Orthodontics in the era of big data analytics

The Implications of Artificial Intelligence in Pedodontics: A Scoping Review of Evidence-Based Literature

BACKGROUND

Artificial intelligence (AI) has emerged as a revolutionary technology with several applications across different dental fields, including pedodontics. This systematic review has the objective to catalog and explore the various uses of artificial intelligence in pediatric dentistry.

METHODS

A thorough exploration of scientific databases was carried out to identify studies addressing the usage of AI in pediatric dentistry until December 2023 in the Embase, Scopus, PubMed, and Web of Science databases by two researchers, S.L.R. and A.L.G.

RESULTS

From a pool of 1301 articles, only 64 met the predefined criteria and were considered for inclusion in this review. From the data retrieved, it was possible to provide a narrative discussion of the potential implications of AI in the specialized area of pediatric dentistry. The use of AI algorithms and machine learning techniques has shown promising results in several applications of daily dental pediatric practice, including the following: (1) assisting the diagnostic and recognizing processes of early signs of dental pathologies, (2) enhancing orthodontic diagnosis by automating cephalometric tracing and estimating growth and development, (3) assisting and educating children to develop appropriate behavior for dental hygiene.

CONCLUSION

AI holds significant potential in transforming clinical practice, improving patient outcomes, and elevating the standards of care in pediatric patients. Future directions may involve developing cloud-based platforms for data integration and sharing, leveraging large datasets for improved predictive results, and expanding AI applications for the pediatric population.

Artificial intelligence for treatment planning and soft tissue outcome prediction of orthognathic treatment: A systematic review

BACKGROUND

The accuracy of artificial intelligence (AI) in treatment planning and outcome prediction in orthognathic treatment (OGT) has not been systematically reviewed.

OBJECTIVES

To determine the accuracy of AI in treatment planning and soft tissue outcome prediction in OGT.

DESIGN

Systematic review.

DATA SOURCES

Unrestricted search of indexed databases and reference lists of included studies.

DATA SELECTION

Clinical studies that addressed the focused question 'Is AI useful for treatment planning and soft tissue outcome prediction in OGT?' were included.

DATA EXTRACTION

Study screening, selection and data extraction were performed independently by two authors. The risk of bias (RoB) was assessed using the Cochrane Collaboration's RoB and ROBINS-I tools for randomised and non-randomised clinical studies, respectively.

DATA SYNTHESIS

Eight clinical studies (seven retrospective cohort studies and one randomised controlled study) were included. Four studies assessed the role of AI for treatment decision making; and four studies assessed the accuracy of AI in soft tissue outcome prediction after OGT. In four studies, the level of agreement between AI and non-AI decision making was found to be clinically acceptable (at least 90%). In four studies, it was shown that AI can be used for soft tissue outcome prediction after OGT; however, predictions were not clinically acceptable for the lip and chin areas. All studies had a low to moderate RoB.

LIMITATIONS

Due to high methodological inconsistencies among the included studies, it was not possible to conduct a meta-analysis and reporting biases assessment.

CONCLUSION

AI can be a useful aid to traditional treatment planning by facilitating clinical treatment decision making and providing a visualisation tool for soft tissue outcome prediction in OGT.

REGISTRATION

PROSPERO CRD42022366864.

Blockchain technology and federated machine learning for collaborative initiatives in orthodontics and craniofacial health

There is a paucity of largescale collaborative initiatives in orthodontics and craniofacial health. Such nationally representative projects would yield findings that are generalizable. The lack of large-scale collaborative initiatives in the field of orthodontics creates a deficiency in study outcomes that can be applied to the population at large. The objective of this study is to provide a narrative review of potential applications of blockchain technology and federated machine learning to improve collaborative care. We conducted a narrative review of articles published from 2018 to 2023 to provide a high level overview of blockchain technology, federated machine learning, remote monitoring, and genomics and how they can be leveraged together to establish a patient centered model of care. To strengthen the empirical framework for clinical decision making in healthcare, we suggest use of blockchain technology and integrating it with federated machine learning. There are several challenges to adoption of these technologies in the current healthcare ecosystem. Nevertheless, this may be an ideal time to explore how best we can integrate these technologies to deliver high quality personalized care. This article provides an overview of blockchain technology and federated machine learning and how they can be leveraged to initiate collaborative projects that will have the patient at the center of care.

Call for algorithmic fairness to mitigate amplification of racial biases in artificial intelligence models used in orthodontics and craniofacial health

Machine Learning (ML), a subfield of Artificial Intelligence (AI), is being increasingly used in Orthodontics and craniofacial health for predicting clinical outcomes. Current ML/AI models are prone to accentuate racial disparities. The objective of this narrative review is to provide an overview of how AI/ML models perpetuate racial biases and how we can mitigate this situation. A narrative review of articles published in the medical literature on racial biases and the use of AI/ML models was undertaken. Current AI/ML models are built on homogenous clinical datasets that have a gross underrepresentation of historically disadvantages demographic groups, especially the ethno-racial minorities. The consequence of such AI/ML models is that they perform poorly when deployed on ethno-racial minorities thus further amplifying racial biases. Healthcare providers, policymakers, AI developers and all stakeholders should pay close attention to various steps in the pipeline of building AI/ML models and every effort must be made to establish algorithmic fairness to redress inequities.

Artificial intelligence in orthodontics and orthognathic surgery: a bibliometric analysis of the 100 most-cited articles

BACKGROUND

The application of artificial intelligence (AI) in orthodontics and orthognathic surgery has gained significant attention in recent years. However, there is a lack of bibliometric reports that analyze the academic literature in this field to identify publishing and citation trends. By conducting an analysis of the top 100 most-cited articles on AI in orthodontics and orthognathic surgery, we aim to unveil popular research topics, key authors, institutions, countries, and journals in this area.

METHODS

A comprehensive search was conducted in the Web of Science (WOS) electronic database to identify the top 100 most-cited articles on AI in orthodontics and orthognathic surgery. Publication and citation data were obtained and further analyzed and visualized using R Biblioshiny. The key domains of the 100 articles were also identified.

RESULTS

The top 100 most-cited articles were published between 2005 and 2022, contributed by 458 authors, with an average citation count of 22.09. South Korea emerged as the leading contributor with the highest number of publications (28) and citations (595), followed by China (16, 373), and the United States (7, 248). Notably, six South Korean authors ranked among the top 10 contributors, and three South Korean institutions were listed as the most productive. International collaborations were predominantly observed between the United States, China, and South Korea. The main domains of the articles focused on automated imaging assessment (42%), aiding diagnosis and treatment planning (34%), and the assessment of growth and development (10%). Besides, a positive correlation was observed between the testing sample size and citation counts (P = 0.010), as well as between the time of publication and citation counts (P < 0.001).

CONCLUSIONS

The utilization of AI in orthodontics and orthognathic surgery has shown remarkable progress, particularly in the domains of imaging analysis, diagnosis and treatment planning, and growth and development assessment. This bibliometric analysis provides valuable insights into the top-cited articles and the trends of AI research in this field.

Stress and displacement patterns during orthodontic intervention in the maxilla of patients with cleft palate analyzed by finite element analysis: a systematic review

OBJECTIVE

Rationale for the review in the context of what is already known about the evaluation of stress and displacement patterns using finite element analysis in the maxilla of patients with cleft palate after orthodontic intervention.

METHODS

This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). The protocol for this systematic review was registered with PROSPERO (CRD42020177494). The following databases were screened: Medline (via PubMed), Scopus, Embase, and Web of Science.

RESULTS

The search identified 31 records. 15 articles were retrieved for full texts and 11 of them were considered eligible for inclusion by 2 authors. Eventually, 11 articles were included in the qualitative analysis.

CONCLUSIONS

Finite element analysis is an appropriate tool for studying and predicting force application points for better controlled expansion in patients with UCLP.

L. Shayeb M. Future of Orthodontics-A Systematic Review and Meta-Analysis on the Emerging Trends in This Field

Technology is rapidly evolving in the modern world, and the accompanying developments due to its influence are shaping each and every aspect of our life, with the field of orthodontics being no exception. This systematic review and meta-analysis aimed to examine such trends in orthodontics and hypothesize which ones would emerge and continue in the near future. After a thorough search of online journals using keywords such as "3D printing," "Aligners," "Artificial intelligence," "Future trends," "Orthodontics," and "Teleorthodontics" across databases of PubMed-MEDLINE, Web of Science, Cochrane, and Scopus, a total of 634 papers were initially recovered. Technological advancements in 3D printing, Computer-aided design and Computer-aided manufacturing (CAD/CAM), biopolymers and Teleorthodontics were the most important categories of development seen across the 17 studies that we selected for our review. All the investigations selected for this systematic review depicted aspects of orthodontics that were influenced by rapid technological changes and could potentially become mainstream in the coming times. However, caution was sought to be observed in the usage/adoption of some of these trends, with social media usage amongst both patients as well as orthodontists being a prime example of this.

Decoding Deep Learning applications for diagnosis and treatment planning

INTRODUCTION

Artificial Intelligence (AI), Machine Learning and Deep Learning are playing an increasingly significant role in the medical field in the 21st century. These recent technologies are based on the concept of creating machines that have the potential to function as a human brain. It necessitates the gathering of large quantity of data to be processed. Once processed with AI machines, these data have the potential to streamline and improve the capabilities of the medical field in diagnosis and treatment planning, as well as in the prediction and recognition of diseases. These concepts are new to Orthodontics and are currently limited to image processing and pattern recognition.

OBJECTIVE

This article exposes and describes the different methods by which orthodontics may benefit from a more widespread adoption of these technologies.

Transforming healthcare with big data analytics: technologies, techniques and prospects

In different studies in the field of healthcare, big data analytics technology has been shown to be effective in observing the behaviour of data, of which analysed to allow the discovery of relevant insights for strategy and decision making. The objective of this study is to present the results of a systematic review of the literature on big data analytics in healthcare, focussing in technologies, main areas and purposes of adoption. To reach its objective, the study conducts an exploratory research, through a systematic review of the literature, using the Methodi Ordinatio protocol supported by content analysis. The results reveal that the use of tools implies work performance at the clinical and managerial level, improving the cost-benefit ratio and reducing the time factor in the practice of the workforce in health services. Thus, this study hopes to contribute to the technological advancement of computational intelligence applied to healthcare.

Convolutional Neural Network Performance for Sella Turcica Segmentation and Classification Using CBCT Images

The present study aims to validate the diagnostic performance and evaluate the reliability of an artificial intelligence system based on the convolutional neural network method for the morphological classification of sella turcica in CBCT (cone-beam computed tomography) images. In this retrospective study, sella segmentation and classification models (CranioCatch, Eskisehir, Türkiye) were applied to sagittal slices of CBCT images, using PyTorch supported by U-Net and TensorFlow 1, and we implemented the GoogleNet Inception V3 algorithm. The AI models achieved successful results for sella turcica segmentation of CBCT images based on the deep learning models. The sensitivity, precision, and F-measure values were 1.0, 1.0, and 1.0, respectively, for segmentation of sella turcica in sagittal slices of CBCT images. The sensitivity, precision, accuracy, and F1-score were 1.0, 0.95, 0.98, and 0.84, respectively, for sella-turcica-flattened classification; 0.95, 0.83, 0.92, and 0.88, respectively, for sella-turcica-oval classification; 0.75, 0.94, 0.90, and 0.83, respectively, for sella-turcica-round classification. It is predicted that detecting anatomical landmarks with orthodontic importance, such as the sella point, with artificial intelligence algorithms will save time for orthodontists and facilitate diagnosis.

The Validity of Machine Learning Procedures in Orthodontics: What Is Still Missing?

Artificial intelligence (AI) models and procedures hold remarkable predictive efficiency in the medical domain through their ability to discover hidden, non-obvious clinical patterns in data. However, due to the sparsity, noise, and time-dependency of medical data, AI procedures are raising unprecedented issues related to the mismatch between doctors’ mentalreasoning and the statistical answers provided by algorithms. Electronic systems can reproduce or even amplify noise hidden in the data, especially when the diagnosis of the subjects in the training data set is inaccurate or incomplete. In this paper we describe the conditions that need to be met for AI instruments to be truly useful in the orthodontic domain. We report some examples of computational procedures that are capable of extracting orthodontic knowledge through ever deeper patient representation. To have confidence in these procedures, orthodontic practitioners should recognize the benefits, shortcomings, and unintended consequences of AI models, as algorithms that learn from human decisions likewise learn mistakes and biases.

Automatic Classification for Sagittal Craniofacial Patterns Based on Different Convolutional Neural Networks

(1) Background: The present study aims to evaluate and compare the model performances of different convolutional neural networks (CNNs) used for classifying sagittal skeletal patterns. (2) Methods: A total of 2432 lateral cephalometric radiographs were collected. They were labeled as Class I, Class II, and Class III patterns, according to their ANB angles and Wits values. The radiographs were randomly divided into the training, validation, and test sets in the ratio of 70%:15%:15%. Four different CNNs, namely VGG16, GoogLeNet, ResNet152, and DenseNet161, were trained, and their model performances were compared. (3) Results: The accuracy of the four CNNs was ranked as follows: DenseNet161 > ResNet152 > VGG16 > GoogLeNet. DenseNet161 had the highest accuracy, while GoogLeNet possessed the smallest model size and fastest inference speed. The CNNs showed better capabilities for identifying Class III patterns, followed by Classes II and I. Most of the samples that were misclassified by the CNNs were boundary cases. The activation area confirmed the CNNs without overfitting and indicated that artificial intelligence could recognize the compensatory dental features in the anterior region of the jaws and lips. (4) Conclusions: CNNs can quickly and effectively assist orthodontists in the diagnosis of sagittal skeletal classification patterns.

Artificial intelligence in orthodontics: Where are we now? A scoping review

OBJECTIVE

This scoping review aims to determine the applications of Artificial Intelligence (AI) that are extensively employed in the field of Orthodontics, to evaluate its benefits, and to discuss its potential implications in this speciality. Recent decades have witnessed enormous changes in our profession. The arrival of new and more aesthetic options in orthodontic treatment, the transition to a fully digital workflow, the emergence of temporary anchorage devices and new imaging methods all provide both patients and professionals with a new focus in orthodontic care.

MATERIALS AND METHODS

This review was performed following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. The electronic literature search was performed through MEDLINE/PubMed, Scopus, Web of Science, Cochrane and IEEE Xplore databases with a 11-year time restriction: January 2010 till March 2021. No additional manual searches were performed.

RESULTS

The electronic literature search initially returned 311 records, and 115 after removing duplicate references. Finally, the application of the inclusion criteria resulted in 17 eligible publications in the qualitative synthesis review.

CONCLUSION

The analysed studies demonstrated that Convolution Neural Networks can be used for the automatic detection of anatomical reference points on radiological images. In the growth and development research area, the Cervical Vertebral Maturation stage can be determined using an Artificial Neural Network model and obtain the same results as expert human observers. AI technology can also improve the diagnostic accuracy for orthodontic treatments, thereby helping the orthodontist work more accurately and efficiently.

Clinical decision support systems in orthodontics: A narrative review of data science approaches

Advancements in technology and data collection generated immense amounts of information from various sources such as health records, clinical examination, imaging, medical devices, as well as experimental and biological data. Proper management and analysis of these data via high-end computing solutions, artificial intelligence and machine learning approaches can assist in extracting meaningful information that enhances population health and well-being. Furthermore, the extracted knowledge can provide new avenues for modern healthcare delivery via clinical decision support systems. This manuscript presents a narrative review of data science approaches for clinical decision support systems in orthodontics. We describe the fundamental components of data science approaches including (a) Data collection, storage and management; (b) Data processing; (c) In-depth data analysis; and (d) Data communication. Then, we introduce a web-based data management platform, the Data Storage for Computation and Integration, for temporomandibular joint and dental clinical decision support systems.

Smartphone applications used in orthodontics: A scoping review of scholarly literature

Mobile applications (apps) play an increasingly important role in day to day life. With the number of orthodontic-related apps continuing to increase, and the rapid development of artificial intelligence, the potential to yield tremendous benefits to both clinicians and patients is apparent. However, if orthodontic apps are to become mainstream and obtain greater acceptance, scientific validation and investigation of these apps are to be undertaken. This scoping review aimed to determine the scope and extent of the published literature on mobile apps in orthodontics, as well as identify the types of studies published, and summarize the outcomes studied- thus also giving direction for future research in a rapidly evolving subject area.

Dentronics: Towards robotics and artificial intelligence in dentistry

OBJECTIVES

This paper provides an overview of existing applications and concepts of robotic systems and artificial intelligence in dentistry. This review aims to provide the community with novel inputs and argues for an increased utilization of these recent technological developments, referred to as Dentronics, in order to advance dentistry.

METHODS

First, background on developments in robotics, artificial intelligence (AI) and machine learning (ML) are reviewed that may enable novel assistive applications in dentistry (Sec A). Second, a systematic technology review that evaluates existing state-of-the-art applications in AI, ML and robotics in the context of dentistry is presented (Sec B).

RESULTS

A systematic literature research in pubmed yielded in a total of 558 results. 41 studies related to ML, 53 studies related to AI and 49 original research papers on robotics application in dentistry were included. ML and AI have been applied in dental research to analyze large amounts of data to eventually support dental decision making, diagnosis, prognosis and treatment planning with the help of data-driven analysis algorithms based on machine learning. So far, only few robotic applications have made it to reality, mostly restricted to pilot use cases.

SIGNIFICANCE

The authors believe that dentistry can greatly benefit from the current rise of digital human-centered automation and be transformed towards a new robotic, ML and AI-enabled era. In the future, Dentronics will enhance reliability, reproducibility, accuracy and efficiency in dentistry through the democratized use of modern dental technologies, such as medical robot systems and specialized artificial intelligence. Dentronics will increase our understanding of disease pathogenesis, improve risk-assessment-strategies, diagnosis, disease prediction and finally lead to better treatment outcomes.

Radiomics and Machine Learning in Oral Healthcare

The increasing storage of information, data, and forms of knowledge has led to the development of new technologies that can help to accomplish complex tasks in different areas, such as in dentistry. In this context, the role of computational methods, such as radiomics and Artificial Intelligence (AI) applications, has been progressing remarkably for dentomaxillofacial radiology (DMFR). These tools bring new perspectives for diagnosis, classification, and prediction of oral diseases, treatment planning, and for the evaluation and prediction of outcomes, minimizing the possibilities of human errors. A comprehensive review of the state-of-the-art of using radiomics and machine learning (ML) for imaging in oral healthcare is presented in this paper. Although the number of published studies is still relatively low, the preliminary results are very promising and in a near future, an augmented dentomaxillofacial radiology (ADMFR) will combine the use of radiomics-based and AI-based analyses with the radiologist's evaluation. In addition to the opportunities and possibilities, some challenges and limitations have also been discussed for further investigations.

Comparison of actual porcine tooth crown development stages and computer image analysis

Tooth developmental stage evaluation is important in dental and chronological age estimation, and it is important for accurate diagnoses and appropriate treatment in dental practice. It is routinely assessed by clinical observations and radiographic techniques. This study aimed at ascertaining tooth developmental stage judgments made by examiners and Mimics software according to the Nolla method with radiographs. Meanwhile, the true tooth developmental stages would be explored with histological analysis. Twenty freshly slaughtered porcine heads were collected and hemisected, and both the left and right mandibular samples were numbered. The developmental stages of the second and third permanent molars (M2 and M3) were evaluated by examiners and Mimics software analysis. The ratio of the radiopaque calcified area to the dental follicle (RCA/DF) at different stages was calculated. Both non-decalcified and decalcified samples were processed for histologic observation. The results showed significant differences between RCA/DF ratios from different developmental stages. There was a high positive correlation between the examiners' evaluation results and Mimics analysis results. Radiograph judgments and histology observation results were consistent from Stages 2-6. However, radiograph images of Stage 1 samples showed only crypts present, while under a surgical operating microscope, a bell-shaped tooth germ was observed. This was also confirmed by normal and hard tissue histology. In conclusion, radiograph judgments made by either examiners or Mimics software were both reliable. Mimics analysis can be a useful tool in evaluating tooth developmental stages. However, judgments need to be made cautiously in early developmental stages.