How Artificial Intelligence is becoming part of Dentistry

Artificial Intelligence is gradually becoming an integral part our daily lives. From simple Google searches to more complex things like Scientific, Medical, and Robots on other planets, AI is slowly becoming a part of our everyday lives. And Dentistry is not far behind is integrating Artificial Intelligence into our field – Diagnosing and treatments.

Yomi, a robot-assisted surgical device created by Neocis has allowed clinicians to place 10,000 dental implants using the robotic platform.

Some terms which are useful when talking about AI are –

  • AI – Artificial Intelligence
  • ML – Machine Learning (It is a branch of AI which performs intelligent tasks without any prior knowledge or rules by just learning)
  • DL – Deep Learning
  • ANN – Artificial Neural Network
  • DNN – Deep Neural Network
  • CNN – Convolutional Neural Network

Convolutional neural networks, or CNN, are the most common AI technology used in Dentistry. They are used for image recognition, taking in digital signals like sound, image, and video.

AI applied to various Branches of Dentistry


This is one branch of Dentistry where AI is already playing a vital role in determining the treatment plan and also aiding in designing the treatment protocol actively in the form of “Aligners treatment“. ANN is actively used in the planning of Orthodontic treatments as well as predicting treatment outcomes for patients.

Because some Orthodontic treatments require the removal of teeth, a combination of clinical evaluation and ANN can help determine the need for tooth extraction. The accuracy of ANNs in determining the need to extract a tooth was 80-93% depending on the malocclusion.

Dental Radiology:

CNN has been trained by Periapical Radiograph to identify Teeth. It has tested and shown a precision rate of 95.8%-99.45%, which is comparable to the precision rate of a clinical worker with 99.98%.

CNN algorithm used to detect Dental Caries in healthy tooth structures. In a sample of 3000 radiographs of Posterior Teeth, it showed a precision of 75.5 to 93%. It detected Dental Caries with Radiograph alone at a sensitivity of 74.5% – 97.1%. This is significantly higher than the sensitivity rate of clinicians who use Radiographs alone to detect dental caries. This tool can be refined to help detect dental caries more quickly and allow clinicians to make a diagnosis.

Cosmetic Dentistry

Smile Designing has gained importance with many giving importance to their smile and aesthetics, 3D smile designing software’s which use AI to show the end result of a patient’s smile is an important tool for the clinician and the patient to come to a definitive conclusion about the treatment plan.

Some software’s such as DSD (Digital Smile Design) and DTS Pro(Digital Treatment Simulation) are available from Planmeca Romexis Digital Smile Design, etc. These software’s make use of AI and Machine learning to help Dentist’s design smiles in minutes.


In Periodontics AI is used to diagnose between the two types of Periodontitis – Aggressive and Chronic. It is possible to identify the type of Periodontitis and determine the treatment plan. Papantanopoulos, along with colleagues, used ANN technology for the distinction between Aggressive and Chronic Periodontitis using immunologic parameters such Leukocytes.

These parameters were identified using ANN. This helped to distinguish AgP from CP with a 90-98% accuracy. Other parameters, such as monocytes or neutrophil counts and CD4+/CD8+/T-cell ratio, were also used as inputs to help with more precise diagnosis.

Lee and her coworkers used CNN algorithm for determining the need to extract Periodontally Compromised or damaged teeth. CNN Algorithm was able to predict the need for extraction with 73.4 to 82% accuracy. With 73.4% being Molar Teeth with Multiple Roots, the accuracy of predicting the need for extraction was 73.4 to 82.8%. The Single-rooted premolars gave 82.8% accuracy.

Oral Pathology

It can be used to distinguish between cancerous and precancerous lesions in the neck, head, and oral cavity using both clinical and other diagnostic methods. CNN has proven to be a promising tool for detecting tumor tissues in tissue samples, radiographs, as well as in real-world situations. The specificity for diagnosing head- and neck cancer has been 78-81% and 80-83% respectively, which is close to what clinicians can do.

A study was done to determine the difference between Ameloblastoma versus OKC. It used certain parameters. AI integration made it much easier to identify which one is faster.

AI integration has allowed single-seat dentistry, reducing the amount of contamination and operating time. As technology advances at an alarming pace, AI integration into Dentistry will continue to grow. This can help us become more efficient.

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Article by Varun Pandula

Varun is a dentist from Hyderabad, India. I try my best to help all people understand Dental problems and treatment and make Dental Education simple for Dental Students and the Dental fraternity.
You can contact me if you have any questions or comments. Thanks for visiting.

How Artificial Intelligence is becoming part of Dentistry

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