Deep Learning-Based Recognition of Periodontitis and Dental Caries in Dental X-ray Images

Advances in hybridized nanoarchitectures for improved oro-dental health

On a global note, oral health plays a critical role in improving the overall human health. In this vein, dental-related issues with dentin exposure often facilitate the risk of developing various oral-related diseases in gums and teeth. Several oral-based ailments include gums-associated (gingivitis or periodontitis), tooth-based (dental caries, root infection, enamel erosion, and edentulous or total tooth loss), as well as miscellaneous diseases in the buccal or oral cavity (bad breath, mouth sores, and oral cancer). Although established conventional treatment modalities have been available to improve oral health, these therapeutic options suffer from several limitations, such as fail to eradicate bacterial biofilms, deprived regeneration of dental pulp cells, and poor remineralization of teeth, resulting in dental emergencies. To this end, the advent of nanotechnology has resulted in the development of various innovative nanoarchitectured composites from diverse sources. This review presents a comprehensive overview of different nanoarchitectured composites for improving overall oral health. Initially, we emphasize various oral-related diseases, providing detailed pathological circumstances and their effects on human health along with deficiencies of the conventional therapeutic modalities. Further, the importance of various nanostructured components is emphasized, highlighting their predominant actions in solving crucial dental issues, such as anti-bacterial, remineralization, and tissue regeneration abilities. In addition to an emphasis on the synthesis of different nanostructures, various nano-therapeutic solutions from diverse sources are discussed, including natural (plant, animal, and marine)-based components and other synthetic (organic- and inorganic-) architectures, as well as their composites for improving oral health. Finally, we summarize the article with an interesting outlook on overcoming the challenges of translating these innovative platforms to clinics.

An Automatic Grading System for Orthodontically Induced External Root Resorption Based on Deep Convolutional Neural Network

Orthodontically induced external root resorption (OIERR) is a common complication of orthodontic treatments. Accurate OIERR grading is crucial for clinical intervention. This study aimed to evaluate six deep convolutional neural networks (CNNs) for performing OIERR grading on tooth slices to construct an automatic grading system for OIERR. A total of 2146 tooth slices of different OIERR grades were collected and preprocessed. Six pre-trained CNNs (EfficientNet-B1, EfficientNet-B2, EfficientNet-B3, EfficientNet-B4, EfficientNet-B5, and MobileNet-V3) were trained and validated on the pre-processed images based on four different cross-validation methods. The performances of the CNNs on a test set were evaluated and compared with those of orthodontists. The gradient-weighted class activation mapping (Grad-CAM) technique was used to explore the area of maximum impact on the model decisions in the tooth slices. The six CNN models performed remarkably well in OIERR grading, with a mean accuracy of 0.92, surpassing that of the orthodontists (mean accuracy of 0.82). EfficientNet-B4 trained with fivefold cross-validation emerged as the final OIERR grading system, with a high accuracy of 0.94. Grad-CAM revealed that the apical region had the greatest effect on the OIERR grading system. The six CNNs demonstrated excellent OIERR grading and outperformed orthodontists. The proposed OIERR grading system holds potential as a reliable diagnostic support for orthodontists in clinical practice.

The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?

The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein-protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go.

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.