Applications and perspectives of artificial intelligence, machine learning and “dentronics” in dentistry: A literature review

Revolutionizing CAD/CAM-based restorative dental processes and materials with artificial intelligence: a concise narrative review

Artificial intelligence (AI) is increasingly prevalent in biomedical and industrial development, capturing the interest of dental professionals and patients. Its potential to improve the accuracy and speed of dental procedures is set to revolutionize dental care. The use of AI in computer-aided design/computer-aided manufacturing (CAD/CAM) within the restorative dental and material science fields offers numerous benefits, providing a new dimension to these practices. This study aims to provide a concise overview of the implementation of AI-powered technologies in CAD/CAM restorative dental procedures and materials. A comprehensive literature search was conducted using keywords from 2000 to 2023 to obtain pertinent information. This method was implemented to guarantee a thorough investigation of the subject matter. Keywords included; "Artificial Intelligence", "Machine Learning", "Neural Networks", "Virtual Reality", "Digital Dentistry", "CAD/CAM", and "Restorative Dentistry". Artificial intelligence in digital restorative dentistry has proven to be highly beneficial in various dental CAD/CAM applications. It helps in automating and incorporating esthetic factors, occlusal schemes, and previous practitioners' CAD choices in fabricating dental restorations. AI can also predict the debonding risk of CAD/CAM restorations and the compositional effects on the mechanical properties of its materials. Continuous enhancements are being made to overcome its limitations and open new possibilities for future developments in this field.

Applications of artificial intelligence in the utilisation of imaging modalities in dentistry: A systematic review and meta-analysis of in-vitro studies

Background

In the past, dentistry heavily relied on manual image analysis and diagnostic procedures, which could be time-consuming and prone to human error. The advent of artificial intelligence (AI) has brought transformative potential to the field, promising enhanced accuracy and efficiency in various dental imaging tasks. This systematic review and meta-analysis aimed to comprehensively evaluate the applications of AI in dental imaging modalities, focusing on in-vitro studies.

Methods

A systematic literature search was conducted, in accordance with the PRISMA guidelines. The following databases were systematically searched: PubMed/MEDLINE, Embase, Web of Science, Scopus, IEEE Xplore, Cochrane Library, CINAHL (Cumulative Index to Nursing and Allied Health Literature), and Google Scholar. The meta-analysis employed fixed-effects models to assess AI accuracy, calculating odds ratios (OR) for true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), and negative predictive value (NPV) with 95 % confidence intervals (CI). Heterogeneity and overall effect tests were applied to ensure the reliability of the findings.

Results

9 studies were selected that encompassed various objectives, such as tooth segmentation and classification, caries detection, maxillofacial bone segmentation, and 3D surface model creation. AI techniques included convolutional neural networks (CNNs), deep learning algorithms, and AI-driven tools. Imaging parameters assessed in these studies were specific to the respective dental tasks. The analysis of combined ORs indicated higher odds of accurate dental image assessments, highlighting the potential for AI to improve TPR, TNR, PPV, and NPV. The studies collectively revealed a statistically significant overall effect in favor of AI in dental imaging applications.

Conclusion

In summary, this systematic review and meta-analysis underscore the transformative impact of AI on dental imaging. AI has the potential to revolutionize the field by enhancing accuracy, efficiency, and time savings in various dental tasks. While further research in clinical settings is needed to validate these findings and address study limitations, the future implications of integrating AI into dental practice hold great promise for advancing patient care and the field of dentistry.

Modular training model for education of students in restorative and prosthodontic dentistry

INTRODUCTION

At the moment, no commercial model solution is available for the individualisation of the dentition depending on the clinical case scenario. Furthermore, the realistic training of most restorative and prosthodontic procedures on a single dental study model is not possible. The aim of this study was the creation of a new training model to fill this gap.

MATERIALS AND METHODS

Complete upper and lower jaw models were created based on existing scans and radiological data from a patient. All components for 100 complete models and 1128 teeth for the training were produced with a SLA-printer. Overall, 94 voluntary students attending the first and second preclinical course in prosthodontics tested the functionality of the model with three different tooth types against a standard dental study model and real teeth. After the training, the model was rated in a questionnaire.

RESULTS

The production of the models and teeth was feasible. The overall rating of the different teeth was worse for type I (Ø 3.6 ± 1.1), significantly better for type II (Ø 2.5 ± 1.0) and type III (Ø 2.4 ± 1.0) than a standard typodont tooth (Ø 2.7 ± 1.1). The new model was rated significantly better overall (Ø 2.6 ± 1.0) than the standard training model (Ø 3.0 ± 1.1).

CONCLUSIONS

The aim of this study was fulfilled. A superior training model was created with equivalent and better tooth types. The new teeth were outstanding in terms of cost-efficiency, appearance and feeling during preparation.