RESUMO
Background: Dental implant surgery has become a widely accepted method for replacing missing teeth. However, the success of dental implant procedures can be influenced by various factors, including the quality of preoperative planning and assessment. Cone beam computed tomography (CBCT) imaging provides valuable insights into a patient's oral anatomy, but accurately predicting implant success remains a challenge. Materials and Methods: In this randomized controlled trial (RCT), a cohort of 150 patients requiring dental implants was randomly divided into two groups: an artificial intelligence (AI)-assisted group and a traditional assessment group. Preoperative CBCT images of all patients were acquired and processed. The AI-assisted group utilized a machine learning model trained on historical data to assess implant success probability based on CBCT images, while the traditional assessment group relied on conventional methods and clinician expertise. Key parameters such as bone density, bone quality, and anatomical features were considered in the AI model. Results: After the completion of the study, the AI-assisted group demonstrated a significantly higher implant success rate, with 92% of implants successfully integrating into the bone compared to 78% in the traditional assessment group. The AI model showed an accuracy of 87% in predicting implant success, whereas traditional assessment methods achieved an accuracy of 71%. Additionally, the AI-assisted group had a lower rate of complications and required fewer postoperative interventions compared to the traditional assessment group. Conclusion: The AI-assisted approach significantly improved implant success rates and reduced complications, underscoring the importance of incorporating AI into the dental implant planning process.
RESUMO
Background: Dental implant placement is a critical procedure in modern dentistry, requiring precise treatment planning to ensure successful outcomes. Traditionally, treatment planning has relied on the expertise of clinicians, but recent advancements in artificial intelligence (AI) have opened up the possibility of AI-assisted treatment planning. Materials and Methods: Twenty patients requiring dental implant placement were included in this comparative study. For each patient, a clinical treatment plan was created by an experienced dentist, while an AI algorithm, trained on a dataset of implant placement cases, generated an alternative plan. Various parameters, including implant position, angulation, and depth, were compared between the two plans. Surgical templates were fabricated based on both plans to guide implant placement accurately. Results: The results of this study indicate that AI-generated treatment plans closely align with clinical plans in terms of implant positioning, angulation, and depth. Mean discrepancies of less than 1 mm and 2 degrees were observed for implant position and angulation, respectively, between the two planning methods. The AI-generated plans also showed a reduction in planning time, averaging 10 min compared to the clinical planning, which averaged 30 min per case. Additionally, the surgical templates based on AI-generated plans exhibited similar accuracy in implant placement as those based on clinical plans. Conclusion: AI-assisted treatment planning for dental implant placement demonstrates promising results in terms of accuracy and efficiency.
RESUMO
Two-implant overdentures have become the accepted treatment for restoring mandibular edentulism. The dimensions of regular implants sometimes limit their use, such as in the case of narrow ridges. Mini-implants with reduced diameters (less than 3.0 mm) enable insertion into narrow ridges. A magnet-retained two-mini-implant overdenture system was developed and is described in this paper. Additionally, we describe a clinical mandibular procedure using the system and evaluate its biomechanical performance.
