ARTIFICIAL INTELLIGENCE METHODS IN DENTAL RADIOGRAPHY: CURRENT DEVELOPMENT OF SEGMENTATION AND IMAGE QUALITY ENHANCEMENT METHODS IN DIFFERENT MODALITIES

Abstract

Introduction: The application of artificial intelligence in dentistry can significantly improve the outcome of patient treatment and reduce human error. Computer systems and certain methods that use complex algorithms are able to encode digital data about the radiographic image, obtained by X-ray radiation, and transfer them to a computer language for processing a large set of data. Applied as an auxiliary tool, it can give information about the patient and diagnostics of teeth and surrounding dental structures on radiographic images (two-dimensional and three-dimensional).

The aim of the paper: The aim of the paper is to present the development and application of artificial intelligence in dentistry and at the same time emphasizing the importance of segmentation methods and methods of improving the quality of dental radiographic images.

Discussion: The common goal of the development and application of artificial intelligence methods in dental radiography is directed on obtaining radiograms from which diagnostically valuable information will be collected faster and easier for the purpose of successful treatment of the patients. Methods of segmentation of a dental structures have been developed to automatically number and localize the tooth or independently mark and display the desired structures on the image. Methods of image quality enhancement tend to work on, for example, improving image resolution or reducing metal artifacts. Research has shown that artificial intelligence can be applied in everyday clinical practice, but it has certain limitations.

Conclusion: Artificial intelligence methods in dental radiography are an useful auxiliary tool in clinical dental practice because they can speed up the process of retrieving patient data. Automatic localization of dental structure reduces the dentist's time for manual image analysis, but cannot completely replace human knowledge. For the full implementation of the mentioned methods, more development standards and resources are needed to overcome the ethical and legal problems of replacing humans with computer systems.