Dental versus zygomatic implants in the treatment of maxillectomy: a finite element analysis.

PURPOSE: This study analyzed the stress distributions on zygomatic and dental implants placed in the zygomatic bone, supporting bones, and superstructures under occlusal loads after maxillary reconstruction with obturator prostheses. MATERIALS AND METHODS: 12 scenarios of three-dimensional finite element models were constructed based on computed tomography scans of a patient who had hemimaxillectomy. Two obturator prostheses were analyzed for each model. A total force of 600 N was applied from the palatal to buccal bones at an angle of 45°. The maximum and minimum principal stress values for bone and also the von Misses stress values for dental implants and prostheses were calculated. RESULTS: When zygomatic implants were applied to the defect area, the maximum principal stresses were similar in intensity to the other models; however, the minimum principal stress values were higher than in scenarios without zygomatic implants. In models that used zygomatic implants in the defect area, von Misses stress levels were significantly higher in zygomatic implants than in dental implants. In scenarios where the prosthesis was supported by tissue in the non-defect area, the maximum and minimum principal stress values on cortical bone were higher than in scenarios where implants were applied to both defect and non-defect areas. CONCLUSIONS: In patients who lack an alveolar crest after maxillectomy, reduced stress on the zygomatic bone is expected if a custom bar-retained prosthesis is placed on the dental implant. The stress was higher on zygomatic implants without alveolar crest support than on dental implants.

Finite Element Analysis of the Stress Distribution Associated With Different Implant Designs for Different Bone Densities.

PURPOSE: The main objective of this study was to investigate the influence of implant design, bone type, and abutment angulation on stress distribution around dental implants. MATERIALS AND METHODS: Two implant designs with different thread designs, but with the same length and brand were used. The three-dimensional geometry of the bone was simulated with four different bone types, for two different abutment angulations. A 30° oblique load of 200 N was applied to the implant abutments. Maximum principal stress and minimum principal stresses were obtained for bone and Von misses stresses were obtained for dental implants. RESULTS: The distribution of the load was concentrated at the coronal portion of the bone and implants. The stress distributions to the D4 type bone were higher for implant models. Increased bone density and increased cortical bone thickness cause less stress on bone and implants. All implants showed a good distribution of forces for non-axial loads, with higher stresses concentrated at the crestal region of the bone-implant interface. In implant types using straight abutments there was a decrease in stress as the bone density decreased. The change in the abutment angle also caused an increase in stress. CONCLUSIONS: The use of different implant threads and angled abutments affects the stress on the surrounding bone and implant. In addition, it was observed that a decrease in density in trabecular bone and a decrease in cortical bone thickness increased stress.

Dental Versus Zygomatic Implants in the Treatment of Maxillectomy: A Finite Element Analysis.

This study analyzed the stress distributions on zygomatic and dental implants placed in the zygomatic bone, supporting bones, and superstructures under occlusal loads after maxillary reconstruction with obturator prostheses. A total of 12 scenarios of 3-dimensional finite element models were constructed based on computerized tomography scans of a hemimaxillectomy patient. Two obturator prostheses were analyzed for each model. A total force of 600 N was applied from the palatal to buccal bones at an angle of 45°. The maximum and minimum principal stress values for bone and von Mises stress values for dental implants and prostheses were calculated. When zygomatic implants were applied to the defect area, the maximum principal stresses were similar in intensity to the other models; however, the minimum principal stress values were higher than in scenarios without zygomatic implants. In models that used zygomatic implants in the defect area, von Mises stress levels were significantly higher in zygomatic implants than in dental implants. In scenarios where the prosthesis was supported by tissue in the nondefect area, the maximum and minimum principal stress values on cortical bone were higher than in scenarios where implants were applied to defect and nondefect areas. In patients who lack an alveolar crest after maxillectomy, a custom bar-retained prosthesis placed on the dental implant should reduce stress on the zygomatic bone. The stress was higher on zygomatic implants without alveolar crest support than on dental implants.

A robust deep learning model for the classification of dental implant brands.

OBJECTIVE: In cases where the brands of implants are not known, treatment options can be significantly limited in potential complications arising from implant procedures. This research aims to explore the application of deep learning techniques for the classification of dental implant systems using panoramic radiographs. The primary objective is to assess the superiority of the proposed model in achieving accurate and efficient dental implant classification. MATERIAL AND METHODS: A comprehensive analysis was conducted using a diverse set of 25 convolutional neural network (CNN) models, including popular architectures such as VGG16, ResNet-50, EfficientNet, and ConvNeXt. The dataset of 1258 panoramic radiographs from patients who underwent implant treatment at faculty of dentistry was utilized for training and evaluation. Six different dental implant systems were employed as prototypes for the classification task. The precision, recall, F1 score, and support scores for each class have included in the classification accuracy report to ensure accurate and reliable results from the model. RESULTS: The experimental results demonstrate that the proposed model consistently outperformed the other evaluated CNN architectures in terms of accuracy, precision, recall, and F1-score. With an impressive accuracy of 95.74 % and high precision and recall rates, the ConvNeXt model showcased its superiority in accurately classifying dental implant systems. Notably, the model's performance was achieved with a relatively smaller number of parameters, indicating its efficiency and speed during inference. CONCLUSION: The findings highlight the effectiveness of deep learning techniques, particularly the proposed model, in accurately classifying dental implant systems from panoramic radiographs.