Generative Adversarial Networks for Dental Patient Identity Protection in Orthodontic Educational Imaging

TL;DR

This paper presents a novel GAN-based method for de-identifying dental images that effectively preserves key dental features, ensuring privacy while maintaining diagnostic utility for education and research.

Contribution

It introduces an innovative GAN inversion technique tailored for dental image de-identification, enhancing privacy without losing critical diagnostic details.

Findings

Effective de-identification of dental images achieved

Preserved dental features important for diagnosis

Clinicians found images useful for education and diagnostics

Abstract

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.