The Role Of Machine Learning In Orthodontic Treatment Planning

Machine learning (ML) is a frequently used term in the context of AI. It is a feature of artificial intelligence that allows software applications to learn from different examples instead of a set of rules (provided by humans). Machine learning algorithms learn from data without being precisely programmed. Owing to its vast capacity, machine learning is being used in various dental specialties including oral radiography, endodontics, etc. However, the greatest benefit can be seen in the field of orthodontics.

How Does Machine Learning Work In Orthodontics?

In the development of AI programs, examples are fed into the computer. Specialized algorithms are generated that generalize specific patterns (from provided data) and predict outcomes. The information provided to the system is generally extracted from:

• Treatment plans
• Imaging data
• Patient profiles

Based on this data, machine learning lends a hand to orthodontists in identifying orthodontic characteristics, diagnosing cases, and predicting treatment results.

Benefits Of Employing Machine Learning In Orthodontics

Specialized AI algorithms (with the help of machine learning) generate mathematical models that are quick to generalize a certain pattern and predict precise decisions.

Enhanced Orthodontic Diagnosis

Machine learning works in conjunction with artificial neural networks to extract data from provided data, analyze it with algorithms, and reach a diagnosis. As machine learning prepares the algorithms after exposure to a plethora of information, the results are reliable.

A study showed that deep learning (a sub-domain of machine learning) with a convolutional neural network (CNN) has a high performance in image recognition. With machine learning, the important features are automatically learned. Manual landmark identification on lateral cephalometric X-rays is an important step in orthodontic diagnosis. The incorporation of machine learning can ease and speed up the process.

More and more orthodontists are now shifting towards 3D intraoral scans for hassle-free practice and more accurate results. Here again, machine learning can play a significant role. ML allows accurate automated landmark identification on 3D cephalometric views. Clinical studies reveal that 3D cephalometric landmark identification by deep reinforcement learning has high accuracy and can enable fast cephalometric analysis and consequent treatment planning.

In some cases, machine learning even seems to outperform experts in terms of diagnostic efficiency.

Tailored Treatment Plans

Orthodontics is a vast field where multiple large and small factors come into play. Better, customized treatment plans can be created with deep analysis of individual cases. Research shows that machine learning paired with artificial intelligence systems can accurately predict small yet useful factors (like unerupted teeth in an individual). This is attributed to preferable generalization and proper selection of data.

Machine learning models can serve to be valuable tools in decision-making and the provision of accurate and to-the-point treatment options. When compared with decisions made by expert orthodontics, the accuracy of machine learning predictive models was found to be 84%.

Moreover, machine learning algorithms can offer effective solutions in the treatment planning of denote-maxillofacial deformities. Such algorithms are known to improve diagnostic efficiency and surgical planning in orthodontic-orthognathic surgery patients. Reports suggest that machine learning and AI have high accuracy in diagnosis and prove to be reliable clinical decision support systems. Despite the high accuracy, there are still some shortcomings and therefore, machine learning systems should be used with caution.

Improved Prediction Of Features

Predicting growth and other factors (important for treatment planning) is essential as it can directly influence the treatment outcomes. “To extract or not extract” is a mind-boggling question for even seasoned orthodontists. Advanced machine learning is capable of identifying the ideal treatment strategy and lacing the orthodontist with this knowledge.

Supervised ML techniques yielded high accuracy in predicting upper and lower premolar (4’s) extractions. To determine the extraction pattern, the machine learning system used the following predictive indicators:

• Molar relationship
• Mandibular crowding
• Overjet
• Overbite

Machine learning does a great job of predicting growth based on skeletal landmarks and parameters (linear and angular). With the use of ML, there is high accuracy and minimal margin of error.

Pubertal mandibular growth is a marked development in orthodontic treatment. This is especially true for class 2 cases with mandibular deficiency. Studies show that ML techniques have the potential to accurately forecast mandibular growth in features. Thus, it can aid in devising the best-suited treatment strategy.

Efficient And Consistent Results

By taking into consideration and enhancing every aspect of orthodontic treatment machine learning can offer consistent results. With supervised learning of the landmarks and growth predictors, it offers a decision-support system that is efficient and consistent. Unlike humans, machines lack emotions and decision bias. Plus, the margin of error in AI-based measurements is very small. Thus, you get systematic and consistent treatment plans for problems from a machine learning AI system.

Can Be Improved

Teaching the latest data to professionals takes a lot of time and effort and may also mean deviation from practical work for some time. Experts suggest keeping your knowledge updated in this continually evolving pool of data. The good thing with machine learning models is that they can be easily updated and improved. The adaptability means that the treatment plans and predictions can stay relevant and accurate over time.

Final Word

Machine learning (ML) is a salient facet of artificial intelligence. It is combined with neural networks (convolutional and artificial) to form a strong tool in the field of orthodontics. ML has the capability to learn from examples instead of set rules. There are manifold benefits of machine learning in the field of orthodontics.

By accurately and quickly identifying landmarks on cephalometric X-rays and 3D scans, machine learning outperforms experts in diagnostic efficiency. With ML and its sub-domain i.e., deep learning, AI offers patient-tailored treatment plans. It can also predict features of treatment. Machine learning gives you accurate growth prediction and determines the ideal pattern of extractions/orthognathic surgery. Orthodontic treatment results based on machine learning are highly efficient and consistent. Plus, there is the option to easily update and improve this system.

References

1. Aminoshariae, A., Kulild, J., & Nagendrababu, V. (2021). Artificial intelligence in endodontics: current applications and future directions. Journal of endodontics, 47(9), 1352-1357.
2. Mohammad-Rahimi, H., Nadimi, M., Rohban, M. H., Shamsoddin, E., Lee, V. Y., & Motamedian, S. R. (2021). Machine learning and orthodontics, current trends and the future opportunities: A scoping review. American Journal of Orthodontics and Dentofacial Orthopedics, 160(2), 170-192.
3. Yasaka, K., Akai, H., Kunimatsu, A., Kiryu, S., & Abe, O. (2018). Deep learning with convolutional neural network in radiology. Japanese journal of radiology, 36, 257-272.
4. Kang, S. H., Jeon, K., Kang, S. H., & Lee, S. H. (2021). 3D cephalometric landmark detection by multiple stage deep reinforcement learning. Scientific reports, 11(1), 17509.
5. Ali, S. M., Saloom, H. F., & Tawfeeq, M. A. (2021). Artificial Neural Network for Prediction of Unerupted Premolars and Canines. Int. Med. J, 28, 74-79.
6. Prasad, J., Mallikarjunaiah, D. R., Shetty, A., Gandedkar, N., Chikkamuniswamy, A. B., & Shivashankar, P. C. (2022). Machine learning predictive model as clinical decision support system in orthodontic treatment planning. Dentistry Journal, 11(1), 1.
7. Du, W., Bi, W., Liu, Y., Zhu, Z., Tai, Y., & Luo, E. (2024). Machine learning-based decision support system for orthognathic diagnosis and treatment planning. BMC Oral Health, 24(1), 286.
8. Asiri, S. N., Tadlock, L. P., Schneiderman, E., & Buschang, P. H. (2020). Applications of artificial intelligence and machine learning in orthodontics. APOS Trends Orthod, 10(1), 17-24.
9. Leavitt, L., Volovic, J., Steinhauer, L., Mason, T., Eckert, G., Dean, J. A., … & Turkkahraman, H. (2023). Can we predict orthodontic extraction patterns by using machine learning?. Orthodontics & Craniofacial Research, 26(4), 552-559.
10. Kim, E., Kuroda, Y., Soeda, Y., Koizumi, S., & Yamaguchi, T. (2023). Validation of Machine Learning Models for Craniofacial Growth Prediction. Diagnostics, 13(21), 3369.
11. Zakhar, G., Hazime, S., Eckert, G., Wong, A., Badirli, S., & Turkkahraman, H. (2023). Prediction of Pubertal Mandibular Growth in Males with Class II Malocclusion by Utilizing Machine Learning. Diagnostics, 13(16), 2713.