用于正畸教育成像中牙科患者身份保护的生成对抗网络
Generative Adversarial Networks for Dental Patient Identity Protection in Orthodontic Educational Imaging
AI总结：
本文提出一种保面积GAN反演技术用于牙科患者图像去身份化，在保护隐私的同时保留关键牙科特征，并通过临床评估验证其对牙科教育和诊断的实用价值。
摘要：
目的：本研究提出一种新颖的保面积生成对抗网络（GAN）反演技术，用于对牙科患者图像进行有效的去身份化处理。该创新方法在解决隐私问题的同时保留关键牙科特征，从而为牙科教育和研究生成有价值的资源。方法：我们改进了现有的GAN反演方法，以在合成图像中最大限度地保留牙科特征。我们开发了一个包含多个深度学习模型的综合技术框架，为图像去身份化提供端到端的开发指导和实际应用。结果：我们使用广泛用于诊断骨骼不对称和面部异常的各种面部图像对所提方法进行了评估。结果表明，我们的模型能够将上下文从一幅图像适配到另一幅图像，在保持兼容性的同时，保留对口腔诊断和牙科教育至关重要的牙科特征。由五名临床医生组成的小组对一组原始图像和GAN处理后的图像进行了评估。生成的图像实现了有效的去身份化，保持了重要牙科特征的真实感，并被认为可用于牙科诊断和教育。临床意义：我们的GAN模型及整体框架可以简化牙科患者图像的去身份化流程，提高牙科教育的效率。该方法通过提供更多接触正畸错颌畸形病例的机会，提升学生的诊断能力。此外，它还有助于在主要研究机构中创建去身份化的数据集，以支持更广泛的二维图像研究。
英文摘要：
Objectives: This research introduces a novel area-preserving Generative Adversarial Networks (GAN) inversion technique for effectively de-identifying dental patient images. This innovative method addresses privacy concerns while preserving key dental features, thereby generating valuable resources for dental education and research. Methods: We enhanced the existing GAN Inversion methodology to maximize the preservation of dental characteristics within the synthesized images. A comprehensive technical framework incorporating several deep learning models was developed to provide end-to-end development guidance and practical application for image de-identification. Results: Our approach was assessed with varied facial pictures, extensively used for diagnosing skeletal asymmetry and facial anomalies. Results demonstrated our model's ability to adapt the context from one image to another, maintaining compatibility, while preserving dental features essential for oral diagnosis and dental education. A panel of five clinicians conducted an evaluation on a set of original and GAN-processed images. The generated images achieved effective de-identification, maintaining the realism of important dental features and were deemed useful for dental diagnostics and education. Clinical Significance: Our GAN model and the encompassing framework can streamline the de-identification process of dental patient images, enhancing efficiency in dental education. This method improves students' diagnostic capabilities by offering more exposure to orthodontic malocclusions. Furthermore, it facilitates the creation of de-identified datasets for broader 2D image research at major research institutions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
