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.

翻译：目的：本研究提出了一种新颖的保面积生成对抗网络（GAN）反演技术，用于有效去标识化牙科患者图像。该创新方法在保留关键牙齿特征的同时解决了隐私问题，从而为牙科教育和研究生成宝贵资源。方法：我们改进了现有GAN反演方法，以最大化保留合成图像中的牙齿特征。开发了一个集成多种深度学习模型的综合技术框架，为图像去标识化提供端到端开发指导与实用应用。结果：采用多种面部图像评估本方法，这些图像广泛用于诊断骨骼不对称和面部异常。结果表明，模型具备跨图像上下文适配能力，在保持兼容性的同时保留口腔诊断和牙科教育所需的牙齿特征。由五名临床医生组成的专家组对原始图像和GAN处理图像进行了评估。生成的图像实现了有效去标识化，保留了重要牙齿特征的真实性，被认为可用于牙科诊断与教育。临床意义：本GAN模型及其框架可简化牙科患者图像的去标识化流程，提升牙科教育效率。该方法通过增加学生接触错牙合畸形病例的机会，提高其诊断能力。此外，该方法有助于在主要研究机构创建去标识化数据集，以支持更广泛的二维图像研究。