Furcation Defects

Bond strength of furcation repair materials is an essential factor in clinical success. Studies on the effect of adding titanium dioxide (TiO₂) nanoparticles on the push-out bond strength of commonly used endodontic cements for furcation perforation repair is limited.

This study aimed to evaluate the effect of adding TiO₂ nanoparticles to white Portland cement (PC), white mineral trioxide aggregate (MTA), and calcium enriched mixture cement (CEM) on their push-out bond strengths.

In this in vitro study, 120 endodontically treated molars were assigned to six groups (n=20) based on the material used to repair the perforation. In three groups, the cements (white PC, white MTA, and CEM) were placed in pure form, and in the three remaining groups, 1 weight % of TiO₂ was added. The push-out bond strength was measured using a universal testing machine at a strain rate of 0.5 mm/min. Data were analyzed using one-way ANOVA and post hoc Games-Howell test (p< 0.05).

One-way ANOVA showed significant differences in the mean bond strength values between the six groups (p= 0.002). The post hoc Games-Howell test showed that the bond strengths in MTA+TiO₂ and PC+TiO₂ groups were significantly higher than those in MTA and PC groups, respectively. However, there was no significant difference in the bond strength between CEM and CEM+ TiO₂ groups.

The incorporation of TiO₂ into MTA and PC increased their push-out bond strength. However, it did not affect the push-out bond strength of CEM cement.

To evaluate the clinical outcomes using the PRECICE magnetic limb lengthening intramedullary nail for the correction of lower limb length discrepancies (LLD) in adults with posttraumatic nonunion or malunion defects in a Latin American center.

A retrospective review of 25 adult patients with LLD associated with posttraumatic nonunion or malunion defects of femur or tibia treated with the PRECICE nail between January 2018 and December 2020. The primary outcomes considered were lengthening length achieved in mm, incidence of complications and quality of life (EQ-5D-3 L questionnaire).

Twenty-five cases (20 femoral and 5 tibial nails) were performed, with a median follow-up of 27 months (Interquartile range-IQR: 17.5 to 34.5). The average age was 36.5 ± 12.9 years; 10 cases were women. Fifteen cases had an LLD secondary to a malunion defect and 10 cases had an LLD secondary to a nonunion. PRECICE nails were inserted for the treatment of a median LLD of 40.0 mm (IQR: 30.2 to 74.2) in the femur and 30.0 mm (28.5 to 50.0) in the tibia. An accuracy of 100% was reported in 18 cases (Femur: 14 and tibia: 4) and consolidation was achieved in 22/25 cases with the PRECICE nail in situ. Complications were recorded in 9 (36%) cases (6/20 femur, 3/5 tibia), mainly related to the consolidation process (5/9). The median EQ-5D and EQ-VAS were 0.79 (IQR: 0.63 to 0.79) and 80.0 (IQR: 50.0 to 90.0), respectively.

The results of this study demonstrated that the PRECICE nail is an effective device for the management of posttraumatic LLD during the treatment of nonunion or malunion bone defects of femur and tibia, offering a reasonable quality of life, despite its postoperative complication risk.

Furcation defects pose a significant challenge in the diagnosis and treatment planning of periodontal diseases. The accurate detection of furcation involvements (FI) on periapical radiographs (PAs) is crucial for the success of periodontal therapy. This research proposes a deep learning-based approach to furcation defect detection using convolutional neural networks (CNN) with an accuracy rate of 95%. This research has undergone a rigorous review by the Institutional Review Board (IRB) and has received accreditation under number 202002030B0C505. A dataset of 300 periapical radiographs of teeth with and without FI were collected and preprocessed to enhance the quality of the images. The efficient and innovative image masking technique used in this research better enhances the contrast between FI symptoms and other areas. Moreover, this technology highlights the region of interest (ROI) for the subsequent CNN models training with a combination of transfer learning and fine-tuning techniques. The proposed segmentation algorithm demonstrates exceptional performance with an overall accuracy up to 94.97%, surpassing other conventional methods. Moreover, in comparison with existing CNN technology for identifying dental problems, this research proposes an improved adaptive threshold preprocessing technique that produces clearer distinctions between teeth and interdental molars. The proposed model achieves impressive results in detecting FI with identification rates ranging from 92.96% to a remarkable 94.97%. These findings suggest that our deep learning approach holds significant potential for improving the accuracy and efficiency of dental diagnosis. Such AI-assisted dental diagnosis has the potential to improve periodontal diagnosis, treatment planning, and patient outcomes. This research demonstrates the feasibility and effectiveness of using deep learning algorithms for furcation defect detection on periapical radiographs and highlights the potential for AI-assisted dental diagnosis. With the improvement of dental abnormality detection, earlier intervention could be enabled and could ultimately lead to improved patient outcomes.