AI-Based Detection and Correction of Motion Artifacts in Optical Coherence Tomography Scans of the Retina

This is the authors' abstract. We don't add key points for this paper.

Fixational eye movements are essential for visual perception but introduce significant challenges in ophthalmic imaging by causing motion artifacts that compromise the accuracy of diagnostic procedures. This study proposes an AI-based solution to achieve comprehensive motion correction in optical coherence tomography scans of the retina, with the aim of substantially enhancing image quality. In the context of intraoperative imaging, artifact correction is essential for accurate visualization, thereby mitigating the risk of diagnostic errors. A key challenge to the use of AI-based methods is the lack of extensive and appropriate training data, as no publicly available datasets currently contain paired optical coherence tomography images with and without motion artifacts. To bridge this gap, a simulation tool was developed to synthetically induce motion artifacts in OCT datasets. These artifacts are generated based on simulated fixational eye movements, creating realistic distortions that serve as a foundation for generating synthetic training data. Furthermore, existing AI models for motion extraction were evaluated and fine-tuned for the specific task of artifact recognition through transfer learning and subsequent artifact correction.