The Promise of Self-Supervised Learning for Dental Caries

learning (SSL) is a type of machine learning that does not require labeled data. Instead, SSL algorithms learn from unlabeled data by predicting the order of image patches, predicting the missing pixels in an image, or predicting the rotation of an image. SSL has been shown to be effective for a variety of tasks, including image classification, object detection, and segmentation. Dental image processing is a rapidly growing field with a wide range of applications, such as caries detection, periodontal disease progression prediction, and oral cancer detection. However, the manual annotation of dental images is time-consuming and expensive, which limits the development of dental image processing algorithms. In recent years, there has been growing interest in using SSL for dental image processing. SSL algorithms have the potential to overcome the challenges of manual annotation and to improve the accuracy of dental image analysis. This paper conducts a comparative examination between studies that have used SSL for dental caries processing and others that use machine learning methods. We also discuss the challenges and opportunities for using SSL in dental image processing. We conclude that SSL is a promising approach for dental image processing. SSL has the potential to improve the accuracy and efficiency of dental image analysis, and it can be used to overcome the challenges of manual annotation. We believe that SSL will play an increasingly important role in dental image processing in the years to come.