Deep learning and clustering approaches for dental implant size classification based on periapical radiographs

Optimized classification of dental implants using convolutional neural networks and pre-trained models with preprocessed data

OBJECTIVE

This study evaluates the performance of various classifiers and pre-trained models for dental implant state classification using preprocessed radiography images with masks.

METHODOLOGY

A dataset of 511 periapical images, including 275 for Bicon, 70 for Bego, and 166 for ITI implants, was expanded to 5110 images using data augmentation techniques such as rotation, flipping, and scaling. Preprocessing included resizing, sharpening, noise reduction, CLAHE-based contrast enhancement, implant-specific masking, and normalization. Classifiers including Convolutional Neural Networks (CNN), Convolutional Support Vector Machine (CSVM), Convolutional Decision Tree (CDT), and Convolutional Random Forest (CRF) were employed. Pre-trained models such as VGG16, ResNet50, and Xception enhanced feature extraction. Model performance was assessed using accuracy, precision, recall, F1 score, and ROC AUC, with fivefold cross-validation ensuring robustness.

RESULTS

CRF achieved the highest performance for ITI with Bego implants, with accuracy of 0.8966, precision of 0.9364, recall of 0.9253, F1 score of 0.9304, and ROC AUC of 0.9351. CNN delivered the best results for Bicon with Bego implants, achieving 0.9533 accuracy. Among pre-trained models, VGG16 with preprocessed data achieved superior results for Bicon vs. ITI classification, with 0.9865 accuracy and 0.9877 ROC AUC. Data augmentation and preprocessing significantly improved classifier performance.

CONCLUSION

Preprocessing steps, coupled with data augmentation, enhanced classification performance, ensuring robustness across models. CRF and CNN were the top-performing classifiers, with VGG16 excelling among pre-trained models. These results highlight the importance of data augmentation and preprocessing in improving dental implant classification accuracy.

Exploring a decade of deep learning in dentistry: A comprehensive mapping review

OBJECTIVES

Artificial Intelligence (AI), particularly deep learning, has significantly impacted healthcare, including dentistry, by improving diagnostics, treatment planning, and prognosis prediction. This systematic mapping review explores the current applications of deep learning in dentistry, offering a comprehensive overview of trends, models, and their clinical significance.

MATERIALS AND METHODS

Following a structured methodology, relevant studies published from January 2012 to September 2023 were identified through database searches in PubMed, Scopus, and Embase. Key data, including clinical purpose, deep learning tasks, model architectures, and data modalities, were extracted for qualitative synthesis.

RESULTS

From 21,242 screened studies, 1,007 were included. Of these, 63.5% targeted diagnostic tasks, primarily with convolutional neural networks (CNNs). Classification (43.7%) and segmentation (22.9%) were the main methods, and imaging data-such as cone-beam computed tomography and orthopantomograms-were used in 84.4% of cases. Most studies (95.2%) applied fully supervised learning, emphasizing the need for annotated data. Pathology (21.5%), radiology (17.5%), and orthodontics (10.2%) were prominent fields, with 24.9% of studies relating to more than one specialty.

CONCLUSION

This review explores the advancements in deep learning in dentistry, particulary for diagnostics, and identifies areas for further improvement. While CNNs have been used successfully, it is essential to explore emerging model architectures, learning approaches, and ways to obtain diverse and reliable data. Furthermore, fostering trust among all stakeholders by advancing explainable AI and addressing ethical considerations is crucial for transitioning AI from research to clinical practice.

CLINICAL RELEVANCE

This review offers a comprehensive overview of a decade of deep learning in dentistry, showcasing its significant growth in recent years. By mapping its key applications and identifying research trends, it provides a valuable guide for future studies and highlights emerging opportunities for advancing AI-driven dental care.

Race to the Moon or the Bottom? Applications, Performance, and Ethical Considerations of Artificial Intelligence in Prosthodontics and Implant Dentistry

Objectives: This review aims to explore the applications of artificial intelligence (AI) in prosthodontics and implant dentistry, focusing on its performance outcomes and associated ethical concerns. Materials and Methods: Following the PRISMA guidelines, a search was conducted across databases such as PubMed, Medline, Web of Science, and Scopus. Studies published between January 2022 and May 2024, in English, were considered. The Population (P) included patients or extracted teeth with AI applications in prosthodontics and implant dentistry; the Intervention (I) was AI-based tools; the Comparison (C) was traditional methods, and the Outcome (O) involved AI performance outcomes and ethical considerations. The Newcastle-Ottawa Scale was used to assess the quality and risk of bias in the studies. Results: Out of 3420 initially identified articles, 18 met the inclusion criteria for AI applications in prosthodontics and implant dentistry. The review highlighted AI's significant role in improving diagnostic accuracy, treatment planning, and prosthesis design. AI models demonstrated high accuracy in classifying dental implants and predicting implant outcomes, although limitations were noted in data diversity and model generalizability. Regarding ethical issues, five studies identified concerns such as data privacy, system bias, and the potential replacement of human roles by AI. While patients generally viewed AI positively, dental professionals expressed hesitancy due to a lack of familiarity and regulatory guidelines, highlighting the need for better education and ethical frameworks. Conclusions: AI has the potential to revolutionize prosthodontics and implant dentistry by enhancing treatment accuracy and efficiency. However, there is a pressing need to address ethical issues through comprehensive training and the development of regulatory frameworks. Future research should focus on broadening AI applications and addressing the identified ethical concerns.

Artificial neural networks development in prosthodontics - a systematic mapping review

OBJECTIVES

This study aimed to systematically categorize the available literature and offer a comprehensive overview of artificial neural network (ANN) prediction models in prosthodontics. Specifically, the present research introduces a systematic analysis of ANN aims, data, architectures, evaluation metrics, and limitations in prosthodontics.

DATA

The review included articles published until June 2024. The search terms included "prosthodontics" (and related MeSH terms), "neural networks", and "deep learning". Out of 597 identified articles, 70 reports remained after deduplication and screening (2007-2024). Of these, 33 % were from 2023. Implant prosthodontics was the focus in approximately 29 % of reports, and non-implant prosthodontics in 71 %.

SOURCES

Data were collected through electronic searches of PubMed MedLine, PubMed Central, ScienceDirect, Web of Science, and IEEE Xplore databases, along with manual searches in specific journals.

STUDY SELECTION

This study focused on English-language research articles and conference proceedings detailing the development and implementation of ANN prediction models specifically designed for prosthodontics.

CONCLUSIONS

This study shows how ANN models are used in implant and non-implant prosthodontics, with various types of data, architectures, and metrics used for their development and evaluation. It also reveals limitations in ANN development, particularly in the data lifecycle.

CLINICAL SIGNIFICANCE

This study equips practitioners with insights, guiding them in optimizing clinical protocols through ANN integration and facilitating informed decision-making on commercially available systems. Additionally, it supports regulatory efforts, smoothing the path for AI integration in dentistry. Moreover, it sets a trajectory for future exploration, identifying untapped tools and research avenues, fostering interdisciplinary collaborations, and driving innovation in the field.

Multi-label dental disorder diagnosis based on MobileNetV2 and swin transformer using bagging ensemble classifier

Dental disorders are common worldwide, causing pain or infections and limiting mouth opening, so dental conditions impact productivity, work capability, and quality of life. Manual detection and classification of oral diseases is time-consuming and requires dentists' evaluation and examination. The dental disease detection and classification system based on machine learning and deep learning will aid in early dental disease diagnosis. Hence, this paper proposes a new diagnosis system for dental diseases using X-ray imaging. The framework includes a robust pre-processing phase that uses image normalization and adaptive histogram equalization to improve image quality and reduce variation. A dual-stream approach is used for feature extraction, utilizing the advantages of Swin Transformer for capturing long-range dependencies and global context and MobileNetV2 for effective local feature extraction. A thorough representation of dental anomalies is produced by fusing the extracted features. To obtain reliable and broadly applicable classification results, a bagging ensemble classifier is utilized in the end. We evaluate our model on a benchmark dental radiography dataset. The experimental results and comparisons show the superiority of the proposed system with 95.7% for precision, 95.4% for sensitivity, 95.7% for specificity, 95.5% for Dice similarity coefficient, and 95.6% for accuracy. The results demonstrate the effectiveness of our hybrid model integrating MoileNetv2 and Swin Transformer architectures, outperforming state-of-the-art techniques in classifying dental diseases using dental panoramic X-ray imaging. This framework presents a promising method for robustly and accurately diagnosing dental diseases automatically, which may help dentists plan treatments and identify dental diseases early on.

A comprehensive biomechanical evaluation of length and diameter of dental implants using finite element analyses: A systematic review

Background

With a wide range of dental implants currently used in clinical scenarios, evidence is limited on selecting the type of dental implant best suited to endure the biting force of missing teeth. Finite Element Analysis (FEA) is a reliable technology which has been applied in dental implantology to study the distribution of biomechanical stress within the bone and dental implants.

Purpose

This study aimed to perform a systematic review to evaluate the biomechanical properties of dental implants regarding their length and diameter using FEA.

Material and methods

A comprehensive search was performed in PubMed/MEDLINE, Scopus, Embase, and Web of Science for peer-reviewed studies published in English from October 2003 to October 2023. Data were organized based on the following topics: area, bone layers, type of bone, design of implant, implant material, diameter of implant, length of implant, stress units, type of loading, experimental validation, convergence analysis, boundary conditions, parts of Finite Element Model, stability factor, study variables, and main findings. The present study is registered in PROSPERO under number CRD42022382211.

Results

The query yielded 852 results, of which 40 studies met the inclusion criteria and were selected in this study. The diameter and length of the dental implants were found to significantly influence the stress distribution in cortical and cancellous bone, respectively. Implant diameter was identified as a key factor in minimizing peri-implant stress concentrations and avoiding crestal overloading. In terms of stress reduction, implant length becomes increasingly important as bone density decreases.

Conclusions

The diameter of dental implants is more important than implant length in reducing bone stress distribution and improving implant stability under both static and immediate loading conditions. Short implants with a larger diameter were found to generate lower stresses than longer implants with a smaller diameter. Other potential influential design factors including implant system, cantilever length, thread features, and abutment collar height should also be considered in future implant design as they may also have an impact on implant performance.