AI In Orthodontic Diagnosis: Reducing Errors And Enhancing Precision

The implementation of artificial intelligence in the field of orthodontics has numerous benefits. From quickly providing a cephalometric report to remotely monitoring patients, AI helps both orthodontists and patients. A lot of dentists are now shifting to advanced computerized systems for diagnosis and treatment planning. This shift is attributed to flawless diagnosis and highly efficient treatment planning with AI. Orthodontists who have used AI algorithms are head over heels for the new technology. Many advocate the use of AI in orthodontics because it reduces diagnostic errors and enhances precision. So, let’s find out how AI is capable of keeping diagnostic errors low!

How Does AI Reduce Errors In Orthodontic Diagnosis And Enhance Precision?

An error-free diagnosis requires the fulfillment of multiple criteria. Accurate inputs in the form of clinical and radiographic data are a must for good diagnosis. The orthodontist needs to thoroughly analyze the situation, accurately highlight anatomical/clinical landmarks, and provide decisions that are equitable and based on facts. Artificial intelligence and machine learning help reduce error in the following ways:

Reduces Noise In Imaging

Unfortunately, noisy images are pretty common in the daily orthodontic practice. According to a study, a noisy medical image corrupts information that can potentially lead to misdiagnosis. Orthodontic diagnosis relies heavily on radiographs (cephalometric and panoramic). Thus, it is critical to get a clear picture of the teeth and jaws.

Anyone who has traced a cephalometric X-ray knows how tedious and laborious it is to carry out a cephalometric analysis. Artificial intelligence steps in to save your time and effort. It has shown promising results in dento-maxillofacial imaging. Most of the commercially available cephalometric systems are prepared to denoise an image and provide accurate analysis. It was revealed in a study that automated cephalometric analysis done by AI provides precise and accurate diagnosis by eliminating image noise.

Kicks Out Cognitive Bias

A common complication of human-based decisions in healthcare is cognitive bias. Implicit bias contributes to unclear diagnoses and suboptimal treatment plans. In fact, cognitive bias in healthcare is directly linked to increases in management errors. The incorporation of AI in orthodontics removes different types of biases including cognitive bias. This is because the diagnosis is done by a computer instead of a man.

Points Out Minute Details

The human eye can miss details that are important for diagnosis. The use of AI enhances diagnosis by picking out data that may be neglected. Studies show that AI is sensitive to pointing radiographic details and has a lower rate of critical misses. Moreover, AI algorithms are also capable of detecting disorders early on and allow the doctor to prevent aggravation. This enhances the precision of the treatment.

Cephalometric X-rays reveal substantial information regarding jaw-teeth discrepancies. Orthodontists need to determine multiple cephalometric landmarks. The laborious task makes dentists prone to missing crucial information. The machine learning algorithms like YOLOv3 and Single-Shot MultiBox Detector (SSD) can accurately identify around 80 landmarks.

Another issue faced by an orthodontist is the determination of the skeletal age. Experts suggest using different methods for the purpose (including wrist analysis and cervical vertebral maturation stages). The analysis of these tests requires expertise in the field. Inexperienced individuals are prone to making errors in diagnosis and consequently mess up the treatment plans. A scoping review revealed that artificial intelligence, deep learning, and machine learning algorithms can be validly used to assess the bone age of individuals. The results of these systems were clinically reliable and free of errors.

Provides Reliable Solutions Based On Hardcore Facts/Data

In addition to removing bias, artificial intelligence enhances precision by providing solutions based on data. As it lacks impulsive thinking and there are no heuristics involved, artificial intelligence’s plan for a specific problem is the same every time. Affect heuristics in human decisions are known to change decisions with changes in emotions. No such issue is seen with AI because algorithms are prepared with extensive training (unsupervised learning).

Such solutions play a significant role in sensitive decisions like opting for orthognathic surgery or not. Reports suggest that machine learning-based decision support systems are efficient and accurate in diagnosis and orthognathic surgical planning.

Minimizes Decision Noise

Artificial intelligence programs enhance precision by minimizing decision noise in orthodontics. Every specialist aims to minimize systematic errors in diagnosis and treatment planning. We can now take help from clinical decision support systems (CDSSs) designed specially for the healthcare field.

It has shown promising results in the field of orthodontics. According to a study, a clinical decision support system significantly reduces diagnostic errors (noise) and enhances the efficiency of treatment planning. Thus, with less diagnostic noise there are fewer errors.

Handles Large Amounts Of Data Easily

A commendable feature of AI is that it can handle large amounts of data very easily. This particular feature directly contributes to enhancements in precision. Nowadays, physicians prefer to provide customized treatment plans to patients. To achieve this, doctors acquire patient data from multiple sources (such as fitness trackers, medical devices, and sensors). Handling, storing, and accessing this much data manually is an impossible task. But it is possible with efficient automated programs.

Orthodontic treatment also requires the doctor to store and correlate large amounts of patient data. With AI, orthodontists can increase clinical efficiency which ultimately improves treatment precision.

Final Word

Artificial intelligence in orthodontics can help reduce diagnostic/treatment errors and enhance precision. Noise and unwanted artifacts can distort medical images (X-rays, etc.) and lead to misdiagnosis. AI reduces diagnostic errors by mitigating noise in orthodontic X-rays. It kicks out cognitive bias and decision noise from the picture to get the most accurate diagnosis. Deep learning is not influenced by emotional changes, thus, it provides the same solution every time. Automated detection points out minute details and detects disorders early on which can greatly enhance treatment precision. It’s ability to handle large amount of data also improves precision and minimizes manual errors


References

  1. Hartoonian, S., Hosseini, M., Yousefi, I., Mahdian, M., & Ahsaie, M. G. (2024). Applications of artificial intelligence in dentomaxillofacial imaging-A systematic review. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology.
  2. Juneja, M., Garg, P., Kaur, R., Manocha, P., Batra, S., Singh, P., … & Jindal, P. (2021). A review on cephalometric landmark detection techniques. Biomedical Signal Processing and Control, 66, 102486.
  3. Kunz, F., Stellzig-Eisenhauer, A., Zeman, F., & Boldt, J. (2020). Artificial intelligence in orthodontics: Evaluation of a fully automated cephalometric analysis using a customized convolutional neural network. Journal of Orofacial Orthopedics/Fortschritte der Kieferorthopadie, 81(1).
  4. Antonacci, A. C., Dechario, S. P., Antonacci, C., Husk, G., Patel, V., Nicastro, J., … & Jarrett, M. (2021). Cognitive bias impact on management of postoperative complications, medical error, and standard of care. Journal of Surgical Research, 258, 47-53.
  5. Plesner, L. L., Müller, F. C., Brejnebøl, M. W., Krag, C. H., Laustrup, L. C., Rasmussen, F., … & Andersen, M. B. (2024). Using AI to Identify Unremarkable Chest Radiographs for Automatic Reporting. Radiology, 312(2), e240272.
  6. Zewail, A., & Saber, S. (2023). AI-powered analytics in healthcare: enhancing decision-making and efficiency. International Journal of Applied Health Care Analytics, 8(5), 1-16.
  7. Subramanian, A. K., Chen, Y., Almalki, A., Sivamurthy, G., & Kafle, D. (2022). Cephalometric analysis in orthodontics using artificial intelligence—A comprehensive review. BioMed Research International, 2022(1), 1880113.
  8. Bajjad, A. A., Gupta, S., Agarwal, S., Pawar, R. A., Kothawade, M. U., & Singh, G. (2023). Use of artificial intelligence in determination of bone age of the healthy individuals: A scoping review. Journal of the World Federation of Orthodontists.
  9. 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.
  10. Belle, N., Cantarelli, P., & Wang, S. Y. (2024). The management of bias and noise in public sector decision-making: experimental evidence from healthcare. Public Management Review, 1-24.
  11. 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.

You'll find more articles in my blog:

Read more