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
The present study was conducted to assess stress distribution around dental implants based on the all-on-four treatment concept. The finite element analysis (FEA) models comprised cancellous bone covered with cortical bone. Four dental implants were placed in two different designs. In model 1, the four implants were placed parallel to each other, whereas, in model 2, the all-on-four concept was followed. The vertical and lateral loads of various values were applied, and stress was evaluated. In model 2, the least stress was observed in both lateral and vertical loading in the peri-implant region. The all-on-four concept proved to be beneficial in reducing the stress around dental implants, thereby reducing the treatment cost.
