Analytical Overview on Transfer Learning in Processing Dental X-rays

Seba Al Mokdad, Anas Al Houria, M. A. Talib, M. Moufti, A. Bouridane, Q. Nasir
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Abstract

Dental x-rays have been a standard piece of dental equipment for many years and are an indispensable diagnostic tool for dentists to detect tooth damage or disease. Recent research has focused on employing computer vision algorithms to automate analysis of dental x-rays. Our study aims to review the work done using transfer learning in dental image processing. AI solutions for dental images have been developed for many purposes, including examining tooth cavities (caries) and restorations and abnormalities in the maxillary sinuses. They have also been used to classify dental implants and determine gender in forensic studies. Transfer Learning is a new approach that is being used to solve a problem that classic deep learning and machine learning techniques could not solve: that of data limitation. Our search has investigated 80 research papers, of which 30 were relevant and analyzed in this paper. The identified studies have discussed a variety of transfer learning models to process different types of x-rays and have reported their efficacy using a variety of metrics. Transfer learning was used to solve various problems depending on the research question. Some papers compared the performance of transfer learning with that of dental experts in analyzing x-ray images, the accuracy of which were surprisingly close to equal. Although the results of the majority of dental applications performed using transfer learning models are encouraging, future research will need to solve the shortcomings highlighted in the present review.
牙科x射线处理中的迁移学习分析综述
牙科x光片多年来一直是一种标准的牙科设备,是牙医检测牙齿损伤或疾病不可或缺的诊断工具。最近的研究集中在使用计算机视觉算法来自动分析牙科x射线。本文综述了迁移学习在牙齿图像处理中的应用。牙科图像的人工智能解决方案已经开发用于许多目的,包括检查蛀牙(龋齿)和修复以及上颌窦的异常。它们还被用于对植牙进行分类,并在法医研究中确定性别。迁移学习是一种新方法,它被用来解决经典深度学习和机器学习技术无法解决的问题:数据限制。我们的搜索调查了80篇研究论文,其中30篇与本文相关并进行了分析。已确定的研究讨论了各种迁移学习模型来处理不同类型的x射线,并使用各种指标报告了它们的有效性。根据研究问题的不同,迁移学习可以解决不同的问题。一些论文将迁移学习的表现与牙科专家在分析x射线图像方面的表现进行了比较,两者的准确性惊人地接近。尽管使用迁移学习模型进行的大多数牙科应用的结果是令人鼓舞的,但未来的研究将需要解决当前综述中突出的缺点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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