Smarter Smiles: A Review of AI Applications in Orthodontics

The term artificial intelligence (AI) refers to a variety of tools and methods that let computers carry out operations that have historically required human intelligence. These include the study of robotics, machine vision, natural language processing, heavy learning, and machine learning (ML).

AI systems in dentistry, healthcare and medicine can swiftly and precisely analyze large volumes of data, assisting physicians in more accurately and early disease detection.

These can help diagnose diseases like cancer, heart disease, and neurological disorders by spotting patterns in test findings, patient records, and medical images that would not be obvious to human observers.

Like other areas of healthcare, AI in dentistry, can support a relatively straightforward array of activities, like cavity detection, periodontal disease diagnosis, or orthodontic treatment planning.

Orthodontics is an ideal place for AI in dentistry to provide support because the research suggests that AI applications in orthodontics may be the most promising as it involves imaging and cephalometric analysis.

 As the number of Dental AI development tools, such as WeDoCeph, WebCeph, and CephX, continues to grow, AI-based diagnostic support is now accessible to practitioners and researchers.

History of AI in Orthodontics

In the early days, orthodontists used computers mainly for viewing X-rays or planning basic treatments. Around the 2000s, new tools began showing up that could study patterns in teeth and jaw development. Slowly, these systems got better at helping doctors plan braces and aligners. What started as simple support has grown into a trusted part of modern orthodontic care.

Why Orthodontics Is Ideal for AI Integration?

Traditional methods are often time-intensive, susceptible to human interpretation   errors, and heavily reliant on individual clinician expertise.

This poses a significant challenge as orthodontic cases present with increasing complexity, demanding more efficient, accurate, and standardized diagnostic support to optimize treatment planning and patient outcomes.

Limitations of Conventional Orthodontic Techniques

Time intensive methodologies, such as manual models and cephalometric tracing, can contribute to delays with treatment planning and can deplete clinic resources, in addition to having measurable impacts for manual measurement and human interpretation errors that influence the accuracy of diagnosis.

Rising Challenges in Orthodontic Cases

More difficult malocclusion cases that contain multiple dental and skeletal errors demand more progressive and deeper diagnostic methods than is capable in traditional two-dimensional views, as well as subjective clinical evaluations.

Demand for Diagnostic Consistency

With the variety of experience and associated opinions within traditional orthodontic evaluations, instances of clinician subjective bias lead to diverging diagnoses, outcomes and treatment plans amongst clinicians. There is a call for improved and standardized methods for reliable and prognostically consistent patient care.

Potential of AI adoption:

The potential of artificial intelligence is to help enhance therapeutic efficiencies, effectiveness, and objective evaluation within orthodontic diagnostic methods is promising, nonetheless there are challenges with integrating them into existing clinical practice.

Pitfalls of AI Adoption

Possible barriers include the existing evidence to support reliability of such newly developed methods, and to safeguard sensitive patient data, all preceding obligation to patient safety with AI driven recommendations, and broader ethical issues regarding autonomous diagnostic decisions.

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Automating Cephalometric Tracing with Intelligent Systems:

1. Digital software offers speed and repeatability gains over traditional cephalometric analysis, which is laborious and subject to human mistake.

 2. AI has proven to be highly accurate in recognizing cephalometric landmarks, frequently  matching or outperforming the dependability of seasoned human readers while also drastically cutting down on analysis time.

3. While lateral radiography is still widely used, AI's ability to automatically identify and analyse landmarks is rekindling interest in CBCT for CA. 

4. Although findings are threshold-dependent, meta analyses indicate that AI can identify  landmarks with promising accuracy, and new developments in deep learning are increasing the accuracy of annotations.

Smarter Skeletal Age Assessment with AI

1. Accurately assessing skeletal age is crucial to plan orthodontic treatments with growth spurts.
2.  Wrist X-rays and CVM are more accurate markers of age than chronological age. Using these techniques, AI has demonstrated diagnostic accuracy in determining skeletal age.
3.  Nevertheless, research indicates that the accuracy of CVM-based AI models varies when compared to human observers, raising questions about their dependability. While some CNN-based models demonstrate high accuracy in CVM assessment, discrepancies have been noted, particularly around peak growth periods.

TMJ Assessments using Artificial Intelligence

1. Temporomandibular joint osteoarthritis (TMJOA) can lead to considerable pain, functional issues, and misalignment of the teeth. A radiographic exam can confirm its existence, with MRI being the preferred method for assessing the disc.
2. Recent research shows that AI in dentistry has a strong ability to detect and stage TMJOA through various imaging techniques.
3. Meta-analyses reveal that AI models have moderate to good accuracy in identifying TMJOA on panoramic radiographs, with even better accuracy found in more extensive studies that look at multiple imaging methods and masticatory muscle disorders.
4. The expected integration of AI in TMJ diagnostic imaging is likely to enhance future research focused on early detection and tailored treatment approaches for osteoarthritis.

Enhancing Extraction Strategies Using AI Technology

1. Figuring out whether to extract teeth during orthodontic treatment is no easy task. It involves a lot of different factors related to both the patient and the orthodontist, which can result in varying opinions among professionals.
2. To help navigate this decision, AI tools have been introduced, and early studies suggest they often match expert decisions quite well, with accuracy rates often surpassing 80% in predicting extraction needs and even in outlining specific extraction patterns.
3. However, it’s worth mentioning that these studies come with limitations, such as training the AI on data from a small pool of experts and not factoring in some critical dental findings.

Precision Orthognathic Surgery Planning Powered by AI

1. There are no clear standards for orthognathic surgery, particularly in cases that are borderline. Using lateral cephalograms and even photos of faces, AI and machine learning algorithms have shown a high degree of accuracy (above 90%) in identifying surgical cases.
2. AI in dental treatment planning is also being investigated in promising research, including automated diagnosis, risk classification, and highly sensitive and specific surgical simulations.
3. Systematic reviews highlight the heterogeneity of current studies, suggesting AI could be a valuable tool but emphasizing the need for further, more robust research for generalizable applications.

CONCLUSION

In orthodontics, the application of artificial intelligence technology advances the field and   facilitates the analysis of imaging phenotypes in patients in need of orthopedic, orthodontic, and/or surgical therapy. This covers automated techniques for quantification, classification, and characterisation, standard orientation, landmark recognition, picture anonymization, segmentation, and registration. Confirming the clinical effectiveness of the automated toolkit procedures for quantitative image analysis and making these methods available and adaptable to the larger orthodontic field are the long-term goals of the AI tools development.

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