Deep Learning in Dental Radiographic Imaging
김현태,
소속 상세정보
김현태 ( Kim Hyun-Tae ) -
Abstract
Deep learning algorithms are becoming more prevalent in dental research because they are utilized in everyday activities. However, dental researchers and clinicians find it challenging to interpret deep learning studies. This review aimed to provide an overview of the general concept of deep learning and current deep learning research in dental radiographic image analysis. In addition, the process of implementing deep learning research is described. Deep-learning-based algorithmic models perform well in classification, object detection, and segmentation tasks, making it possible to automatically diagnose oral lesions and anatomical structures. The deep learning model can enhance the decision-making process for researchers and clinicians.
This review may be useful to dental researchers who are currently evaluating and assessing deep learning studies in the field of dentistry.
키워드
Artificial intelligence; Deep learning; Dentistry; Image classification; Image segmentation
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