Since its wider commercial introduction in the 2000s, cone-beam computed tomography (CBCT) has become an invaluable tool in dental imaging, offering high-resolution, three-dimensional representations of the oral cavity. However, the presence of metallic dental objects, such as amalgam fillings, crowns, orthodontic appliances, root fillings and implants, often leads to significant image artifacts of varying appearance and extent, complicating accurate diagnosis and treatment planning.  

CBCT artifacts are produced by discrepancies between mathematical models and actual imaging processes. Beam hardening is the most significant metal-induced artifact of CBCT and is one of the main factors responsible for diminished image quality of the entire CBCT examination. Closer to dental foreign materials, beam hardening and noise induce excessive gray value variation, obscuring critical anatomical structures and compromising diagnostic accuracy. Image noise manifests as a disturbance in a signal, and reduced resolution at low contrast and can significantly impair the quality of CBCT images. Moreover, noise and scatter are important factors influencing the creation of new artifacts.

Recently, artificial intelligence (AI) has attracted significant interest in the field of dentistry, particularly in orthodontics and dentomaxillofacial imaging. Recent years have led to the development of deep learning-based image reconstruction algorithms (DLRs) that can effectively reduce excess image noise. Initially, three CT vendors and two independent (vendor-agnostic) software companies released deep learning (DL) algorithms approved by the U.S. Food and Drug Administration: the first one, TrueFidelity (GE Healthcare, Wauwatosa, WI, USA), in 2019; the second, AiCE (Canon Medical Systems, Tustin, CA, USA); the latter, Precise Image (Philips Healthcare, Amsterdam, The Netherlands); and vendor-agnostic software companies, ClariCT.AI (ClariPi) and PixelShine (AlgoMedica, Heidelberg Germany).

One of the available vendor-neutral deep learning models (DLMs) is ClariCT (ClariPi, Seoul, South Korea), which operates in the image postprocessing stage. According to the vendor, the algorithm was trained on a dataset of over a million CT images from various CT systems and reconstruction settings. ClariCT.AI is based on a modified U-net-type convolutional neural network (CNN) model and has already been proven to reduce image noise and provide high diagnostic accuracy. Hypothetically, this AI denoising tool could also positively impact the quality parameters of CBCT images by reducing additional noise associated with metal artifacts. To the best of our knowledge, no studies have yet analyzed the application of DLM in dental CBCT, along with an objective and subjective assessment of image quality parameters of metal artifact reduction.  

The aim of this study was to assess the impact of AI-driven noise reduction algorithms on metal artifacts and image quality parameters in CBCT images of the oral cavity.