Artifacts in Dental Implant Cone-Beam Computed Tomography Scans and how Artificial Intelligence Could Help

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.