied to classify the root canal morphology.