How is machine learning used in dentistry?


Artificial intelligence (AI) is a general term that refers to the performance of human tasks with the help of machines and technology. Artificial intelligence is part of computer science associated with the design of an intelligent computer system that combines the characteristics we associate with intelligence with understanding human behavior, language, learning, reasoning, problem-solving, and more. There are subfields of artificial intelligence that include machine learning and related fields such as deep learning, cognitive computing, natural language processing, robotics, expert systems, and fuzzy logic.

Machine learning is a subset of artificial intelligence that enhances the ability to learn automatically without specific planning. Its major purpose is to allow automatic learning without human judgment. Artificial intelligence models predict future events with a set of current observations.


  1. Orthodontic

The decision to have a tooth extracted for orthodontic treatment is important and difficult because it depends on the doctor’s experience. An artificial intelligence expert system has been developed to detect extraction using neural networks (NN; type of machine learning) and evaluate the effectiveness of this model. The results show that orthodontic systems may be useful for expert systems with machine learning.

In NN which is a big help for doctors who work less. The experiment in orthodontics was performed with the aim of establishing an expert decision-making system for the treatment of orthodontic patients in order to determine whether tooth extraction is necessary or not.

Experiments have been performed using a learning machine that can assist orthodontic treatment calculations and decisions. Take cephalometric values, for example. First, you enter normal variables as a reference. Then, you enter patients’ cephalometric values. The system compares these entries and gives you a final analysis.

You can use an already existing machine learning software. RapidMiner predicts the mandibular morphology through craniomaxillary variables on lateral radiographs in patients with skeletal classes I, II, and III. Standardized lateral radiographs were used to create mandibular measurements.

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  1. Periodontology

One of the areas that can be promoted using machine learning is gum disease. Scientists have designed an algorithm to do this, they enter data including risk factors, data related to the periodontium, radio graphical information, bone loss, etc. The system uses them to design a matrix that can be considered a classification unit. As a result, six classifications of diseases were considered as the periodontium. Using this system, it is possible to diagnose and classify gum diseases with 98% accuracy.

Aggressive Periodontitis (AgP) is one of the problems caused by autoimmune disease. Another gum-related disease is chronic periodontitis (CD). There is no clinical, microbiological, or histopathological marker to diagnose AgP in Patients with CD. Scientists have developed an algorithm that can correctly classify patients into either AgP or CP classes.

To do this, immune factors such as CD4 / CD8 ratio showed from 2 to 7 clusters, CD3, monocytes, eosinophils, neutrophils, and lymphocytes, levels of interleukin (IL) -1, IL-2, IL- 4, interferon-gamma (INF-γ) and tumor necrosis factor α (TNF-α) from monocytes, as well as antibody levels against Actinobacillus actinomycetemcomitans (Aa) and Porphyromonas gingivalis (Pg), are calculated. Based on these factors, machine learning gave 90–98% accuracy in classifying patients into both groups.

Another area that can be evaluated using machine learning is saliva evaluation. The presence of methyl mercaptan in exhaled air is considered an indicator of yeasts. Thus, the high percentage of this substance in the respiratory air indicates yeasts in saliva. This is useful for saliva screening in respiratory tract allergies before visits to specialist clinics.


  1. Dental surgery

A study that provides machine learning developed to diagnose vertical root fractures may be in the field of endodontics or on the verge of dental surgery. This is an important and difficult issue because vertical fractures are often difficult for a physician to diagnose because of cracks that overlap the anatomical structures of a two-dimensional dental X-ray. It is possible to use a machine learning scheme to assess whether the tooth root was intact or had a vertical fracture.

Two hundred photos from digital radiography were used for training, and in the end, it was efficient enough to diagnose whether there is a fracture in the root or not. The image indicated gray data on the line passing through the root.

Dental surgery includes implantology, which combines prosthetics, orthodontics, and periodontology. Therefore, the physician must analyze a large amount of data before deciding on implantation. There is a web application that provides recommendations for implant use based on medical history and examination, such as 3D measurements, diagnostic information for treatment planning, and objective implant measurements.

The core of the program was specialized knowledge programming, such as DT. The structure model had four fundamental components: inference engine, knowledge base, working memory, and explanation. This system will be valuable for dentists as long as it is widely available and widespread.

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  1. Maxillofacial surgery

The field of dentistry, which is all-important in daily practice, is maxillofacial surgery, which includes the diagnosis and treatment of oral cancer. A potential diagnostic error may cost patients their lives. Therefore, it is recommended to introduce objective solutions that allow 100% accurate diagnosis. Some scientists have taken steps to solve this problem. The major purpose of this study was to apply a combination of feature selection and machine learning methods to the prognosis of oral cancer based on the correlation parameters of pathological and genomic clinical markers.


Neural network analysis has been also used to assess hypernasality in patients treated for oral or oropharyngeal cancer. Speech recordings were evaluated according to the patient’s hypernasality, expression, intelligibility, and speech outcome. Objective measurement by machine learning could not distinguish between patients for tumor stage and tumor location, while trained listeners can differentiate between patients for tumor stage, but not for tumor location. As a result, machine learning is not yet able to substitute trained raters.

The topic of risk and causes of malignization is presented in a recently published article in which the authors aim to estimate the risk of cancer due to oxidative stress of potential malignant processes. The risk was estimated as a specific numerical value on a scale of 1 to 10 depending on the numerical/linguistic value of the input. This system can be a great achievement in oral cancer screening. However, tests on a large number of samples are needed before being accepted as a reliable tool for inferring screening in this highly challenging medical field.

All of the mentioned are just results of studies, and there is not enough evidence about their reliability. They need to be studied in large sample sizes to spread out an as applicable method. Given the potential of machine learning, it can be cooperative for dentistry in the time to come. The use of AI in dentistry is tight at the moment, and there are a lot of countries that yet need to be familiar with this innovative technology.

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