Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review

Innovative learning in dental education: integrating narrative and 3D industrial design for teaching caries health disease processes

INTRODUCTION

Dental education requires innovative pedagogical tools to understand the evolution of the health-disease process in individuals with dental caries. "The Origin of Teeth" integrates narrative storytelling with industrial character design to address this need.

AIM

To create and implement industrial designs based on characters from "The Origin of Teeth" to improve teaching outcomes and student engagement with the health-disease process of caries.

MATERIALS AND METHODS

Using Computer-Aided Design (CAD) and high-precision 3D printers, four anthropometric character models were developed and legally registered. A quasi-experimental study was conducted with 30 dental students from the Universidad Nacional Toribio Rodríguez de Mendoza. Pre- and post-intervention knowledge assessments were conducted using a validated multiple-choice questionaire, and data were analized using paired t-tests to evaluate the effectiveness of the intervention.

RESULTS

The educational intervention significantly increased students´ understanding of dental caries processes, with posttest scores demonstrating a mean improvement of 35% compared to pretest scores (p < 0.001). The characters successfully embodied educational content, facilitating learning through tactile and visual interaction.

CONCLUSION

Integrating narrative storytelling with 3D industrial design is a powerful educational approach, enhacing student comprehension and engagement in dental education.This methodology hold promise for broader applications in health education.

Teledentistry Applied to Health and Education Outcomes: Evidence Gap Map

BACKGROUND

Teledentistry is a field of activities that comprises information and communication technologies (ICTs) applied to dentistry, including the exchange of clinical information, patient care, and the use of educational strategies across remote distances. Its use has grown progressively over the past decades-intensified by the COVID-19 pandemic-and has been improving the provision of dental services and educational strategies ever since.

OBJECTIVE

This evidence gap map (EGM) study aims to present a collection of systematic reviews (SRs) with meta-analyses to answer the question "What are the applications of teledentistry in dental services and dental education?" by identifying gaps and current evidence on the improvement of health care and education.

METHODS

The EGM methodology has been developed by the Latin American and Caribbean Center on Health Sciences Information and is based on the concept created by the International Initiative for Impact Evaluation. Embase, PubMed, and Virtual Health Library databases were used for the literature research, using terms for teledentistry associated with eHealth, dental education, and oral health care. The data obtained from the included studies were then characterized in a Microsoft Excel spreadsheet, with a matrix containing 8 intervention groups (combined interventions, e-learning and tele-education, teleconsultation and teleservice, telemonitoring, telediagnosis, telescreening, ICTs, and artificial intelligence) and 8 outcome groups (diagnosis accuracy, education and professional training, user behavior, clinical practice, patient-centered outcomes, clinical outcomes, health services management, and access to health services). The quality of the studies was assessed using AMSTAR2 (A Measurement Tool to Assess Systematic Reviews). The visual analytics platform Tableau (Salesforce) was used to graphically display the confidence level, number of reviews, health outcomes, and intervention effects.

RESULTS

The confidence level obtained by the criteria applied was high for 28% (19/68) of the studies, moderate for 6% (4/68), low for 15% (10/68), and critically low for 51% (35/68). Among the interventions, the ICT group stood out with 182 (36.8%) out of 494 associations, followed by interventions with e-learning and tele-education (n=96, 19.4% of associations), telediagnosis (n=67, 13.6%), and combined interventions (n=53, 10.7%). Most of the outcomes were aimed at education and professional training (97/494, 19.6% of associations), patient-centered outcomes (74/494, 15%), and health services management (60/494, 12.1%).

CONCLUSIONS

This EGM presents an overview of the contributions of teledentistry in patient care, health services, clinical practice, and education. The study results may help guide future research and policy decisions and serve as a convenient virtual tool for accessing valuable evidence-based information on teledentistry.

A Patient-Centered Perspectives and Future Directions in AI-powered Teledentistry

This scoping review investigates the integration of AI into teledentistry with a focus on patient-centered perspectives and future directions. Teledentistry has progressed rapidly in the COVID-19 pandemic period, providing remote dental care by means of digital communication technologies.The introduction of AI has made diagnosis more precise, treatment planning more personalized, and processes more efficient and have also made dental services better accessible to the underserved. AI algorithms help in early diagnosis of dental issues, provides customized treatment plans, and improve patient outcomes. Despite the advantages, still many challenges exist. These are ethical concerns, data privacy issues, and regulatory hurdles that prevent widespread adoption. Use of AI in dental settings results in patients having mixed sentiments surrounding trust and data security arising out of fear of having   reduced   personal interactions with providers. Additionally, AI driven teledentistry is not validated in large scale clinical setting and cost effectiveness assessment which undermines scalability. This review identifies gaps in existing research and provides guidance for how patient-centered applications further facilitate increased transparency, AI education, and cross-disciplinary collaboration among dentists, computer scientists, ethicists, and policymakers. The future research should include clinical validation, economics, and ethical standards to make AI teledentistry use responsible and inclusive. This scoping review equips clinicians and researchers with a roadmap for responsible, patient-centered implementation of AI-enabled teledentistry, offering practical strategies and insights to enhance the quality and accessibility of remote dental care.

Forensic odontology in DVI-A path forward

Dental identification is a pillar of disaster victim identification (DVI). Dental identification is accurate, efficient, inexpensive, and accepted in courts of law. The (known) antemortem (AM) dental charts and radiographic images acquired from the dentist of the missing person are evaluated, processed, and compared to post mortem (PM) findings present in the dentition or fragments of the dentition of the deceased individual. These comparisons evaluate and assess individuating restorative dental work, dental anatomical areas of concordance, spatial relationships of teeth one to another, and occasionally calculate the degree of "uniqueness" of either or both of the AM and PM dentition compared to known population databases. In a multiple fatality incident, odontologists may utilize age stratification to assist other means of identification. Computer comparison algorithms using recorded data can indicate possible matches between AM and PM data sets. Following clinical assessment, collection of post mortem tooth specimens for DNA profiling generation may be undertaken. This paper will highlight modern and efficient use of these tools. The framework for how dental identification in these incidents is currently managed is presented. The authors propose a change to this approach that moves away from interpretive subjective assessment toward comparisons based largely on objective data. The aim of this paper is to highlight the benefits of minimizing subjective decisions and maximizing objective data in the dental DVI process while simultaneously reducing risk to clinical personnel and minimizing costs by reducing the number of clinicians required onsite.

Empowering Modern Dentistry: The Impact of Artificial Intelligence on Patient Care and Clinical Decision Making

Advancements in artificial intelligence (AI) are poised to catalyze a transformative shift across diverse dental disciplines including endodontics, oral radiology, orthodontics, pediatric dentistry, periodontology, prosthodontics, and restorative dentistry. This narrative review delineates the burgeoning role of AI in enhancing diagnostic precision, streamlining treatment planning, and potentially unveiling innovative therapeutic modalities, thereby elevating patient care standards. Recent analyses corroborate the superiority of AI-assisted methodologies over conventional techniques, affirming their capacity for personalization, accuracy, and efficiency in dental care. Central to these AI applications are convolutional neural networks and deep learning models, which have demonstrated efficacy in diagnosis, prognosis, and therapeutic decision making, in some instances surpassing traditional methods in complex cases. Despite these advancements, the integration of AI into clinical practice is accompanied by challenges, such as data security concerns, the demand for transparency in AI-generated outcomes, and the imperative for ongoing validation to establish the reliability and applicability of AI tools. This review underscores the prospective benefits of AI in dental practice, envisioning AI not as a replacement for dental professionals but as an adjunctive tool that fortifies the dental profession. While AI heralds improvements in diagnostics, treatment planning, and personalized care, ethical and practical considerations must be meticulously navigated to ensure responsible development of AI in dentistry.

Performance of Generative Artificial Intelligence in Dental Licensing Examinations

OBJECTIVES

Generative artificial intelligence (GenAI), including large language models (LLMs), has vast potential applications in health care and education. However, it is unclear how proficient LLMs are in interpreting written input and providing accurate answers in dentistry. This study aims to investigate the accuracy of GenAI in answering questions from dental licensing examinations.

METHODS

A total of 1461 multiple-choice questions from question books for the US and the UK dental licensing examinations were input into 2 versions of ChatGPT 3.5 and 4.0. The passing rates of the US and UK dental examinations were 75.0% and 50.0%, respectively. The performance of the 2 versions of GenAI in individual examinations and dental subjects was analysed and compared.

RESULTS

ChatGPT 3.5 correctly answered 68.3% (n = 509) and 43.3% (n = 296) of questions from the US and UK dental licensing examinations, respectively. The scores for ChatGPT 4.0 were 80.7% (n = 601) and 62.7% (n = 429), respectively. ChatGPT 4.0 passed both written dental licensing examinations, whilst ChatGPT 3.5 failed. ChatGPT 4.0 answered 327 more questions correctly and 102 incorrectly compared to ChatGPT 3.5 when comparing the 2 versions.

CONCLUSIONS

The newer version of GenAI has shown good proficiency in answering multiple-choice questions from dental licensing examinations. Whilst the more recent version of GenAI generally performed better, this observation may not hold true in all scenarios, and further improvements are necessary. The use of GenAI in dentistry will have significant implications for dentist-patient communication and the training of dental professionals.

Deep learning-based tooth segmentation methods in medical imaging: A review

Deep learning approaches for tooth segmentation employ convolutional neural networks (CNNs) or Transformers to derive tooth feature maps from extensive training datasets. Tooth segmentation serves as a critical prerequisite for clinical dental analysis and surgical procedures, enabling dentists to comprehensively assess oral conditions and subsequently diagnose pathologies. Over the past decade, deep learning has experienced significant advancements, with researchers introducing efficient models such as U-Net, Mask R-CNN, and Segmentation Transformer (SETR). Building upon these frameworks, scholars have proposed numerous enhancement and optimization modules to attain superior tooth segmentation performance. This paper discusses the deep learning methods of tooth segmentation on dental panoramic radiographs (DPRs), cone-beam computed tomography (CBCT) images, intro oral scan (IOS) models, and others. Finally, we outline performance-enhancing techniques and suggest potential avenues for ongoing research. Numerous challenges remain, including data annotation and model generalization limitations. This paper offers insights for future tooth segmentation studies, potentially facilitating broader clinical adoption.

The Impetus of Artificial Intelligence on Periodontal Diagnosis: A Brief Synopsis

The current advances in digitized data additions, machine learning and computing framework, lead to the swiftly emerging concept of "Artificial Intelligence" (AI), that are developing into areas that were formerly contemplated for human expertise. AI is a relatively rapid paced mechanics wherein the computer technology is tuned to perform human tasks. An auxiliary domain of AI is machine learning (ML), and Deep learning, a subclass of ML technique comprehends multi-layer mathematical operations. AI-based applications have tremendous potential to improve and systematize patient care thereby alleviating dentists from laborious regular tasks, and facilitate personalized, predictive and preventive dentistry. In the dental clinic, AI can execute a variety of easy tasks with greater accuracy, minimal manpower, and with fewer mistakes over human equivalents. These tasks range from appointment scheduling and coordination to helping with clinical evaluation and therapy. Besides, this could assist in the early diagnosis of dental and maxillofacial abnormalities like periodontal ailments, root caries, bony lesions, and facial malformations in addition to automatically identifying and classifying dental restorations on digital radiographs. This brusque narrative review describes the AI-based systems, their respective applications in periodontal diagnosis, the multifarious studies, possible limitations and the predictable future of AI-based dental diagnostics and treatment planning.

Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review

Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning. In addition, the review critically evaluates the literature to guide and further develop future studies in this field. An extensive literature search was conducted for the 2019-May 2023 range using the Medline (PubMed) and Google Scholar databases to identify eligible articles, and 101 studies were shortlisted, including applications for diagnosing dental anomalies (n = 22) and diseases (n = 79) using deep learning for classification, object detection, and segmentation tasks. According to the results, the most commonly used task type was classification (n = 51), the most commonly used dental image material was panoramic radiographs (n = 55), and the most frequently used performance metric was sensitivity/recall/true positive rate (n = 87) and accuracy (n = 69). Dataset sizes ranged from 60 to 12,179 images. Although deep learning algorithms are used as individual or at least individualized architectures, standardized architectures such as pre-trained CNNs, Faster R-CNN, YOLO, and U-Net have been used in most studies. Few studies have used the explainable AI method (n = 22) and applied tests comparing human and artificial intelligence (n = 21). Deep learning is promising for better diagnosis and treatment planning in dentistry based on the high-performance results reported by the studies. For all that, their safety should be demonstrated using a more reproducible and comparable methodology, including tests with information about their clinical applicability, by defining a standard set of tests and performance metrics.

Transforming Dental Caries Diagnosis Through Artificial Intelligence-Based Techniques

Diagnosing dental caries plays a pivotal role in preventing and treating tooth decay. However, traditional methods of diagnosing caries often fall short in accuracy and efficiency. Despite the endorsement of radiography as a diagnostic tool, the identification of dental caries through radiographic images can be influenced by individual interpretation. Incorporating artificial intelligence (AI) into diagnosing dental caries holds significant promise, potentially enhancing the precision and efficiency of diagnoses. This review introduces the fundamental concepts of AI, including machine learning and deep learning algorithms, and emphasizes their relevance and potential contributions to the diagnosis of dental caries. It further explains the process of gathering and pre-processing radiography data for AI examination. Additionally, AI techniques for dental caries diagnosis are explored, focusing on image processing, analysis, and classification models for predicting caries risk and severity. Deep learning applications in dental caries diagnosis using convolutional neural networks are presented. Furthermore, the integration of AI systems into dental practice is discussed, including the challenges and considerations for implementation as well as ethical and legal aspects. The breadth of AI technologies and their prospective utility in clinical scenarios for diagnosing dental caries from dental radiographs is presented. This review outlines the advancements of AI and its potential in revolutionizing dental caries diagnosis, encouraging further research and development in this rapidly evolving field.

Artificial intelligence in dentistry-A review

Artificial Intelligence (AI) is the ability of machines to perform tasks that normally require human intelligence. AI is not a new term, the concept of AI can be dated back to 1950. However, it did not become a practical tool until two decades ago. Owing to the rapid development of three cornerstones of current AI technology-big data (coming through digital devices), computational power, and AI algorithm-in the past two decades, AI applications have started to provide convenience to people's lives. In dentistry, AI has been adopted in all dental disciplines, i.e., operative dentistry, periodontics, orthodontics, oral and maxillofacial surgery, and prosthodontics. The majority of the AI applications in dentistry are for diagnosis based on radiographic or optical images, while other tasks are not as applicable as image-based tasks mainly due to the constraints of data availability, data uniformity, and computational power for handling 3D data. Evidence-based dentistry (EBD) is regarded as the gold standard for decision making by dental professionals, while AI machine learning (ML) models learn from human expertise. ML can be seen as another valuable tool to assist dental professionals in multiple stages of clinical cases. This review describes the history and classification of AI, summarizes AI applications in dentistry, discusses the relationship between EBD and ML, and aims to help dental professionals better understand AI as a tool to support their routine work with improved efficiency.