ARTIFICIAL INTELLIGENCE MODELS SHOW POTENTIAL IN RECOGNIZING THE DENTAL IMPLANT TYPE, PREDICTING IMPLANT SUCCESS, AND OPTIMIZING IMPLANT DESIGN

Deep learning-based approach for 3D bone segmentation and prediction of missing tooth region for dental implant planning

Recent studies have shown that dental implants have high long-term survival rates, indicating their effectiveness compared to other treatments. However, there is still a concern regarding treatment failure. Deep learning methods, specifically U-Net models, have been effectively applied to analyze medical and dental images. This study aims to utilize U-Net models to segment bone in regions where teeth are missing in cone-beam computerized tomography (CBCT) scans and predict the positions of implants. The proposed models were applied to a CBCT dataset of Taibah University Dental Hospital (TUDH) patients between 2018 and 2023. They were evaluated using different performance metrics and validated by a domain expert. The experimental results demonstrated outstanding performance in terms of dice, precision, and recall for bone segmentation (0.93, 0.94, and 0.93, respectively) with a low volume error (0.01). The proposed models offer promising automated dental implant planning for dental implantologists.

Dental implant planning using artificial intelligence: A systematic review and meta-analysis

STATEMENT OF PROBLEM

Data on the role of artificial intelligence (AI) in dental implant planning is insufficient.

PURPOSE

The purpose of this systematic review with meta-analysis was to analyze and evaluate articles that assess the effectiveness of AI algorithms in dental implant planning, specifically in detecting edentulous areas and evaluating bone dimensions.

MATERIAL AND METHODS

A systematic review was conducted across the MEDLINE/PubMed, Web of Science, Cochrane, and Scopus databases. In addition, a manual search was performed. The inclusion criteria consisted of peer-reviewed studies that examined the accuracy of AI-based diagnostic tools on dental radiographs for dental implant planning. The most recent search was conducted in January 2024. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess the quality of the included articles.

RESULTS

Twelve articles met the inclusion criteria for this review and focused on the application of AI in dental implant planning using cone beam computed tomography (CBCT) images. The pooled data indicated an overall accuracy of 96% (95% CI=94% to 98%) for the mandible and 83% (95% CI=82% to 84%) for the maxilla in identifying edentulous areas for implant planning. Eight studies had a low risk of bias, 2 studies had some concern of bias, and 2 studies had a high risk of bias.

CONCLUSIONS

AI models have the potential to identify edentulous areas and provide measurements of bone as part of dental implant planning using CBCT images. However, additional well-conducted research is needed to enhance the accuracy, generalizability, and applicability of AI-based approaches.

Advancements in artificial intelligence algorithms for dental implant identification: A systematic review with meta-analysis

STATEMENT OF PROBLEM

The evidence regarding the application of artificial intelligence (AI) in identifying dental implant systems is currently inconclusive. The available studies present varying results and methodologies, making it difficult to draw definitive conclusions.

PURPOSE

The purpose of this systematic review with meta-analysis was to comprehensively analyze and evaluate articles that investigate the application of AI in identifying and classifying dental implant systems.

MATERIAL AND METHODS

An electronic systematic review was conducted across 3 databases: MEDLINE/PubMed, Cochrane, and Scopus. Additionally, a manual search was performed. The inclusion criteria consisted of peer-reviewed studies investigating the accuracy of AI-based diagnostic tools on dental radiographs for identifying and classifying dental implant systems and comparing the results with those obtained by expert judges using manual techniques-the search strategy encompassed articles published until September 2023. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess the quality of included articles.

RESULTS

Twenty-two eligible articles were included in this review. These articles described the use of AI in detecting dental implants through conventional radiographs. The pooled data showed that dental implant identification had an overall accuracy of 92.56% (range 90.49% to 94.63%). Eleven studies showed a low risk of bias, 6 demonstrated some concern risk, and 5 showed a high risk of bias.

CONCLUSIONS

AI models using panoramic and periapical radiographs can accurately identify and categorize dental implant systems. However, additional well-conducted research is recommended to identify the most common implant systems.

Bioengineering Applied to Oral Implantology, a New Protocol: “Digital Guided Surgery”

Rehabilitative dentistry has made enormous progress in recent years, not only due to the advent of new implant-prosthetic methods, but also thanks to new information technologies that support the doctor. This study aims to present a new implant protocol that involves the application of bioengineering methods. With the application of the finite element analysis, it is possible to evaluate the distribution of the forces of a fixture and possible implant rehabilitation on each patient, even before performing the surgery. This protocol provides for the combination of radiographic images and three-dimensional files to obtain predictable results on possible rehabilitation, guiding its planning in the best possible way. Surely, the evolution of machines and computers will enable the surgeon to carry out and maintain these protocols in a chair-side manner, and to carry out safe and predictable rehabilitations.

Digital technologies in dentistry in Saudi Arabia: Perceptions, practices and challenges

Background

The aim of this study was to assess practicing dentists' characteristics and professional aspects that influence users to adopt Digital Technologies in Dentistry (DTD) in Saudi Arabia (SA). Moreover, we aimed to identify dentists' perceived barriers and challenges and to anticipate future potential developments towards implementing DTDs in their practices in SA.

Methods

This analytical cross-sectional study based on a validated questionnaire was conducted using a snowball sampling technique to include a conveniently selected sample of dentists from all dental specialties currently working in SA.

Results

The response rate was 64% completed and returned. A statistically significantly large share of Gen X (10.1%) used digital technologies (DTs) in dental practice compared with non-digital users. 40% received sufficient postgraduate education on DT, 92% agreed that DT should be included in the undergraduate dental curriculum. However, 79% actually gained skills or knowledge on DT through workshops and formal courses. 'Lack of practitioners' awareness', 'lack of education and pioneers' and 'lack of clinical evidence' were highlighted as the foremost barriers. Lower-frequency DT users believed that treating patients with DT makes treatment more predictable. Together, clinical trials and hands-on training courses can help overcome the barriers to the adoption of new dental technologies.

Conclusions

Exploring technology adoption and usage amongst practicing dentists allows healthcare stakeholders and policymakers to set a clear direction towards the digitalisation of the healthcare system and within healthcare organisations. The study also highlighted the foremost barriers, challenges and actions towards the adoption of DTDs.