Smart Implants: AI-Driven Predictive Monitoring in Implant Dentistry

Background and Aims: The integration of AI-driven smart implants into implant dentistry offers innovative solutions for real-time monitoring, early detection of complications, and predictive maintenance. These advancements aim to improve the longevity of dental implants and reduce the costs associated with failures. This study systematically reviews the efficacy of smart implants equipped with sensors (e.g., for strain, temperature, or pH) and AI models (e.g., machine learning, deep learning, or predictive analytics) in enhancing treatment outcomes. Methods: A comprehensive systematic review was conducted, analyzing peer-reviewed studies published from ۲۰۱۷ to ۲۰۲۵. Data were collected from PubMed, Web of Science, and Engineering Village, focusing on studies that investigated AI-integrated smart implants. The inclusion criteria covered research involving sensors and AI models for dental implant monitoring. Key metrics analyzed included the detection accuracy of complications (e.g., peri-implantitis, mechanical loosening, bone loss), predictive maintenance capabilities, implant survival rates, and patient-specific risk profiling. The performance of AI models (accuracy, sensitivity, specificity), sensor reliability, and clinical integration were synthesized and compared against conventional monitoring methods. Results: AI-driven smart implants significantly outperformed traditional methods in monitoring and predicting implant health. Sensor-equipped implants, when combined with machine learning models, demonstrated detection accuracies ranging from ۹۰% to ۹۷% for early-stage complications like peri-implantitis or screw loosening, in contrast to ۶۵%–۸۰% achieved through clinician-based assessments. Predictive maintenance algorithms identified implant failure risks ۶–۱۲ months in advance, with sensitivities and specificities exceeding ۹۲%. Real-time monitoring of biomechanical (e.g., occlusal load) and biological (e.g., pH variations indicating inflammation) parameters enabled personalized interventions, reducing failure rates by ۲۵%–۴۰% in high-risk cases. Furthermore, AI-assisted risk stratification improved treatment planning, while maintenance costs decreased by ۱۵%–۳۰% due to timely interventions. Implant survival rates improved by ۱۰%–۲۰% over ۵-year follow-ups. Challenges to adoption included sensor durability, data privacy concerns, and the lack of standardized clinical protocols. Conclusion: AI-driven smart implants represent a transformative innovation in implant dentistry. These systems excel in real-time monitoring, early detection of complications, and predictive maintenance, leading to improved implant survival rates and accuracy, personalized care, and cost efficiency.

کلیدواژه ها:

AI ، Smart Implants ، Machine Learning ، Peri-Implantitis