CREATION OF CARIES DETECTION SYSTEM - KRYVENCHUK, Yurii; OLESKEVYCH, Sofiia (Herald of Khmelnytskyi National University. Technical sciences, 2023)
The usage of modern technological solutions in dentistry is gaining increasingly popularity. Every year the systems are becoming more and more perfect. Of course, it will take more than one year to get the best ones and most reliable results, but you can be sure that it will completely change the way people think about receiving quality dental treatment. Identifying the problem, treatment and its quality directly depends on the human factor from both sides. This applies both to the doctor's qualifications and his own experience, and to the patient. At the same time, doctors very often have to deal with non-standard and unique cases, complications, which in the future are accompanied by the involvement of a whole team of specialists. From the patient's side – from X-ray to computed tomography – has a very high probability of receiving completely different diagnoses from specialists of the same sector. That is, in essence, his health depends on how accurately dentists will be able to interpret the results of examinations and formulate a treatment plan. In order to minimize the influence of the human factor, the use of artificial intelligence, which does not have the ability to miss nuances, feel fatigue and be distracted, is quite necessary.Caries, also known as tooth decay or cavities, are one of the most common oral health problems. They are caused by the buildup of plaque and bacteria on the teeth, which can lead to erosion of the enamel and dentin. Dental x-rays, also known as radiographs, are commonly used to detect caries in the early stages. However, interpreting radiographs can be challenging and prone to human error. AIbased caries detection systems can assist with the interpretation of radiographs by identifying patterns in the images that are indicative of caries. One example of an AI-based caries detection system is a machine learning algorithm will be trained to recognize patterns in radiographs that are indicative of caries. The algorithm can analyze the x-ray images and flag any areas of the tooth that have a high probability of being carious. This can help dentists identify the problem in its early stages, when it is still reversible, and provide appropriate treatment. AI-based caries detection systems can also be used to improve patient diagnosis and treatment by identifying high-risk patients, providing more accurate diagnosis, and developing personalized treatment plans.
Keywords: caries; dentistry; AI; ML; caries detection