Artificial intelligence in revolutionizing orthodontic practice

Abstract

This analytical research paper explores the transformative impact of artificial intelligence (AI) in orthodontics, with a focus on its objectives: Identifying current applications, evaluating benefits, addressing challenges, and projecting future developments. AI, a subset of computer science designed to simulate human intelligence, has seen rapid integration into orthodontic practice. The paper examines AI technologies such as machine learning, deep learning, natural language processing, computer vision, and robotics, which are increasingly used to analyze patient data, assist with diagnosis and treatment planning, automate routine tasks, and improve patient communication. AI systems offer precise malocclusion diagnoses, predict treatment outcomes, and customize treatment plans by leveraging dental imagery. They also streamline image analysis, improve diagnostic accuracy, and enhance patient engagement through personalized communication. The objectives include evaluating the benefits of AI in terms of efficiency, accuracy, and personalized care, while acknowledging the challenges like data quality, algorithm transparency, and practical implementation. Despite these hurdles, AI presents promising prospects in advanced imaging, predictive analytics, and clinical decision-making. In conclusion, AI holds the potential to revolutionize orthodontic practices by improving operational efficiency, diagnostic precision and patient outcomes. With collaborative efforts to overcome challenges, AI could play a pivotal role in advancing orthodontic care.

Key Words: Artificial intelligence; Orthodontics; Machine learning; Deep learning; Diagnosis; Treatment planning; Patient management; Efficiency; Accuracy; Personalized treatment; Challenges; Future directions

Core Tip: This paper explores the future of orthodontics, highlighting the integration of artificial intelligence (AI) and digital technologies. AI is becoming crucial in treatment planning, yet clinicians remain essential in decision-making. The growing role of 3D digital technologies in orthodontics reflects AI's increasing influence, but ethical and legal challenges persist. Emphasizing the need for clinical trials, the study calls for further exploration of AI's potential to transform traditional orthodontic practices.

INTRODUCTION

In recent years, the field of orthodontics has seen significant technological advancements, particularly in artificial intelligence (AI). AI, a branch of computer science that enables machines to simulate human intelligence, has become a powerful tool with the potential to transform various aspects of orthodontic practice. This emerging field allows computers to perform tasks previously done by humans. Though still in its early stages, AI is becoming integral to many sectors, including healthcare, where researchers are incorporating it to enhance patient care. Each dental specialty stands to benefit from AI through improved precision of care, more accurate diagnoses, and time savings in both clinical and administrative tasks. While the digital shift in the profession is well underway, it is crucial for clinicians to rigorously assess the reliability and accuracy of each AI system being introduced[1]. This article examines the current state of AI in orthodontics, its applications, benefits, challenges, and prospects.

OVERVIEW OF AI

AI encompasses a range of technologies and techniques that enable computers to perform tasks that traditionally require human intelligence[2]. These include machine learning (ML), deep learning, natural language processing, computer vision, and robotics. In orthodontics, AI systems leverage these technologies to analyze patient data, assist in diagnosis and treatment planning, automate tasks, and improve patient communication and management. Machine intelligence functions like machines. It adheres to the fundamental hierarchy of machines: Input, processing, and output. Input data can be voice data (handheld sounds), text data (medical or processing records, experimental parameters), or picture data (X-ray images, photos). The neural networks process this input data and provide an output. The result can be a prognosis, diagnosis, treatment, or disease prediction. In orthodontics, two of the previous options could be beneficial: Diagnosis and treatment planning (Figure 1).

Figure 1 The role of artificial intelligence in orthodontics.

APPLICATIONS OF AI IN ORTHODONTICS

ML in orthodontics

Various approaches can be employed to analyze different types of orthodontic records, leading to accurate and accelerated diagnoses. By directly engaging with patient records, these results can be used to develop effective treatment plans. ML and deep learning techniques have transformed medical diagnostics, including orthodontics. By utilizing vast datasets and sophisticated algorithms, these methods can swiftly and accurately analyze orthodontic records such as dental images, patient histories, and treatment outcomes. This leads to more precise diagnoses, enabling orthodontists to create treatment plans that are better tailored to each patient's needs.

Additionally, integrating ML into orthodontic practice can significantly speed up the diagnostic process. Traditional methods often involve manual examination of patient records and subjective interpretation by clinicians, which can be time-consuming and susceptible to human error. By contrast, ML algorithms can quickly process large volumes of data, identifying patterns and anomalies that may not be immediately noticeable to human observers. This expedited diagnostic process allows for faster treatment initiation, ultimately improving patient outcomes and satisfaction.

The synergy between ML systems and patient records supports a personalized approach to orthodontic treatment planning. By incorporating individual patient data, such as medical history, genetic factors, and treatment preferences, into the analysis, clinicians can develop customized treatment plans that address specific patient needs and goals[2].

Diagnosis and treatment planning

AI algorithms can analyze patient records, including dental images such as X-rays, intraoral scans, and photographs, to assist orthodontists in diagnosing malocclusions, predicting treatment outcomes, and designing personalized treatment plans[3]. For instance, AI systems can accurately detect anomalies in dental images, measure tooth and bone structures, and identify risk factors for orthodontic issues.

Imaging and analysis

AI-powered software can automatically segment dental images, extract relevant features, and analyze them to detect abnormalities and track treatment progress[4]. This automation streamlines the diagnostic process, reduces errors, and allows orthodontists to focus more on patient care. Additionally, AI algorithms can enhance imaging techniques such as cone-beam computed tomography (CBCT) and magnetic resonance imaging (MRI), leading to improved visualization and analysis of dental structures.

Image analysis using AI can be divided into four categories: Image filtering and knowledge-based landmark search, model-based approaches, soft-computing and model-based approaches, and hybrid approaches. Many web-based applications are available for clinicians to perform image analysis via AI, including CephX (Herzliya, Israel), WebCeph (Gyeonggi-do, Republic of Korea), Dolphin Imaging (Los Angeles, CA, United States), and AudaxCeph (Ljubljana, Slovenia).

For example, WebCeph (Gyeonggi-do, Republic of Korea) is a fully automated web-based platform powered by AI that can perform nine different cephalometric analyses and provide interpretations based on the obtained cephalometric measurements. It can store and preserve digital cephalograms, orthopantomograms, and patient photographs. Its features, such as simulation and visual treatment overlays, are highly beneficial in daily orthodontic practice.

AI can perform various tasks, such as prediction and classification, using different algorithms. Hatice et al[5] aimed to determine cervical vertebral stages (CVS) for growth and development periods using six commonly used AI classifiers and compared their performances. The classifiers are k-nearest neighbors (k-NN), Naive Bayes (NB), decision tree (Tree), artificial neural networks (ANNs), support vector machine (SVM), random forest (RF), and logistic regression (LR). Among these algorithms, k-NN and LR had the lowest precision values, while SVM, RF, Tree, and NB had variable precision values. ANNs might be the preferred method for determining CVS.

Patient management and communication

The traditional method of acquiring orthodontic images, which involves manual classification, archiving, and monitoring, is time-consuming and prone to errors due to fatigue. With advancements in digital dentistry, imaging data is increasingly being indexed and stored in digital archives, allowing for easy retrieval for diagnostic, treatment, and subsequent monitoring purposes. Developing an efficient AI tool for the automated classification and monitoring of orthodontic images would be highly beneficial. AI-driven tools, such as chatbots and virtual assistants, can provide patients with personalized treatment information, appointment reminders, and post-treatment care instructions[6]. These digital tools enhance patient engagement, satisfaction, and adherence to treatment plans. Additionally, AI systems can analyze patient feedback and preferences to optimize treatment protocols and improve the overall patient experience.

Deep learning, a branch of ML, excels in analyzing high-dimensional data such as texts and images. In computer vision, deep learning has replaced many traditional ML tasks, including classification, segmentation, and detection. In orthodontics, studies have begun applying deep learning for diagnosis, screening, and decision-making[7,8].

The performance of the DeepID model was thoroughly evaluated through external tests and compared with orthodontists. The findings demonstrated that deep learning methods could automatically classify, archive, and monitor orthodontic images with greater accuracy and speed than manual methods[9] (Figure 2).

Figure 2 Deep learning workflow for orthodontic image analysis.

Dental monitoring (DM, Paris, France) is a new orthodontic application that combines teledentistry with AI using a knowledge-based algorithm, enabling precise semi-automatic treatment monitoring. It is the world's first Software as a Service (commonly referred to as SaaS) application designed for remote dental care monitoring, allowing orthodontists to remotely monitor patients' treatments using intraoral photos taken by patients with their smartphones and a special ScanBox. An AI-powered system in DM performs a preliminary assessment of data from intraoral images, partially automating communication between the doctor, staff, and patient. Unlike other dental applications, it provides validated real-time information through the knowledge-based algorithm.

During orthodontic treatment with aligners, remote monitoring should be performed at each aligner change. This allows clinicians to assess fit, ensure the adaptability of the aligners, check the presence or integrity of attachments, maintain elastic buttons, and monitor the integrity of the teeth and aligners. The DM system provides follow-up information to the patient based on a preliminary analysis conducted by AI. For aligner treatment, immediately after the intraoral scan, the DM application informs the patient whether they can proceed to switch to the next aligner and start using it (indicated by "GO") or if they should continue using the current one (indicated by "No Go"). If a "No Go" signal is given, a new intraoral scan may be required after a few days.

BENEFITS OF AI IN ORTHODONTICS

Improved efficiency

AI technologies automate repetitive tasks, such as image analysis and data processing, allowing orthodontists to work more efficiently and focus on complex cases[10]. This leads to faster diagnosis, treatment planning, and decision-making, ultimately reducing treatment times and enhancing patient outcomes.

Enhanced accuracy

AI algorithms can analyze large volumes of patient data with greater speed and accuracy than human professionals[11]. This allows orthodontists to make more informed decisions, avoid diagnostic errors, and achieve optimal treatment outcomes. Furthermore, AI systems can learn from past cases, continuously enhancing their diagnostic and predictive capabilities over time.

Personalized treatment

By analyzing patient-specific data, such as dental anatomy, facial characteristics, and treatment history, AI systems can tailor specific treatment plans to individual patient needs and preferences[12]. This individualized approach improves treatment outcomes, patient satisfaction, and long-term oral health.

Dental extractions

When considering dental extractions as part of orthodontic treatment, AI systems can analyze cases and guide orthodontists in selecting specific treatment plans. In orthodontic treatment planning, various factors affect the decision to extract teeth, such as systemic diseases, remaining growth, and patient's primary complaints. Space analysis results should not be the sole criterion for extraction but remain a priority when addressing crowding issues. For conditions such as bimaxillary protrusion, profile improvement, orthognathic surgery planning, and aesthetic considerations, extraction may be necessary based on established norms for dentomaxillary discrepancy. Orthodontists develop treatment plans based on clinical evidence, personal experience, and potential biases from past treatments. These plans are influenced by patient history, personal philosophy, and aesthetic standards. In some cases, treatment decisions can rely solely on intra-oral photographs, which provide sufficient information for experienced clinicians. However, determining the need for extractions from clinical photos poses a limitation for AI, which relies on accumulated data for training (ML).

Two AI models have been developed using photographic input data for different purposes: Landmark detection models with crowding categorization capabilities and diagnostic models for orthodontic extractions. Factors beyond crowding may also influence extraction decisions, necessitating advanced AI algorithms to improve their generalizability and support clinical decision-making effectively[13].

Aligners and maxillary distalization

AI algorithms play a crucial role in the planning and staging of aligners. A key consideration in aligner preparation is dividing the desired dental movements into logical stages. Specialized AI algorithms typically handle this automatic staging by sequentially dividing the planned movements. Another important biomechanical consideration during aligner treatment is anchorage. For instance, when distalizing premolars and molars followed by retracting anterior teeth, maintaining anchorage and preventing lingual tipping of incisors are critical. This requires a sequential movement of teeth: First molars are distalized, followed by premolars, canines, and finally incisors. Some aligner treatment software incorporates AI algorithms that automatically manage this sequential dental movement as part of aligner treatment planning.

Clinical decision support systems

AI-powered clinical decision support systems aid orthodontists in making real-time decisions by providing evidence-based recommendations, treatment guidelines, and risk assessments[14]. These systems analyze patient data, scientific literature, and clinical guidelines to offer personalized treatment suggestions and enhance clinical decision-making.

To enhance orthodontic care, clinicians must utilize ML and AI tools to analyze relationships among dentition, craniofacial skeleton, and soft tissues. This knowledge can then be applied to advance orthodontic diagnosis, treatment planning, growth and development assessment, progress evaluation, treatment outcomes, and stability assessment.

Currently, companies manufacturing aligners utilize digital dental model data and AI algorithms to predict and plan tooth movement and perform tooth segmentation. However, clinicians should exercise caution when relying on predictions from these AI algorithms and when monitoring treatment outcomes. Moreover, these technological advancements require integrating multiple sources, including clinical information, CBCT, digital dental models, photographs, cephalograms, and panoramic images.

CHALLENGES AND LIMITATIONS OF AI IN ORTHODONTICS

Data quality and bias

AI algorithms rely on extensive and varied datasets for training to achieve reliable performance[15]. Nevertheless, the quality and representativeness of available data can vary, potentially introducing biases and inaccuracies into AI models. Moreover, ethical considerations such as data privacy and regulatory compliance are essential to ensure the responsible use of patient data in AI applications.

Interpretability and transparency

AI systems frequently function as "black boxes", which can pose challenges for orthodontists in comprehending how specific diagnoses or treatment recommendations are generated[16]. Improving the interpretability and transparency of AI algorithms is crucial to building trust and gaining acceptance among clinicians and patients.

Integration and adoption

Successfully integrating AI into orthodontic practice involves overcoming technical, organizational, and cultural hurdles[17]. Orthodontists need sufficient training to effectively utilize AI technologies, and practice workflows may require redesigning to incorporate AI-driven processes. Moreover, the initial high costs of implementing AI systems and the ongoing need for maintenance and updates can present adoption challenges, especially in smaller practices and resource-limited environments.

FUTURE DIRECTIONS AND OPPORTUNITIES

Despite the challenges, the future of AI in orthodontics is promising, with numerous opportunities for further research, development, and innovation. Some potential areas for future exploration include.

Advanced imaging techniques

AI algorithms have the potential to augment the capabilities of established imaging modalities like CBCT, MRI, and 3D scanning, offering comprehensive anatomical details and enhancing diagnostic precision[18]. Furthermore, AI-powered image reconstruction and enhancement algorithms can optimize image quality and minimize radiation exposure, thereby benefiting both clinicians and patients.

Predictive analytics

AI systems have the capability to analyze longitudinal patient data, enabling prediction of treatment outcomes, anticipation of complications, and optimization of treatment protocols[19]. With ML and predictive analytics techniques, orthodontists can discern patterns and trends in patient responses to treatment, facilitating the development of personalized intervention strategies aimed at enhancing outcomes.

CONCLUSION

The future of orthodontics will increasingly integrate digital technologies and AI, fostering a balance between technological advancements and the indispensable role of human intelligence and creativity. As AI continues to evolve, it will play a critical role in supporting digital treatment planning through the incorporation of computational geometry, biomechanics, 3D visualization, and enhanced human-machine interaction. AI algorithms are already becoming central to the field of digital orthodontics, influencing nearly every aspect of patient analysis, diagnosis, and treatment planning. However, while AI promises to significantly enhance the precision, efficiency, and customization of orthodontic practices, it will not replace the expertise and judgment of clinicians in the foreseeable future. The growing prominence of 3D digital technologies reflects AI's increasing role, but the responsibility for final health decisions will still rest with clinicians. As AI becomes more prevalent, the ethical and legal considerations surrounding its use in medical settings, including orthodontics, will demand greater attention, especially as medical responsibility and patient safety remain paramount. In addition, the legal recognition of AI in clinical practice will be crucial for its integration into mainstream orthodontics. To truly transform traditional orthodontic treatment approaches, more clinical trials focused on AI applications are essential. These trials will help ensure that AI’s potential is fully realized while maintaining the highest standards of patient care.