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  • Artificial Intelligence applied to implant surgical planning: a systematic review
    Publication . Molcard, Arthur Charles Félix; Durão, Ana Paula Reis
    Prior assessment of bone quality is a critical factor in implant dentistry, influencing implant stability, osseointegration, and long-term success. Conventional approaches, including conebeam computed tomography (CBCT) imaging and tactile perception, are often qualitative and variable. Artificial intelligence (AI) can help with the development of automated, objective, and reproducible analyses. The present systematic review aims to present the current evidence on AI-driven bone quality assessment (BQA) and its impact on implant planning and clinical outcomes. The search strategy followed the PRISMA guidelines and included several electronic databases: PubMed, IEEE Xplore, Web of Science, and ScienceDirect. Included studies were peer reviewed and published between 2019 and February 2025. The primary objectives were to determine the efficacy and consistency of AI in the assessment of bone quality, compare it with traditional methods, and analyse its influence on clinical decision-making and implant success. The Joanna Briggs Institute (JBI) Critical Appraisal Checklists were used to evaluate the risk of bias (RoB). As a result, thirteen studies met the inclusion criteria. AI models, particularly deep learning (DL) approaches, showed strong performance in estimating bone quality parameters, such as bone mineral density (BMD), cortical thickness and trabecular pattern. AI-based approaches led to more accurate and less biased bone assessments than conventional methods. Some studies indicated that using AI tools may contribute in better implant site identification (based on quality) and potentially improve clinical results. The application of AI techniques for BQA is a promising approach for enhancing the diagnostic accuracy and clinical decision-making in dental implantology. AI can increase the standardisation of bone assessments and thus improve the predictability of implant treatments outcomes. More research is required to confirm these findings in broader clinical settings and to investigate the implementation of such AI technologies.
    2025-07-22Master thesis Embargoed access Show more