Artificial Intelligence in Dentistry-Narrative Review

Automated diagnosis and classification of temporomandibular joint degenerative disease via artificial intelligence using CBCT imaging

OBJECTIVES

In this study, artificial intelligence (AI) techniques were used to achieve automated diagnosis and classification of temporomandibular joint (TMJ) degenerative joint disease (DJD) on cone beam computed tomography (CBCT) images.

METHODS

An AI model utilizing the YOLOv10 algorithm was trained, validated and tested on 7357 annotated and corrected oblique sagittal TMJ images (3010 images of normal condyles and 4347 images of condyles with DJD) from 1018 patients who visited Peking University School and Hospital of Stomatology for temporomandibular disorders and underwent TMJ CBCT examinations. This model could identify DJD as well as the radiographic signs of DJD, namely, erosion, osteophytes, sclerosis and subchondral cysts. The diagnosis and classification performances of the model were evaluated on the test set. The accuracy of the model for evaluating images with one to four DJD signs was also evaluated.

RESULTS

The accuracy, precision, sensitivity, specificity, F1 score and mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 of the model for DJD detection all exceeded 0.95. The accuracies for identifying erosion, osteophytes, sclerosis and subchondral cysts were 0.91, 0.96, 0.91 and 0.96, respectively. The precisions, specificities and F1 scores for the DJD signs were all >0.90. The sensitivity ranged from 0.88 to 0.95, and the mAP (IoU=0.5) ranged from 0.87 to 0.97. The accuracies of the model for detecting one to four DJD signs in one image were 94 %, 84 %, 66 % and 63 %, respectively.

CONCLUSIONS

A deep learning model based on the YOLOv10 algorithm can not only detect the presence of TMJ DJD on CBCT images but also differentiate the typical radiographic signs of DJD, including erosion, osteophytes, sclerosis and subchondral cysts, with acceptable accuracy.

CLINICAL SIGNIFICANCE

TMJ DJD is a very common disease that causes joint pain and mandibular dysfunction and affects patients' quality of life; therefore, early diagnosis and intervention are particularly important. However, identifying radiographic signs of early-stage TMJ DJD is difficult. AI can quickly review CBCT images and assist in the accurate and rapid diagnosis and classification of TMJ DJD.

Machine Learning Approach to Investigating Macrophage Polarization on Various Titanium Surface Characteristics

Objective: Current laboratory studies on the effect of biomaterial properties on immune reactions are incomplete and based on a single or a few combination features of the biomaterial design. This study utilizes intelligent prediction models to explore the key features of titanium implant materials in macrophage polarization. Impact Statement: This pilot study provided some insights into the great potential of machine learning in exploring bone immunomodulatory biomaterials. Introduction: Titanium materials are commonly utilized as bone replacement materials to treat missing teeth and bone defects. The immune response caused by implant materials after implantation in the body has a double-edged sword effect on osseointegration. Macrophage polarization has been extensively explored to understand early material-mediated immunomodulation. However, understanding of implant material surface properties and immunoregulations remains limited due to current experimental settings, which are based on trial-by-trial approaches. Artificial intelligence, with its capacity to analyze large datasets, can help explore complex material-cell interactions. Methods: In this study, the effect of titanium surface properties on macrophage polarization was analyzed using intelligent prediction models, including random forest, extreme gradient boosting, and multilayer perceptron. Additionally, data extracted from the newly published literature were further input into the trained models to validate their performance. Results: The analysis identified "cell seeding density", "contact angle", and "roughness" as the most important features regulating interleukin 10 and tumor necrosis factor α secretion. Additionally, the predicted interleukin 10 levels closely matched the experimental results from newly published literature, while the tumor necrosis factor α predictions exhibited consistent trends. Conclusion: The polarization response of macrophages seeded on titanium materials is influenced by multiple factors, and artificial intelligence can assist in extracting the key features of implant materials for immunoregulation.

Evaluating ChatGPT-4's performance on oral and maxillofacial queries: Chain of Thought and standard method

Objectives

Oral and maxillofacial diseases affect approximately 3.5 billion people worldwide. With the continuous advancement of Artificial Intelligence technologies, particularly the application of generative pre-trained transformers like ChatGPT-4, there is potential to enhance public awareness of the prevention and early detection of these diseases. This study evaluated the performance of ChatGPT-4 in addressing oral and maxillofacial disease questions using standard approaches and the Chain of Thought (CoT) method, aiming to gain a deeper understanding of its capabilities, potential, and limitations.

Materials and methods

Three experts, drawing from their extensive experience and the most common questions in clinical settings, selected 130 open-ended questions and 1,805 multiple-choice questions from the national dental licensing examination. These questions encompass 12 areas of oral and maxillofacial surgery, including Prosthodontics, Pediatric Dentistry, Maxillofacial Tumors and Salivary Gland Diseases, and maxillofacial Infections.

Results

Using CoT approach, ChatGPT-4 exhibited marked enhancements in accuracy, structure, completeness, professionalism, and overall impression for open-ended questions, revealing statistically significant differences compared to its performance on general oral and maxillofacial inquiries. In the realm of multiple-choice questions, the application of CoT method boosted ChatGPT-4's accuracy across all major subjects, achieving an overall accuracy increase of 3.1%.

Conclusions

When employing ChatGPT-4 to address questions in oral and maxillofacial surgery, incorporating CoT as a querying method can enhance its performance and help the public improve their understanding and awareness of such issues. However, it is not advisable to consider it a substitute for doctors.

A Deep Learning-Based Approach to Detect Lamina Dura Loss on Periapical Radiographs

This study aimed to develop a custom artificial intelligence (AI) model for detecting lamina dura (LD) loss around the roots of anterior and posterior teeth on intraoral periapical radiographs. A total of 701 periapical radiographs of the anterior and posterior regions retrieved from the Dentomaxillofacial Radiology archives were reviewed. Images were cropped to include only the teeth exhibiting LD loss and those without LD loss, which were labeled as "1" and "0," respectively. The dataset was diversified using image preprocessing and data augmentation techniques. Among the radiographs, 72% were used for training, 18% for validation, and 10% for testing. A custom AI model, consisting of 4 blocks and 49 layers, with a total of 21.2 million parameters, was developed using the TensorFlow library and residual blocks introduced in ResNet architecture. Sensitivity, specificity, accuracy, precision, F1 score, and kappa (κ) coefficients (for intra-observer agreement) were calculated to evaluate the performance of the AI model. When applied to a test set of 71 images, the AI model showed good performance in detecting LD loss, achieving an average sensitivity of 0.730, specificity of 0.706, accuracy of 0.718, precision of 0.730, and an F1 score of 0.730, regardless of the dental region. This study represents the first known application of an AI algorithm tailored to detect LD loss on periapical radiographs. The developed AI model could aid clinicians in making accurate diagnosis and help prevent misdiagnosis.

A novel deep learning-based pipeline architecture for pulp stone detection on panoramic radiographs

OBJECTIVES

Pulp stones are ectopic calcifications located in pulp tissue. The aim of this study is to introduce a novel method for detecting pulp stones on panoramic radiography images using a deep learning-based two-stage pipeline architecture.

MATERIALS AND METHODS

The first stage involved tooth localization with the YOLOv8 model, followed by pulp stone classification using ResNeXt. 375 panoramic images were included in this study, and a comprehensive set of evaluation metrics, including precision, recall, false-negative rate, false-positive rate, accuracy, and F1 score was employed to rigorously assess the performance of the proposed architecture.

RESULTS

Despite the limited annotated training data, the proposed method achieved impressive results: an accuracy of 95.4%, precision of 97.1%, recall of 96.1%, false-negative rate of 3.9%, false-positive rate of 6.1%, and a F1 score of 96.6%, outperforming existing approaches in pulp stone detection.

CONCLUSIONS

Unlike current studies, this approach adopted a more realistic scenario by utilizing a small dataset with few annotated samples, acknowledging the time-consuming and error-prone nature of expert labeling. The proposed system is particularly beneficial for dental students and newly graduated dentists who lack sufficient clinical experience, as it aids in the automatic detection of pulpal calcifications. To the best of our knowledge, this is the first study in the literature that propose a pipeline architecture to address the PS detection tasks on panoramic images.

Performance of 4 Artificial Intelligence Chatbots in Answering Endodontic Questions

INTRODUCTION

Artificial intelligence models have shown potential as educational tools in healthcare, such as answering exam questions. This study aimed to assess the performance of 4 prominent chatbots: ChatGPT-4o, MedGebra GPT-4o, Meta LIama 3, and Gemini Advanced in answering multiple-choice questions (MCQs) in endodontics.

METHODS

The study utilized 100 MCQs, each with 4 potential answers. These MCQs were obtained from 2 well-known endodontic textbooks. The performance of the above chatbots regarding choosing the correct answers was assessed twice with a 1-week interval.

RESULTS

The stability of the performance in the 2 rounds was highest for ChatGPT-4o, followed by Gemini Advanced and Meta Llama 3. MedGebra GPT-4o provided the highest percentage of true answers in the first round (93%) followed by ChatGPT-4o in the second round (90%). Meta Llama 3 provided the lowest percentages in the first (73%) and second rounds (75%). Although the performance of MedGebra GPT-4o was the best in the first round, it was less stable upon the second round (McNemar P > .05; Kappa = 0.725, P < .001).

CONCLUSIONS

ChatGPT-4o and MedGebra GPT-4o answered a high fraction of endodontic MCQs, while Meta LIama 3 and Gemini Advanced showed lower performance. Further training and development are required to improve their accuracy and reliability in endodontics.

Diagnosis of approximal caries in children with convolutional neural networks based detection algorithms on radiographs: A pilot study

OBJECTIVES

Approximal caries diagnosis in children is difficult, and artificial intelligence-based research in pediatric dentistry is scarce. To create a convolutional neural network (CNN)-based diagnostic system for the prompt and efficient identification of approximal caries in pediatric patients aged 5-12 years.

MATERIALS AND METHODS

Pediatric patients' digital periapical radiographic images were collected to create a unique dataset. Various augmentation methods were used, and approximal caries in the augmented images were labeled by a pediatric dentist to minimize labeling errors. The dataset consisted of 830 data labeled for approximal caries on 415 images, which were divided into 80% training and 20% testing sets. After comparing 13 detection algorithms, including the latest YOLOv8, the most appropriate one was selected for the proposed system, which was then evaluated based on various performance metrics.

RESULTS

The proposed detection system achieved a precision of 91.2%, an accuracy of 90.8%, a recall of 89.3%, and an F1 value of 90.24% after 300 iterations, utilizing a learning rate of 0.01.

CONCLUSION

Approximal caries has been successfully detected with the developed system. Future efforts will focus on augmenting the dataset and expanding the sample size to enhance the efficacy of the system.

Artificial intelligence in dentistry: Assessing the informational quality of YouTube videos

BACKGROUND AND PURPOSE

The most widely used social media platform for video content is YouTubeTM. The present study evaluated the quality of information on YouTubeTM on artificial intelligence (AI) in dentistry.

METHODS

This cross-sectional study used YouTubeTM (https://www.youtube.com) for searching videos. The terms used for the search were "artificial intelligence in dentistry," "machine learning in dental care," and "deep learning in dentistry." The accuracy and reliability of the information source were assessed using the DISCERN score. The quality of the videos was evaluated using the modified Global Quality Score (mGQS) and the Journal of the American Medical Association (JAMA) score.

RESULTS

The analysis of 91 YouTube™ videos on AI in dentistry revealed insights into video characteristics, content, and quality. On average, videos were 22.45 minutes and received 1715.58 views and 23.79 likes. The topics were mainly centered on general dentistry (66%), with radiology (18%), orthodontics (9%), prosthodontics (4%), and implants (3%). DISCERN and mGQS scores were higher for videos uploaded by healthcare professionals and educational content videos(P<0.05). DISCERN exhibited a strong correlation (0.75) with the video source and with JAMA (0.77). The correlation of the video's content and mGQS, was 0.66 indicated moderate correlation.

CONCLUSION

YouTube™ has informative and moderately reliable videos on AI in dentistry. Dental students, dentists and patients can use these videos to learn and educate about artificial intelligence in dentistry. Professionals should upload more videos to enhance the reliability of the content.

A Pilot Investigation Into the Use of Teledentistry and Artificial Intelligence to Assess Dental Erosion in Competitive Swimmers

OBJECTIVE

The aim of the study was to assess the prevalence of dental erosion in competitive swimmers using teledentistry and artificial intelligence.

MATERIALS AND METHODS

An opportunistic sample of 20 competitive swimmers was recruited. The participants reported that they carried out an average of 2.40 h of training per day, 4.45 days per week. Data gathering was carried out remotely. The subjects completed a digital questionnaire and uploaded three photos of their mouth. Intraoral photographs were analyzed using the "Intact-Tooth" application to assess dental erosion. A statistical analysis was carried out to verify a possible correlation between the collected data.

RESULTS

The average calculated Basic Erosive Wear Examination (BEWE) index was 13.95, and 11 subjects (55%) had a severe BEWE (≥ 14). More than 40% of the participants disagreed about having social issues related to tooth hypersensitivity. Considering only subjects with a severe total BEWE value, we have observed an indirect correlation between the degree of dental erosion and diet (Pearson coefficient r = -0.57), whereas a direct correlation was observed between dental erosion and age (r = 0.493) and between BEWE and weekly training hours (r = 0.217).

CONCLUSIONS

Because of their lifestyle, competitive swimmers can be considered at a higher risk of developing dental erosion. In this context, teledentistry and AI tools can be effectively used to intercept those at the highest risk and prevent the occurrence of conditions.

SUMMARY

Dental erosion assessment in competitive swimmers using teledentistry and AI.

Classification of periodontitis stage and grade using natural language processing techniques

Periodontitis is a complex and microbiome-related inflammatory condition impacting dental supporting tissues. Emphasizing the potential of Clinical Decision Support Systems (CDSS), this study aims to facilitate early diagnosis of periodontitis by extracting patients' information collected as dental charts and notes. We developed a CDSS to predict the stage and grade of periodontitis using natural language processing (NLP) techniques including bidirectional encoder representation for transformers (BERT). We compared the performance of BERT with that of a baseline feature-engineered model. A secondary data analysis was conducted using 309 anonymized patient periodontal charts and corresponding clinician's notes obtained from the university periodontal clinic. After data preprocessing, we added a classification layer on top of the pre-trained BERT model to classify the clinical notes into their corresponding stage and grades. Then, we fine-tuned the pre-trained BERT model on 70% of our data. The performance of the model was evaluated on 32 unseen new patients' clinical notes. The results were compared with the output of a baseline feature-engineered algorithm coupled with MLP techniques to classify the stage and grade of periodontitis. Our proposed BERT model predicted the patients' stage and grade with 77% and 75% accuracy, respectively. MLP model showed that the accuracy of correct classification of stage and grade of the periodontitis on a set of 32 new unseen data was 59.4% and 62.5%, respectively. The BERT model could predict the periodontitis stage and grade on the same new dataset with higher accuracy (66% and 72%, respectively). The utilization of BERT in this context represents a groundbreaking application in dentistry, particularly in CDSS. Our BERT model outperformed baseline models, even with reduced information, promising efficient review of patient notes. This integration of advanced NLP techniques with CDSS frameworks holds potential for timely interventions, preventing complications and reducing healthcare costs.

Artificial Neural Networks: A New Frontier in Dental Tissue Regeneration

In the realm of dental tissue regeneration research, various constraints exist such as the potential variance in cell quality, potency arising from differences in donor tissue and tissue microenvironment, the difficulties associated with sustaining long-term and large-scale cell expansion while preserving stemness and therapeutic attributes, as well as the need for extensive investigation into the enduring safety and effectiveness in clinical settings. The adoption of artificial intelligence (AI) technologies has been suggested as a means to tackle these challenges. This is because, tissue regeneration research could be advanced through the use of diagnostic systems that incorporate mining methods such as neural networks (NN), fuzzy, predictive modeling, genetic algorithms, machine learning (ML), cluster analysis, and decision trees. This article seeks to offer foundational insights into a subset of AI referred to as artificial neural networks (ANNs) and assess their potential applications as essential decision-making support tools in the field of dentistry, with a particular focus on tissue engineering research. Although ANNs may initially appear complex and resource intensive, they have proven to be effective in laboratory and therapeutic settings. This expert system can be trained using clinical data alone, enabling their deployment in situations where rule-based decision-making is impractical. As ANNs progress further, it is likely to play a significant role in revolutionizing dental tissue regeneration research, providing promising results in streamlining dental procedures and improving patient outcomes in the clinical setting.

2D Facial Analysis Performed by a Human Operator Versus an Artificial Intelligence Software

BACKGROUND

Artificial intelligence (AI) has been a contribution in recent years to the development of new tools for dental, surgical, and esthetic treatment. In the case of image diagnosis, AI allows automated analysis of some facial parameters. The aim of this study was to evaluate the precision and reproducibility of these IA analyses compared with a human operator.

METHODS

Thirty subjects were selected, and frontal and profile photographs were taken of each. The photographs were previously calibrated and then analyzed by a human operator and an AI for vertical, horizontal, symmetry parameters, and facial profile angles.

RESULTS

Statistically significant differences were found in at least 1 vertical, horizontal, symmetry, and facial profile measurement.

CONCLUSIONS

The 2D facial analysis performed on photographs using AI is an interesting and constantly evolving tool. However, there are still many differences with the analysis performed by the human operator, so it should be used carefully. Further studies using metric measurements should be performed to contrast the values obtained by human operators and AI and to analyze the accuracy of point selection in facial soft tissues.

Automatic feature segmentation in dental panoramic radiographs

OBJECTIVE

The purpose of the present study was to verify the diagnostic performance of an AI system for the automatic detection of teeth, caries, implants, restorations, and fixed prosthesis on panoramic radiography.

METHODS

This is a cross-sectional study. A dataset comprising 1000 panoramic radiographs collected from 500 adult patients was analyzed by an AI system and compared with annotations provided by two oral and maxillofacial radiologists.

RESULTS

A strong correlation (R > 0.5) was observed between AI perception and observers 1 and 2 in carious teeth (0.691-0.878), implants (0.770-0.952), restored teeth (0.773-0.834), teeth with fixed prostheses (0.972-0.980), and missing teeth (0.956-0.988).

DISCUSSION

Panoramic radiographs are commonly used for diagnosis and treatment planning. However, they often suffer from artifacts, distortions, and superimpositions, leading to potential misinterpretations. Thus, an automated detection system is required to tackle these challenges. Artificial intelligence (AI) has revolutionized various fields, including dentistry, by enabling the development of intelligent systems that can assist in complex tasks such as diagnosis and treatment planning.

CONCLUSION

The automatic detection by the AI system was comparable to oral radiologists and may be useful for automatic identifications in panoramic radiographs. These findings signify the potential for AI systems to enhance diagnostic accuracy and efficiency in dental practices, potentially reducing the likelihood of diagnostic errors caused by unexperienced professionals.

Deep learning in the overall process of implant prosthodontics: A state-of-the-art review

Artificial intelligence represented by deep learning has attracted attention in the field of dental implant restoration. It is widely used in surgical image analysis, implant plan design, prosthesis shape design, and prognosis judgment. This article mainly describes the research progress of deep learning in the whole process of dental implant prosthodontics. It analyzes the limitations of current research, and looks forward to the future development direction.

Evaluation of Attitudes and Perceptions in Students About the Use of Artificial Intelligence in Craniomaxillofacial Surgery

Developments in technology have created great changes in the field of medicine and dentistry. Artificial intelligence technology is one of the most important innovations that caused this change. This study aimed to evaluate the opinions of dentistry students regarding the use of artificial intelligence in dentistry and craniomaxillofacial surgery. Two hundred ninety-six dentistry students between the ages of 19 and 30 participated in the study. Participants submitted the survey by e-mail examining the student's opinions and attitudes regarding the use of artificial intelligence in dentistry and craniomaxillofacial surgery. Respondents' anonymity was ensured. 47.30% (n: 140) of the students participating in the study are fourth-year students, and 52.70% (n: 156) are fifth-year students. While 48.98% (n: 145) of the participants have knowledge about the uses of artificial intelligence in daily life, 28.37% (n: 84) of the students have knowledge about robotic surgery. While ~74% of the participants think that artificial intelligence will improve the field of dentistry and craniomaxillofacial surgery, it has been observed that they are not worried about these applications replacing dentists in the future. It was determined that there was no statistically significant difference between fourth-year and fifth-year students in their knowledge levels about the areas of use of artificial intelligence (P=0.548). Students' opinions show that 74% agree that artificial intelligence will lead to major advances in the field of dentistry and craniomaxillofacial surgery. This shows the relationship between dentists and artificial intelligence points to a bright future.

Impact of Artificial Intelligence in Endodontics: Precision, Predictions, and Prospects

Artificial intelligence (AI) has become increasingly prevalent and significant across many industries, including the dental field. AI has shown accuracy and precision in detecting, evaluating, and predicting diseases. It can imitate human intelligence to carry out sophisticated predictions and decision-making in the health-care industry, especially in endodontics. AI models have demonstrated a wide range of applications in the field of endodontics. These include examining the anatomy of the root canal system, predicting the survival of dental pulp stem cells, gauging working lengths, identifying per apical lesions and root fractures, and predicting the outcome of retreatment treatments. Future uses of this technology were discussed in terms of robotic endodontic surgery, drug-drug interactions, patient care, scheduling, and prognostic diagnosis.

The future of medicine: an outline attempt using state-of-the-art business and scientific trends

Introduction

Currently, there is a lot of discussion about the future of medicine. From research and development to regulatory approval and access to patients until the withdrawal of a medicinal product from the market, there have been many challenges and a lot of barriers to overcome. In parallel, the business environment changes rapidly. So, the big question is how the pharma ecosystem will evolve in the future.

Methods

The current literature about the latest business and scientific evolutions and trends was reviewed.

Results

In the business environment, vast changes have taken place via the development of the internet as well as the Internet of Things. A new approach to production has emerged in a frame called Creative Commons; producer and consumer may be gradually identified in the context of the same process. As technology rapidly evolves, it is dominated by Artificial Intelligence (AI), its subset, Machine Learning, and the use of Big Data and Real-World Data (RWD) to produce Real-World Evidence (RWE). Nanotechnology is an inter-science field that gives new opportunities for the manufacturing of devices and products that have dimensions of a billionth of a meter. Artificial Neural Networks and Deep Learning (DL) are mimicking the use of the human brain, combining computer science with new theoretical foundations for complex systems. The implementation of these evolutions has already been initiated in the medicinal products' lifecycle, including screening of drug candidates, clinical trials, pharmacovigilance (PV), marketing authorization, manufacturing, and the supply chain. This has emerged as a new ecosystem which features characteristics such as free online tools and free data available online. Personalized medicine is a breakthrough field where tailor-made therapeutic solutions can be provided customized to the genome of each patient.

Conclusion

Various interactions take place as the pharma ecosystem and technology rapidly evolve. This can lead to better, safer, and more effective treatments that are developed faster and with a more solid, data-driven and evidence-concrete approach, which will drive the benefit for the patient.

Revolutionizing Dentistry: The Applications of Artificial Intelligence in Dental Health Care

Artificial intelligence (AI) is transforming the landscape of health care, and dentistry is no exception. This article explores the various applications of AI in dentistry, showcasing how this advanced technology is revolutionizing diagnosis, treatment, and patient care. From enabling early detection of oral diseases to enhancing the precision of dental procedures, AI is driving the industry toward more efficient and effective dental healthcare services. This article delves into the specific ways in which AI is being integrated into dental practices, highlighting its potential to improve patient outcomes and advance the field of dentistry.

Artificial Intelligence Used for Diagnosis in Facial Deformities: A Systematic Review

AI is included in a lot of different systems. In facial surgery, there are some AI-based software programs oriented to diagnosis in facial surgery. This study aims to evaluate the capacity and training of models for diagnosis of dentofacial deformities in class II and class III patients using artificial intelligence and the potential use for indicating orthognathic surgery. The search strategy is from 1943 to April 2024 in PubMed, Embase, Scopus, Lilacs, and Web of Science. Studies that used imaging to assess anatomical structures, airway volume, and craniofacial positions using the AI algorithm in the human population were included. The methodological quality of the studies was assessed using the Effective Public Health Practice Project instrument. The systematic search identified 697 articles. Eight studies were obtained for descriptive analysis after exclusion according to our inclusion and exclusion criteria. All studies were retrospective in design. A total of 5552 subjects with an age range between 14.7 and 56 years were obtained; 2474 (44.56%) subjects were male, and 3078 (55.43%) were female. Six studies were analyzed using 2D imaging and obtained highly accurate results in diagnosing skeletal features and determining the need for orthognathic surgery, and two studies used 3D imaging for measurement and diagnosis. Limitations of the studies such as age, diagnosis in facial deformity, and the included variables were observed. Concerning the overall analysis bias, six studies were at moderate risk due to weak study designs, while two were at high risk of bias. We can conclude that, with the few articles included, using AI-based software allows for some craniometric recognition and measurements to determine the diagnosis of facial deformities using mainly 2D analysis. However, it is necessary to perform studies based on three-dimensional images, increase the sample size, and train models in different populations to ensure accuracy of AI applications in this field. After that, the models can be trained for dentofacial diagnosis.

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.

Global trends and hotspots of ChatGPT in medical research: a bibliometric and visualized study

Objective

With the rapid advancement of Chat Generative Pre-Trained Transformer (ChatGPT) in medical research, our study aimed to identify global trends and focal points in this domain.

Method

All publications on ChatGPT in medical research were retrieved from the Web of Science Core Collection (WoSCC) by Clarivate Analytics from January 1, 2023, to January 31, 2024. The research trends and focal points were visualized and analyzed using VOSviewer and CiteSpace.

Results

A total of 1,239 publications were collected and analyzed. The USA contributed the largest number of publications (458, 37.145%) with the highest total citation frequencies (2,461) and the largest H-index. Harvard University contributed the highest number of publications (33) among all full-time institutions. The Cureus Journal of Medical Science published the most ChatGPT-related research (127, 10.30%). Additionally, Wiwanitkit V contributed the majority of publications in this field (20). "Artificial Intelligence (AI) and Machine Learning (ML)," "Education and Training," "Healthcare Applications," and "Data Analysis and Technology" emerged as the primary clusters of keywords. These areas are predicted to remain hotspots in future research in this field.

Conclusion

Overall, this study signifies the interdisciplinary nature of ChatGPT research in medicine, encompassing AI and ML technologies, education and training initiatives, diverse healthcare applications, and data analysis and technology advancements. These areas are expected to remain at the forefront of future research, driving continued innovation and progress in the field of ChatGPT in medical research.

Dental education and practice: past, present, and future trends

This position paper explores the historical transitions and current trends in dental education and practice and attempts to predict the future. Dental education and practice landscape, especially after the COVID-19 epidemic, are at a crossroads. Four fundamental forces are shaping the future: the escalating cost of education, the laicization of dental care, the corporatization of dental care, and technological advances. Dental education will likely include individualized, competency-based, asynchronous, hybrid, face-to-face, and virtual education with different start and end points for students. Dental practice, similarly, will be hybrid, with both face-to-face and virtual opportunities for patient care. Artificial intelligence will drive efficiencies in diagnosis, treatment, and office management.

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.

Role of Artificial Intelligence (AI) in Dentistry: A Literature Review

Artificial intelligence (AI) refers to the ability of machines to execute tasks that traditionally require human intelligence. Healthcare AI is evolving and has a highly promising future. The main uses in dentistry are patient management, diagnosis and treatment planning, and administrative tasks. Consequently, this AI system facilitates the acquisition of familiarity with this technology for every dentist, as it possesses the potential to synergistically complement forthcoming revolutionary advancements in the field of dentistry. This review aims to explore and analyze the several applications of AI within the field of dentistry.

Applying machine learning to assess the morphology of sculpted teeth

Background/purpose

Producing tooth crowns through dental technology is a basic function of dentistry. The morphology of tooth crowns is the most important parameter for evaluating its acceptability. The procedures were divided into four steps: tooth collection, scanning skills, use of mathematical methods and software, and machine learning calculation.

Materials and methods

Dental plaster rods were prepared. The effective data collected were to classify 121 teeth (15th tooth position), 342 teeth (16th tooth position), 69 teeth (21st tooth position), and 89 teeth (43rd tooth position), for a total of 621 teeth. The procedures are divided into four steps: tooth collection, scanning skills, use of mathematical methods and software, and machine learning calculation.

Results

The area under the curve (AUC) value was 0, 0.5, and 0.72 in this study. The precision rate and recall rate of micro-averaging/macro-averaging were 0.75/0.73 and 0.75/0.72. If we took a newly carved tooth picture into the program, the current effectiveness of machine learning was about 70%-75% to evaluate the quality of tooth morphology. Through the calculation and analysis of the two different concepts of micro-average/macro-average and AUC, similar values could be obtained.

Conclusion

This study established a set of procedures that can judge the quality of hand-carved plaster sticks and teeth, and the accuracy rate is about 70%-75%. It is expected that this process can be used to assist dental technicians in judging the pros and cons of hand-carved plaster sticks and teeth, so as to help dental technicians to learn the tooth morphology more effectively.

Convolutional neural networks for automated tooth numbering on panoramic radiographs: A scoping review

Purpose

The objective of this scoping review was to investigate the applicability and performance of various convolutional neural network (CNN) models in tooth numbering on panoramic radiographs, achieved through classification, detection, and segmentation tasks.

Material and Methods

An online search was performed of the PubMed, Science Direct, and Scopus databases. Based on the selection process, 12 studies were included in this review.

Results

Eleven studies utilized a CNN model for detection tasks, 5 for classification tasks, and 3 for segmentation tasks in the context of tooth numbering on panoramic radiographs. Most of these studies revealed high performance of various CNN models in automating tooth numbering. However, several studies also highlighted limitations of CNNs, such as the presence of false positives and false negatives in identifying decayed teeth, teeth with crown prosthetics, teeth adjacent to edentulous areas, dental implants, root remnants, wisdom teeth, and root canal-treated teeth. These limitations can be overcome by ensuring both the quality and quantity of datasets, as well as optimizing the CNN architecture.

Conclusion

CNNs have demonstrated high performance in automated tooth numbering on panoramic radiographs. Future development of CNN-based models for this purpose should also consider different stages of dentition, such as the primary and mixed dentition stages, as well as the presence of various tooth conditions. Ultimately, an optimized CNN architecture can serve as the foundation for an automated tooth numbering system and for further artificial intelligence research on panoramic radiographs for a variety of purposes.

Quantification of the apical palatal bone index for maxillary incisor immediate implant assessment: A retrospective cross-sectional study

BACKGROUND

Apical palatal bone is important in immediate implant evaluation. Current consensus gives qualitative suggestions regarding it, limiting its clinical decision-making value.

OBJECTIVES

To quantify the apical palatal bone dimension in maxillary incisors and reveal its quantitative correlation with other implant-related hard tissue indices to give practical advice for pre-immediate implant evaluation and design.

MATERIAL AND METHODS

A retrospective analysis of immediate implant-related hard tissue indices in maxillary incisors obtained by cone beam computed tomography (CBCT) was conducted. Palatal bone thickness at the apex level (Apical-P) on the sagittal section was selected as a parameter reflecting the apical palatal bone. Its quantitative correlation with other immediate implant-related hard tissue indices was revealed. Clinical advice of pre-immediate implant assessment was given based on the quantitative classification of Apical-P and its other correlated immediate implant-related hard tissue indices.

RESULTS

Apical-P positively correlated with cervical palatal bone, whole cervical buccal-palatal bone, sagittal root angle, and basal bone width indices. while negatively correlated with apical buccal bone, cervical buccal bone, and basal bone length indices. Six quantitative categories of Apical-P are proposed. Cases with Apical-P below 4 mm had an insufficient apical bone thickness to accommodate the implant placement, while Apical-P beyond 12 mm should be cautious about the severe implant inclination. Cases with Apical-P of 4-12 mm can generally achieve satisfying immediate implant outcomes via regulating the implant inclination.

CONCLUSIONS

Quantification of the apical palatal bone index for maxillary incisor immediate implant assessment can be achieved, providing a quantitative guide for immediate implant placement in the maxillary incisor zone.

Orthodontic Implementation of Machine Learning Algorithms for Predicting Some Linear Dental Arch Measurements and Preventing Anterior Segment Malocclusion: A Prospective Study

Background and Objectives: Orthodontics is a field that has seen significant advancements in recent years, with technology playing a crucial role in improving diagnosis and treatment planning. The study aimed to implement artificial intelligence to predict the arch width as a preventive measure to avoid future crowding in growing patients or even in adult patients seeking orthodontic treatment as a tool for orthodontic diagnosis. Materials and Methods: Four hundred and fifty intraoral scan (IOS) images were selected from orthodontic patients seeking treatment in private orthodontic centers. Real inter-canine, inter-premolar, and inter-molar widths were measured digitally. Two of the main machine learning models were used: the Python programming language and machine learning algorithms, implementing the data on k-nearest neighbor and linear regression. Results: After the dataset had been implemented on the two ML algorithms, linear regression and k-nearest neighbor, the evaluation metric shows that KNN gives better prediction accuracy than LR does. The resulting accuracy was around 99%. Conclusions: it is possible to leverage machine learning to enhance orthodontic diagnosis and treatment planning by predicting linear dental arch measurements and preventing anterior segment malocclusion.

Revolutionizing Dental Care: A Comprehensive Review of Artificial Intelligence Applications Among Various Dental Specialties

Since the beginning of recorded history, the human brain has been one of the most intriguing structures for scientists and engineers. Over the centuries, newer technologies have been developed based on principles that seek to mimic their functioning, but the creation of a machine that can think and behave like a human remains an unattainable fantasy. This idea is now known as "artificial intelligence". Dentistry has begun to experience the effects of artificial intelligence (AI). These include image enhancement for radiology, which improves the visibility of dental structures and facilitates disease diagnosis. AI has also been utilized for the identification of periapical lesions and root anatomy in endodontics, as well as for the diagnosis of periodontitis. This review is intended to provide a comprehensive overview of the use of AI in modern dentistry's numerous specialties. The relevant publications published between March 1987 and July 2023 were identified through an exhaustive search. Studies published in English were selected and included data regarding AI applications among various dental specialties. Dental practice involves more than just disease diagnosis, including correlation with clinical findings and administering treatment to patients. AI cannot replace dentists. However, a comprehensive understanding of AI concepts and techniques will be advantageous in the future. AI models for dental applications are currently being developed.

Artificial Intelligence Techniques for Automatic Detection of Peri-implant Marginal Bone Remodeling in Intraoral Radiographs

Peri-implantitis can cause marginal bone remodeling around implants. The aim is to develop an automatic image processing approach based on two artificial intelligence (AI) techniques in intraoral (periapical and bitewing) radiographs to assist dentists in determining bone loss. The first is a deep learning (DL) object-detector (YOLOv3) to roughly identify (no exact localization is required) two objects: prosthesis (crown) and implant (screw). The second is an image understanding-based (IU) process to fine-tune lines on screw edges and to identify significant points (intensity bone changes, intersections between screw and crown). Distances between these points are used to compute bone loss. A total of 2920 radiographs were used for training (50%) and testing (50%) the DL process. The mAP@0.5 metric is used for performance evaluation of DL considering periapical/bitewing and screws/crowns in upper and lower jaws, with scores ranging from 0.537 to 0.898 (sufficient because DL only needs an approximation). The IU performance is assessed with 50% of the testing radiographs through the t test statistical method, obtaining p values of 0.0106 (line fitting) and 0.0213 (significant point detection). The IU performance is satisfactory, as these values are in accordance with the statistical average/standard deviation in pixels for line fitting (2.75/1.01) and for significant point detection (2.63/1.28) according to the expert criteria of dentists, who establish the ground-truth lines and significant points. In conclusion, AI methods have good prospects for automatic bone loss detection in intraoral radiographs to assist dental specialists in diagnosing peri-implantitis.

A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration

In the field of orthodontics, providing patients with accurate treatment time estimates is of utmost importance. As orthodontic practices continue to evolve and embrace new advancements, incorporating machine learning (ML) methods becomes increasingly valuable in improving orthodontic diagnosis and treatment planning. This study aimed to develop a novel ML model capable of predicting the orthodontic treatment duration based on essential pre-treatment variables. Patients who completed comprehensive orthodontic treatment at the Indiana University School of Dentistry were included in this retrospective study. Fifty-seven pre-treatment variables were collected and used to train and test nine different ML models. The performance of each model was assessed using descriptive statistics, intraclass correlation coefficients, and one-way analysis of variance tests. Random Forest, Lasso, and Elastic Net were found to be the most accurate, with a mean absolute error of 7.27 months in predicting treatment duration. Extraction decision, COVID, intermaxillary relationship, lower incisor position, and additional appliances were identified as important predictors of treatment duration. Overall, this study demonstrates the potential of ML in predicting orthodontic treatment duration using pre-treatment variables.

Artificial Intelligence for Image Analysis in Oral Squamous Cell Carcinoma: A Review

Head and neck tumor differential diagnosis and prognosis have always been a challenge for oral pathologists due to their similarities and complexity. Artificial intelligence novel applications can function as an auxiliary tool for the objective interpretation of histomorphological digital slides. In this review, we present digital histopathological image analysis applications in oral squamous cell carcinoma. A literature search was performed in PubMed MEDLINE with the following keywords: "artificial intelligence" OR "deep learning" OR "machine learning" AND "oral squamous cell carcinoma". Artificial intelligence has proven to be a helpful tool in histopathological image analysis of tumors and other lesions, even though it is necessary to continue researching in this area, mainly for clinical validation.

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.

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues.

Patients' perspectives on the use of artificial intelligence in dentistry: a regional survey

The use of artificial intelligence (AI) in dentistry is rapidly evolving and could play a major role in a variety of dental fields. This study assessed patients' perceptions and expectations regarding AI use in dentistry. An 18-item questionnaire survey focused on demographics, expectancy, accountability, trust, interaction, advantages and disadvantages was responded to by 330 patients; 265 completed questionnaires were included in this study. Frequencies and differences between age groups were analysed using a two-sided chi-squared or Fisher's exact tests with Monte Carlo approximation. Patients' perceived top three disadvantages of AI use in dentistry were (1) the impact on workforce needs (37.7%), (2) new challenges on doctor-patient relationships (36.2%) and (3) increased dental care costs (31.7%). Major expected advantages were improved diagnostic confidence (60.8%), time reduction (48.3%) and more personalised and evidencebased disease management (43.0%). Most patients expected AI to be part of the dental workflow in 1-5 (42.3%) or 5-10 (46.8%) years. Older patients (> 35 years) expected higher AI performance standards than younger patients (18-35 years) (p < 0.05). Overall, patients showed a positive attitude towards AI in dentistry. Understanding patients' perceptions may allow professionals to shape AI-driven dentistry in the future.

Smart Smile: Revolutionizing Dentistry With Artificial Intelligence

Artificial intelligence (AI) has emerged as a transformative technology in various industries, and its potential in dentistry is gaining significant attention. This abstract explores the future prospects of AI in dentistry, highlighting its potential to revolutionize clinical practice, improve patient outcomes, and enhance the overall efficiency of dental care. The application of AI in dentistry encompasses several key areas, including diagnosis, treatment planning, image analysis, patient management, and personalized care. AI algorithms have shown promising results in the automated detection and diagnosis of dental conditions, such as caries, periodontal diseases, and oral cancers, aiding clinicians in early intervention and improving treatment outcomes. Furthermore, AI-powered treatment planning systems leverage machine learning techniques to analyze vast amounts of patient data, considering factors like medical history, anatomical variations, and treatment success rates. These systems provide dentists with valuable insights and support in making evidence-based treatment decisions, ultimately leading to more predictable and tailored treatment approaches. While the potential of AI in dentistry is immense, it is essential to address certain challenges, including data privacy, algorithm bias, and regulatory considerations. Collaborative efforts between dental professionals, AI experts, and policymakers are crucial to developing robust frameworks that ensure the responsible and ethical implementation of AI in dentistry. Moreover, AI-driven robotics has introduced innovative approaches to dental surgery, enabling precise and minimally invasive procedures, and ultimately reducing patient discomfort and recovery time. Virtual reality (VR) and augmented reality (AR) applications further enhance dental education and training, allowing dental professionals to refine their skills in a realistic and immersive environment. AI holds tremendous promise in shaping the future of dentistry. Through its ability to analyze vast amounts of data, provide accurate diagnoses, facilitate treatment planning, improve image analysis, streamline patient management, and enable personalized care, AI has the potential to enhance dental practice and significantly improve patient outcomes. Embracing this technology and its future development will undoubtedly revolutionize the field of dentistry, fostering a more efficient, precise, and patient-centric approach to oral healthcare. Overall, AI represents a powerful tool that has the potential to revolutionize various aspects of society, from improving healthcare outcomes to optimizing business operations. Continued research, development, and responsible implementation of AI technologies will shape our future, unlocking new possibilities and transforming the way we live and work.

ChatGPT in Dentistry: A Comprehensive Review

Chat generative pre-trained transformer (ChatGPT) is an artificial intelligence chatbot that uses natural language processing that can respond to human input in a conversational manner. ChatGPT has numerous applications in the health care system including dentistry; it is used in diagnoses and for assessing disease risk and scheduling appointments. It also has a role in scientific research. In the dental field, it has provided many benefits such as detecting dental and maxillofacial abnormalities on panoramic radiographs and identifying different dental restorations. Therefore, it helps in decreasing the workload. But even with these benefits, one should take into consideration the risks and limitations of this chatbot. Few articles mentioned the use of ChatGPT in dentistry. This comprehensive review represents data collected from 66 relevant articles using PubMed and Google Scholar as databases. This review aims to discuss all relevant published articles on the use of ChatGPT in dentistry.

Prediction of the as Low as Diagnostically Acceptable CT Dose for Identification of the Inferior Alveolar Canal Using 3D Convolutional Neural Networks with Multi-Balancing Strategies

Ionizing radiation is necessary for diagnostic imaging and deciding the right radiation dose is extremely critical to obtain a decent quality image. However, increasing the dosage to improve the image quality has risks due to the potential harm from ionizing radiation. Thus, finding the optimal as low as diagnostically acceptable (ALADA) dosage is an open research problem that has yet to be tackled using artificial intelligence (AI) methods. This paper proposes a new multi-balancing 3D convolutional neural network methodology to build 3D multidetector computed tomography (MDCT) datasets and develop a 3D classifier model that can work properly with 3D CT scan images and balance itself over the heavy unbalanced multi-classes. The proposed models were exhaustively investigated through eighteen empirical experiments and three re-runs for clinical expert examination. As a result, it was possible to confirm that the proposed models improved the performance by an accuracy of 5% to 10% when compared to the baseline method. Furthermore, the resulting models were found to be consistent, and thus possibly applicable to different MDCT examinations and reconstruction techniques. The outcome of this paper can help radiologists to predict the suitability of CT dosages across different CT hardware devices and reconstruction algorithms. Moreover, the developed model is suitable for clinical application where the right dose needs to be predicted from numerous MDCT examinations using a certain MDCT device and reconstruction technique.

Artificial intelligence applications and ethical challenges in oral and maxillo-facial cosmetic surgery: a narrative review

Artificial intelligence (AI) refers to using technologies to simulate human cognition to solve a specific problem. The rapid development of AI in the health sector has been attributed to the improvement of computing speed, exponential increase in data production, and routine data collection. In this paper, we review the current applications of AI for oral and maxillofacial (OMF) cosmetic surgery to provide surgeons with the fundamental technical elements needed to understand its potential. AI plays an increasingly important role in OMF cosmetic surgery in various settings, and its usage may raise ethical issues. In addition to machine learning algorithms (a subtype of AI), convolutional neural networks (a subtype of deep learning) are widely used in OMF cosmetic surgeries. Depending on their complexity, these networks can extract and process the elementary characteristics of an image. They are, therefore, commonly used in the diagnostic process for medical images and facial photos. AI algorithms have been used to assist surgeons with diagnosis, therapeutic decisions, preoperative planning, and outcome prediction and evaluation. AI algorithms complement human skills while minimizing shortcomings through their capabilities to learn, classify, predict, and detect. This algorithm should, however, be rigorously evaluated clinically, and a systematic ethical reflection should be conducted regarding data protection, diversity, and transparency. It is possible to revolutionize the practice of functional and aesthetic surgeries with 3D simulation models and AI models. Planning, decision-making, and evaluation during and after surgery can be improved with simulation systems. A surgical AI model can also perform time-consuming or challenging tasks for surgeons.

Artificial Intelligence Systems Assisting in the Assessment of the Course and Retention of Orthodontic Treatment

This scoping review examines the contemporary applications of advanced artificial intelligence (AI) software in orthodontics, focusing on its potential to improve daily working protocols, but also highlighting its limitations. The aim of the review was to evaluate the accuracy and efficiency of current AI-based systems compared to conventional methods in diagnosing, assessing the progress of patients' treatment and follow-up stability. The researchers used various online databases and identified diagnostic software and dental monitoring software as the most studied software in contemporary orthodontics. The former can accurately identify anatomical landmarks used for cephalometric analysis, while the latter enables orthodontists to thoroughly monitor each patient, determine specific desired outcomes, track progress, and warn of potential changes in pre-existing pathology. However, there is limited evidence to assess the stability of treatment outcomes and relapse detection. The study concludes that AI is an effective tool for managing orthodontic treatment from diagnosis to retention, benefiting both patients and clinicians. Patients find the software easy to use and feel better cared for, while clinicians can make diagnoses more easily and assess compliance and damage to braces or aligners more quickly and frequently.

Progression of Selected Parameters of the Clinical Profile of Patients with Periodontitis Using Kohonen's Self-Organizing Maps

(1) Background: Periodontitis is an inflammatory condition that affects the tissues surrounding the tooth and causes clinical attachment loss, which is the loss of periodontal attachment (CAL). Periodontitis can advance in various ways, with some patients experiencing severe periodontitis in a short period of time while others may experience mild periodontitis for the rest of their lives. In this study, we have used an alternative methodology to conventional statistics, self-organizing maps (SOM), to group the clinical profiles of patients with periodontitis. (2) Methods: To predict the periodontitis progression and to choose the best treatment plan, we can use artificial intelligence, more precisely Kohonen's self-organizing maps (SOM). In this study, 110 patients, both genders, between the ages of 30 and 60, were included in this retrospective analysis. (3) Results: To discover the pattern of patients according to the periodontitis grade and stage, we grouped the neurons together to form three clusters: Group 1 was made up of neurons 12 and 16 that represented a percentage of slow progression of almost 75%; Group 2 was made up of neurons 3, 4, 6, 7, 11, and 14 in which the percentage of moderate progression was almost 65%; and Group 3 was made up of neurons 1, 2, 5, 8, 9, 10, 13, and 15 that represented a percentage of rapid progression of almost 60%. There were statistically significant differences in the approximate plaque index (API), and bleeding on probing (BoP) versus groups (p < 0.0001). The post-hoc tests showed that API, BoP, pocket depth (PD), and CAL values were significantly lower in Group 1 relative to Group 2 (p < 0.05) and Group 3 (p < 0.05). A detailed statistical analysis showed that the PD value was significantly lower in Group 1 relative to Group 2 (p = 0.0001). Furthermore, the PD was significantly higher in Group 3 relative to Group 2 (p = 0.0068). There was a statistically significant CAL difference between Group 1 relative to Group 2 (p = 0.0370). (4) Conclusions: Self-organizing maps, in contrast to conventional statistics, allow us to view the issue of periodontitis advancement by illuminating how the variables are organized in one or the other of the various suppositions.

A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs

The study aims to evaluate the diagnostic performance of an artificial intelligence system based on deep learning for the segmentation of occlusal, proximal and cervical caries lesions on panoramic radiographs. The study included 504 anonymous panoramic radiographs obtained from the radiology archive of Inonu University Faculty of Dentistry's Department of Oral and Maxillofacial Radiology from January 2018 to January 2020. This study proposes Dental Caries Detection Network (DCDNet) architecture for dental caries segmentation. The main difference between DCDNet and other segmentation architecture is that the last part of DCDNet contains a Multi-Predicted Output (MPO) structure. In MPO, the final feature map split into three different paths for detecting occlusal, proximal and cervical caries. Extensive experimental analyses were executed to analyze the DCDNet network architecture performance. In these comparison results, while the proposed model achieved an average F1-score of 62.79%, the highest average F1-score of 15.69% was achieved with the state-of-the-art segmentation models. These results show that the proposed artificial intelligence-based model can be one of the indispensable auxiliary tools of dentists in the diagnosis and treatment planning of carious lesions by enabling their detection in different locations with high success.

FROM TEXT TO DIAGNOSE: CHATGPT'S EFFICACY IN MEDICAL DECISION-MAKING

OBJECTIVE

The aim: Evaluate the diagnostic capabilities of the ChatGPT in the field of medical diagnosis.

PATIENTS AND METHODS

Materials and methods: We utilized 50 clinical cases, employing Large Language Model ChatGPT-3.5. The experiment had three phases, each with a new chat setup. In the initial phase, ChatGPT received detailed clinical case descriptions, guided by a "Persona Pattern" prompt. In the second phase, cases with diagnostic errors were addressed by providing potential diagnoses for ChatGPT to choose from. The final phase assessed artificial intelligence's ability to mimic a medical practitioner's diagnostic process, with prompts limiting initial information to symptoms and history.

RESULTS

Results: In the initial phase, ChatGPT showed a 66.00% diagnostic accuracy, surpassing physicians by nearly 50%. Notably, in 11 cases requiring image inter¬pretation, ChatGPT struggled initially but achieved a correct diagnosis for four without added interpretations. In the second phase, ChatGPT demonstrated a remarkable 70.59% diagnostic accuracy, while physicians averaged 41.47%. Furthermore, the overall accuracy of Large Language Model in first and second phases together was 90.00%. In the third phase emulating real doctor decision-making, ChatGPT achieved a 46.00% success rate.

CONCLUSION

Conclusions: Our research underscores ChatGPT's strong potential in clinical medicine as a diagnostic tool, especially in structured scenarios. It emphasizes the need for supplementary data and the complexity of medical diagnosis. This contributes valuable insights to AI-driven clinical diagnostics, with a nod to the importance of prompt engineering techniques in ChatGPT's interaction with doctors.

Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives

Artificial intelligence has been widely used in the field of dentistry in recent years. The present study highlights current advances and limitations in integrating artificial intelligence, machine learning, and deep learning in subfields of dentistry including periodontology, endodontics, orthodontics, restorative dentistry, and oral pathology. This article aims to provide a systematic review of current clinical applications of artificial intelligence within different fields of dentistry. The preferred reporting items for systematic reviews (PRISMA) statement was used as a formal guideline for data collection. Data was obtained from research studies for 2009-2022. The analysis included a total of 55 papers from Google Scholar, IEEE, PubMed, and Scopus databases. Results show that artificial intelligence has the potential to improve dental care, disease diagnosis and prognosis, treatment planning, and risk assessment. Finally, this study highlights the limitations of the analyzed studies and provides future directions to improve dental care.