A Review: Teeth Numbering and Classification Methods on the OPG Image
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Abstract
Abstract
Subject of research: A review of the existing teeth numbering and classification methods on images is presented. The available architectural peculiarities and their practical importance are considered. The best solutions comparison and identification in these areas were carried out.
Method: To evaluate the teeth numbering and classification methods results, the following quality metrics were selected: IoU, average precision, and accuracy, as well as other metrics that were given in the reviewed studies. Also, attention is paid to the data pre-processing, the image sources, and the amount of data used to train and test the models. The advantages and disadvantages of each solution are considered.
Main results: Based on the study results, the best algorithm for regression-based tooth numbering was identified. This method allows us to carry out qualitative teeth segmentation. The advantage of this approach besides the superiority in metrics values is that it is capable of finding out a position of missing teeth.
Practical relevance: This research can be useful for specialists in the field of machine learning technologies, as well as physicians conducting research in the field of medical process automation. The results of this work may be useful in the dental X-ray image recognition system implementation in medical assistants.
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