  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.
