Cone-beam computed tomography (CBCT) has become a cornerstone of dental imaging, delivering high-resolution and the three-dimensional views that are essential for preoperative planning and postoperative evaluation of dental implants. Despite its advantages, CBCT is often hampered by artifacts – particularly in the presence of metal implants – which cause image deterioration, complicate diagnosis and hinder surgical planning. Artifacts introduce significant challenges to accurate assessment of peri-implant structures and surrounding tissues. Current solutions, including adjustments to X-ray exposure parameters as well as hardware- and software-based corrections, provide partial advancements yet remain insufficient in completely eliminating artifacts in numerous instances. The introduction of artificial intelligence (AI) offers a transformative approach by leveraging deep learning algorithms to identify, correct, and even predict artifact patterns. While AI holds potential for improved image quality, diagnostic accuracy, and operational efficiency, challenges remain. For AI to be safely and effectively integrated into dental imaging, it is important to ensure that the model works well with different CBCT units, implant types, and patient anatomies, and that it is trained on a diverse set of data. Addressing ethical considerations is equally crucial to maintain patient safety and trust. Future research should focus on hybrid methods that combine AI with traditional artifact correction techniques and expanding AI capabilities to reduce artifacts across broader imaging modalities. These advancements will further enhance the precision and comprehensiveness of dental implantology.
Cone-beam computed tomography (CBCT) is an advanced imaging modality that plays a critical role in dental implantology, producing high-resolution, three-dimensional images that enable accurate preoperative planning and postoperative assessment. By providing detailed data, CBCT helps minimize surgical risks, improve patient outcomes, and support the long-term success of dental implants (Bornstein et al. 2017; Fuglsig et al. 2024; Jacobs et al. 2018). However, one of the major challenges associated with CBCT imaging is the presence of artifacts, particularly those caused by metal objects such as dental implants. Artifacts, in this context, refer to visualized structures that appear in the final image but do not actually exist in the object being examined. In simple terms, they represent image deterioration. These artifacts generally arise when the real condition of the scan, like the position or material of the object being scanned and the scanner setup, do not match the mathematical models used to create the 3D image (Schulze et al. 2011). Metal-related artifacts can manifest in various ways, potentially obscuring critical details of the image, complicating diagnostic accuracy. Therefore, addressing these artifacts is essential for gaining a better understanding of the reliability of CBCT scans in dental implantology. To facilitate a deeper understanding of the terminology employed in this manuscript and its clinical relevance, Table 1 provides a comprehensive summary of key terms, their definitions, and their significance in oral implantology.