人工智能在龋齿诊断和检测中的应用:综述

IF 1.7 Q3 DENTISTRY, ORAL SURGERY & MEDICINE
Sapna Negi, Ankita Mathur, Snehasish Tripathy, Vini Mehta, Niher Tabassum Snigdha, Abdul Habeeb Adil, Mohmed Isaqali Karobari
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引用次数: 0

摘要

背景和目的 龋齿在很大程度上是可以预防的,但却是一个重要的全球健康问题。许多系统综述总结了人工智能(AI)模型在诊断和检测龋齿方面的功效。因此,本综述旨在综合人工智能模型在诊断和检测龋齿方面的应用和有效性的系统综述结果。 方法 对 MEDLINE/PubMed、IEEE Explore、Embase 和 Cochrane 系统性综述数据库进行检索。两位作者根据资格标准对文章进行了独立筛选,然后对纳入的文章进行了评估。研究结果以表格形式汇总,并采用叙述法进行讨论。 结果 共发现了 1249 篇文章,其中 7 篇最终被收录。最常用的人工智能算法是多层感知器、支持向量机(SVM)和神经网络。这些算法用于对根尖周X光片、全景X光片、智能手机图像、咬翼X光片、近红外光透射图像等多种来源的图像进行分割、分类、龋病检测、诊断和龋病预测。在龋病检测、分割和分类测试中,卷积神经网络(CNN)表现出较高的灵敏度、特异性和曲线下面积。值得注意的是,人工智能与根尖周和全景放射影像相结合,在检测和诊断龋齿方面具有更高的准确性。 结论 人工智能模型,尤其是基于卷积神经网络(CNN)的模型,在准确、客观的龋齿诊断和检测方面具有巨大的潜力。然而,伦理方面的考虑和谨慎采用仍是其成功融入常规实践的关键。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Artificial Intelligence in Dental Caries Diagnosis and Detection: An Umbrella Review

Artificial Intelligence in Dental Caries Diagnosis and Detection: An Umbrella Review

Background and Aim

Dental caries is largely preventable, yet an important global health issue. Numerous systematic reviews have summarized the efficacy of artificial intelligence (AI) models for the diagnosis and detection of dental caries. Therefore, this umbrella review aimed to synthesize the results of systematic reviews on the application and effectiveness of AI models in diagnosing and detecting dental caries.

Methods

MEDLINE/PubMed, IEEE Explore, Embase, and Cochrane Database of Systematic Reviews were searched to retrieve studies. Two authors independently screened the articles based on eligibility criteria and then, appraised the included articles. The findings are summarized in tabulation form and discussed using the narrative method.

Result

A total of 1249 entries were identified out of which 7 were finally included. The most often employed AI algorithms were the multilayer perceptron, support vector machine (SVM), and neural networks. The algorithms were built to perform the segmentation, classification, caries detection, diagnosis, and caries prediction from several sources, including periapical radiographs, panoramic radiographs, smartphone images, bitewing radiographs, near-infrared light transillumination images, and so forth. Convoluted neural networks (CNN) demonstrated high sensitivity, specificity, and area under the curve in the caries detection, segmentation, and classification tests. Notably, AI in conjunction with periapical and panoramic radiography images yielded better accuracy in detecting and diagnosing dental caries.

Conclusion

AI models, especially convolutional neural network (CNN)-based models, have an enormous amount of potential for accurate, objective dental caries diagnosis and detection. However, ethical considerations and cautious adoption remain critical to its successful integration into routine practice.

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来源期刊
Clinical and Experimental Dental Research
Clinical and Experimental Dental Research DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
3.30
自引率
5.60%
发文量
165
审稿时长
26 weeks
期刊介绍: Clinical and Experimental Dental Research aims to provide open access peer-reviewed publications of high scientific quality representing original clinical, diagnostic or experimental work within all disciplines and fields of oral medicine and dentistry. The scope of Clinical and Experimental Dental Research comprises original research material on the anatomy, physiology and pathology of oro-facial, oro-pharyngeal and maxillofacial tissues, and functions and dysfunctions within the stomatognathic system, and the epidemiology, aetiology, prevention, diagnosis, prognosis and therapy of diseases and conditions that have an effect on the homeostasis of the mouth, jaws, and closely associated structures, as well as the healing and regeneration and the clinical aspects of replacement of hard and soft tissues with biomaterials, and the rehabilitation of stomatognathic functions. Studies that bring new knowledge on how to advance health on the individual or public health levels, including interactions between oral and general health and ill-health are welcome.
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