生成对抗网络在牙科成像:系统回顾。

IF 1.6 3区 医学 Q3 DENTISTRY, ORAL SURGERY & MEDICINE
Oral Radiology Pub Date : 2024-04-01 Epub Date: 2023-11-24 DOI:10.1007/s11282-023-00719-1
Sujin Yang, Kee-Deog Kim, Eiichiro Ariji, Yoshitaka Kise
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引用次数: 0

摘要

目的:本文对牙科图像分析的生成对抗网络(GAN)架构进行了系统综述,为读者提供了有关牙科图像中当前GAN趋势和潜在未来应用的全面概述。方法:检索电子数据库(PubMed/MEDLINE、Scopus、Embase和Cochrane Library),以确定涉及gan用于牙科图像分析的研究。综述了18篇描述gan在牙科图像中的应用的全文文章。使用QUADAS-2工具评估偏倚风险和适用性问题。结果:gan用于各种成像方式,包括二维和三维图像。在牙科成像中,gan被用于诸如伪影减少、去噪、超分辨率、领域转移、增强图像生成、结果预测和识别等任务。生成的图像被用于地标检测、目标检测和分类等任务。由于研究之间存在异质性,因此无法进行meta分析。大多数研究(72%)在所有四个领域的偏倚风险都很低。然而,只有3项(17%)研究存在低风险的适用性问题。结论:对gan在牙科成像中的广泛分析突出了其在牙科领域的广泛应用潜力。未来的研究应该解决与GAN架构的稳定性、可重复性和整体可解释性相关的限制。通过克服这些挑战,可以增强gan在牙科领域的适用性,最终使gan和人工智能在牙科领域的应用受益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Generative adversarial networks in dental imaging: a systematic review.

Generative adversarial networks in dental imaging: a systematic review.

Objectives: This systematic review on generative adversarial network (GAN) architectures for dental image analysis provides a comprehensive overview to readers regarding current GAN trends in dental imagery and potential future applications.

Methods: Electronic databases (PubMed/MEDLINE, Scopus, Embase, and Cochrane Library) were searched to identify studies involving GANs for dental image analysis. Eighteen full-text articles describing the applications of GANs in dental imagery were reviewed. Risk of bias and applicability concerns were assessed using the QUADAS-2 tool.

Results: GANs were used for various imaging modalities, including two-dimensional and three-dimensional images. In dental imaging, GANs were utilized for tasks such as artifact reduction, denoising, and super-resolution, domain transfer, image generation for augmentation, outcome prediction, and identification. The generated images were incorporated into tasks such as landmark detection, object detection and classification. Because of heterogeneity among the studies, a meta-analysis could not be conducted. Most studies (72%) had a low risk of bias in all four domains. However, only three (17%) studies had a low risk of applicability concerns.

Conclusions: This extensive analysis of GANs in dental imaging highlighted their broad application potential within the dental field. Future studies should address limitations related to the stability, repeatability, and overall interpretability of GAN architectures. By overcoming these challenges, the applicability of GANs in dentistry can be enhanced, ultimately benefiting the dental field in its use of GANs and artificial intelligence.

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来源期刊
Oral Radiology
Oral Radiology DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
4.20
自引率
13.60%
发文量
87
审稿时长
>12 weeks
期刊介绍: As the official English-language journal of the Japanese Society for Oral and Maxillofacial Radiology and the Asian Academy of Oral and Maxillofacial Radiology, Oral Radiology is intended to be a forum for international collaboration in head and neck diagnostic imaging and all related fields. Oral Radiology features cutting-edge research papers, review articles, case reports, and technical notes from both the clinical and experimental fields. As membership in the Society is not a prerequisite, contributions are welcome from researchers and clinicians worldwide.
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