An overview of image processing for dental diagnosis

IF 2.4 Q2 ENGINEERING, MULTIDISCIPLINARY
Rahulsinh B. Chauhan, Tejas V. Shah, Deepali H. Shah, Tulsi J. Gohil, Ankit D. Oza, Brijesh Jajal, K. Saxena
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引用次数: 19

Abstract

Dental disease evaluation and clinical assessment are frequently accomplished through radiographic penetration. The difficulty of obtaining an accurate clinical diagnosis from radiographs rises due to the minimal mineral density change in demineralized tissue of tooth and gum disorders. Dental abnormalities may not be visible on radiographs until the demineralization is higher than 40%, according to the literature. As a result, a dental practitioner’s judgment can have a big impact on how accurately the radiography penetration depth is determined through visual inspection. To counteract this effect, image processing-based clinical diagnosis methods have become widely adopted, transforming dentistry from traditional to advance in recent years. The efforts made in the area of image processing-based digital dental diagnosis of the most challenging dental issues are outlined in the presented comprehensive literature evaluation, which also identifies any research gaps in the scope of work already done. The included studies’ quality was evaluated using Quality Assessment and Diagnostic Accuracy Tool-2 (QUADAS-2). A total of 52 out of 178 articles, published from 2012 to February 2023, were reviewed and data like image-processing approach, the size of datasets, approach results, advantages and disadvantages, name(s) of diagnosed diseases, imaging type, author, and publication year were extracted. Results show that, in 52 studies, more than 14 image-processing approaches were used on different types of radiographs for the diagnosis of a single or more than one disease by a single approach with an accuracy range from 64% to 93%. Most studies have used artificial intelligence (AI) for computer-aided diagnosis and used dental experts to label their dataset and validate the outcome of proposed methods. Efforts done by different research groups for image processing-based digital diagnosis are appreciable but still, they are lagging to meet clinically expected accuracy. There looks to be a great requirement for the development or standardization of existing methodology and it is also needed to construct standard public dental datasets to attract a greater number of research groups in the dental field.
牙科诊断的图像处理综述
牙科疾病的评估和临床评估通常是通过x线透视完成的。由于牙齿和牙龈疾病的脱矿组织中矿物质密度变化很小,因此从x线片获得准确临床诊断的难度增加。根据文献,在脱矿率高于40%之前,牙齿异常可能不会在x光片上显示出来。因此,牙科医生的判断对通过目视检查确定射线透视深度的准确性有很大影响。为了抵消这种影响,近年来基于图像处理的临床诊断方法被广泛采用,使牙科从传统转向先进。在最具挑战性的牙科问题的基于图像处理的数字牙科诊断领域所做的努力在提出的综合文献评估中进行了概述,这也确定了已经完成的工作范围内的任何研究差距。采用质量评估和诊断准确性工具-2 (QUADAS-2)对纳入研究的质量进行评价。对2012年至2023年2月发表的178篇文章中的52篇进行了综述,提取了图像处理方法、数据集大小、方法结果、优缺点、诊断疾病名称、成像类型、作者、出版年份等数据。结果表明,在52项研究中,在不同类型的x线片上使用了14种以上的图像处理方法,通过一种方法诊断一种或多种疾病,准确率从64%到93%不等。大多数研究使用人工智能(AI)进行计算机辅助诊断,并使用牙科专家标记他们的数据集并验证所提出方法的结果。不同的研究小组为基于图像处理的数字诊断所做的努力是值得赞赏的,但仍然落后于满足临床期望的准确性。现有方法的发展或标准化似乎是一个很大的需求,也需要建立标准的公共牙科数据集,以吸引更多的牙科领域的研究小组。
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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