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To create and validate a transformer‐based deep neural network architecture for classifying 3D scans of teeth for computer‐assisted manufacturing and dental prosthetic rehabilitation surpassing previously reported validation accuracies obtained with convolutional neural networks (CNNs). Voxel‐based representation and encoding input data in a high‐dimensional space forms of preprocessing were investigated using 34 3D models of teeth obtained from intraoral scanning. Independent CNNs and vision transformers (ViTs), and their combination (CNN and ViT hybrid model) were implemented to classify the 3D scans directly from standard tessellation language (.stl) files and an Explainable AI (ExAI) model was generated to qualitatively explore the deterministic patterns that influenced the outcomes of the automation process. The results demonstrate that the CNN and ViT hybrid model architecture surpasses conventional supervised CNN, achieving a consistent validation accuracy of 90% through three‐fold cross‐validation. This process validated our initial findings, where each instance had the opportunity to be part of the validation set, ensuring it remained unseen during training. Furthermore, employing high‐dimensional encoding of input data solely with 3DCNN yields a validation accuracy of 80%. When voxel data preprocessing is utilized, ViT outperforms CNN, achieving validation accuracies of 80% and 50%, respectively. The study also highlighted the saliency map's ability to identify areas of tooth cavity preparation of restorative importance, that can theoretically enable more accurate 3D printed prosthetic outputs. The investigation introduced a CNN and ViT hybrid model for classification of 3D tooth models in digital dentistry, and it was the first to employ ExAI in the efforts to automate the process of dental computer‐assisted manufacturing. [ABSTRACT FROM AUTHOR]

Objectives: Advancements in artificial intelligence (AI)‐driven predictive modeling in dentistry are outpacing the clinical translation of research findings. Predictive modeling uses statistical methods to anticipate norms related to TMJ dynamics, complementing imaging modalities like cone beam computed tomography (CBCT) and magnetic resonance imaging (MRI). Deep learning, a subset of AI, helps quantify and analyze complex hierarchical relationships in occlusion and TMJ function. This narrative review explores the application of predictive modeling and deep learning to identify clinical trends and associations related to occlusion and TMJ function. Results: Debates persist regarding best practices for managing occlusal factors in temporomandibular joint (TMJ) function analysis while interpreting and quantifying findings related to the TMJ and occlusion and mitigating biases remain challenging. Data generated from noninvasive chairside tools such as jaw trackers, video tracking, and 3D scanners with virtual articulators offer unique insights by predicting variations in dynamic jaw movement, TMJ, and occlusion. The predictions help us understand the highly individualized norms surrounding TMJ function that are often required to address temporomandibular disorders (TMDs) in general practice. Conclusions: Normal TMJ function, occlusion, and the appropriate management of TMDs are complex and continue to attract ongoing debate. This review examines how predictive modeling and artificial intelligence aid in understanding occlusion and TMJ function and provides insights into complex dental conditions such as TMDs that may improve diagnosis and treatment outcomes with noninvasive techniques. [ABSTRACT FROM AUTHOR]

The Dental Loop Chatbot was developed as a real-time, evidence-based guidance system for dental practitioners using a fine-tuned large language model (LLM) and Retrieval-Augmented Generation (RAG). This paper outlines the development and preliminary evaluation of the chatbot as a scalable clinical decision-support tool designed for resource-limited settings. The system's architecture incorporates Quantized Low-Rank Adaptation (QLoRA) for efficient fine-tuning, while dynamic retrieval mechanisms ensure contextually accurate and relevant responses. This prototype lays the groundwork for future triaging and diagnostic support systems tailored specifically to the field of dentistry. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to assess the effectiveness of the best performing interpretable machine learning models in the diagnoses of leukoplakia and oral squamous cell carcinoma (OSCC). Methods: A total of 237 patient cases were analysed that included information about patient demographics, lesion characteristics, and lifestyle factors, such as age, gender, tobacco use, and lesion size. The dataset was preprocessed and normalised, and then separated into training and testing sets. The following models were tested: K-Nearest Neighbours (KNN), Logistic Regression, Naive Bayes, Support Vector Machine (SVM), and Random Forest. The overall accuracy, Kappa score, class-specific precision, recall, and F1 score were used to assess performance. SHAP (SHapley Additive ExPlanations) was used to interpret the Random Forest model and determine the contribution of each feature to the predictions. Results: The Random Forest model had the best overall accuracy (93%) and Kappa score (0.90). For OSCC, it had a precision of 0.91, a recall of 1.00, and an F1 score of 0.95. The model had a precision of 1.00, recall of 0.78, and F1 score of 0.88 for leukoplakia without dysplasia. The precision for leukoplakia with dysplasia was 0.91, the recall was 1.00, and the F1 score was 0.95. The top three features influencing the prediction of leukoplakia with dysplasia are buccal mucosa localisation, ages greater than 60 years, and larger lesions. For leukoplakia without dysplasia, the key features are gingival localisation, larger lesions, and tongue localisation. In the case of OSCC, gingival localisation, floor-of-mouth localisation, and buccal mucosa localisation are the most influential features. Conclusions: The Random Forest model outperformed the other machine learning models in diagnosing oral cancer and potentially malignant oral lesions with higher accuracy and interpretability. The machine learning models struggled to identify dysplastic changes. Using SHAP improves the understanding of the importance of features, facilitating early diagnosis and possibly reducing mortality rates. The model notably indicated that lesions on the floor of the mouth were highly unlikely to be dysplastic, instead showing one of the highest probabilities for being OSCC. [ABSTRACT FROM AUTHOR]

Purpose. To evaluate the influence of edible liquids on the characteristic properties of 3D printable materials compared to conventionally used dental resin acrylic. Method. Dental polymethyl methacrylate (PMMA) specimens were fabricated from preformed molds while polylactic acid (PLA) and polyethylene terephthalate glycol (PETG) specimens were 3D printed using fused deposition modelling at 0.1 mm layer thickness. All specimen forms adhered to ISO 37:2017 and ISO 604:2002 specifications. Specimens underwent controlled immersion for 180 hr into different media (no immersion (control), oil, soda, milk, and caffeine). Changes in mass (g), plastic deformity (N/mm2), ultimate tensile stress (N), flexural deformity (N/mm2), and break force (N) were evaluated using analysis of variance. Results. There was an increase in mass for all specimens following immersion with significant interactions between immersion media and the materials. The materials exhibited significant differences in plastic deformity (F (df) = 156.632(2), P < 0.001), ultimate tensile stress (F (df) = 109.521(2), P < 0.001), and break force (F (df) = 319.785 (2), P < 0.001) with no significant interactions with immersion media (P > 0.05) on both accounts. Materials showed no significant differences in flexural deformity (F (df) = 2.693(2), P = 0.074) but with significant interactions (F (df) = 4.984(8), P < 0.001) between acrylic and immersion media. Conclusion. Commercially available 3D printable PLA and PETG filaments printed at 0.1 mm thickness possess approximately half the mechanical resilience as dental PMMA with comparable flexural deformity after exposure to edible liquids. [ABSTRACT FROM AUTHOR]

Background. The aim of this review was to determine the current clinical applications of intraoral scanners IOS and its limitations. Materials and Methods. The literature search was carried out using electronic databases such as PubMed, Embase and Science Direct. Both observational studies like case-control, comparative, prospective, retrospective, cohort, case reports, and experimental studies like randomized control trials and cross over trials were selected for this review. The keywords used based on a search strategy: intraoral scanners, optical impressions, digital impressions, and intraoral scanning systems. Results. 59 articles out of which only 54 articles were selected in this narrative review and 5 articles were removed as these studies did not match the selection criteria and the journal articles which were published over a period of 10-years from Jan 2011 to Jan 2021 were included. Conclusion. IOS system is less time consuming when compared with other conventional methods of impression making. In terms of patient benefits, it reduces pain and discomfort. Ease of communicating with patients and educate them by providing video presentations before delivering any appliance or prostheses. Advanced technology is reshaping the future of digital dentistry, not only in orthodontics but also in other fields of dentistry as well. [ABSTRACT FROM AUTHOR]