PDF(Pubmed)
Abstract：
Background: Generative Adversarial Networks (GANs) are a class of artificial neural networks capable of generating content such as images, text, and sound. For several years already, artificial intelligence algorithms have shown promise as tools in the medical field, particularly in oncology. Generative Adversarial Networks (GANs) represent a new frontier of innovation, as they are revolutionizing artificial content generation, opening opportunities in artificial intelligence and deep learning. Purpose: This systematic review aims to investigate what the stage of development of such technology is in the field of head and neck surgery, offering a general overview of the applications of such algorithms, how they work, and the potential limitations to be overcome in the future. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed in conducting this study, and the PICOS framework was used to formulate the research question. The following databases were evaluated: MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), Scopus, ClinicalTrials.gov, ScienceDirect, and CINAHL. Results: Out of 700 studies, only 9 were included. Eight applications of GANs in the head and neck region were summarized, including the classification of craniosynostosis, recognition of the presence of chronic sinusitis, diagnosis of radicular cysts in panoramic X-rays, segmentation of craniomaxillofacial bones, reconstruction of bone defects, removal of metal artifacts from CT scans, prediction of the postoperative face, and improvement of the resolution of panoramic X-rays. Conclusions: Generative Adversarial Networks may represent a new evolutionary step in the study of pathology, oncological and otherwise, making the approach to the disease much more precise and personalized.
摘要：
背景：生成对抗网络（GAN）是一类能够生成图像等内容的人工神经网络，文本,和声音。几年来,人工智能算法已经显示出作为医疗领域工具的前景，尤其是肿瘤学。生成对抗网络(GAN)代表了创新的新前沿，因为他们正在彻底改变人工内容生成，开启人工智能和深度学习的机会。目的：本系统综述旨在探讨头颈外科领域这种技术的发展阶段，提供了这些算法应用的一般概述，它们是如何工作的，以及未来需要克服的潜在限制。方法：本研究遵循系统评价和荟萃分析（PRISMA）指南的首选报告项目，并使用PICOS框架来制定研究问题。对以下数据库进行了评估：MEDLINE，Embase,Cochrane中央对照试验登记册（中央），Scopus,ClinicalTrials.gov,ScienceDirect,和CINAHL。结果：在700项研究中，只包括9个。总结了GAN在头颈部的八种应用,包括颅骨融合的分类，认识到慢性鼻窦炎的存在，在全景X射线中诊断神经根囊肿，颅颌面骨的分割，骨缺损重建，从CT扫描中去除金属伪影，预测术后面部，提高全景X射线的分辨率。结论：生成对抗网络可能代表病理学研究的一个新的进化步骤，肿瘤学和其他方面，使治疗疾病的方法更加精确和个性化。
