Background: Anterior segment optical coherence tomography (AS-OCT) constitutes an important imaging modality to examine the anterior eye, which is commonly used in research and clinical practice. Since its introduction, a range of image analysis methods have been developed to quantify these images using different analysis techniques for various applications. This systematic review aims to provide an in-depth summary and to classify image analysis techniques found in the literature applied to AS-OCT images.
Methods: Scopus and Engineering Village databases were searched to retrieve relevant studies up to and including January 2022. Customized search statements were used along with cross reference and hand search techniques to ensure a complete coverage. Performance metrics were extracted, analyzed, and compared (when possible).
Results: Three main application categories were identified: glaucoma assessment, corneal segmentation, and anterior segment biometry. These three categories constitute 66% of the total studies reported in this review. Studies were also analyzed by year of publication, and since 2019 deep learning methods were favored over traditional programming or machine learning methodologies. Overall, the AS-OCT image analysis field is less developed compared to posterior segment OCT imaging.
Conclusion: This review presents the state of the art in the field of AS-OCT image analysis. It highlights the opportunities for future areas of research, such as the expansion of DL methods and the extension to specific clinical areas that have received limited attention including surgical monitoring, contact lenses, and specific clinical conditions such as keratoconus and corneal lesions.
Keywords: AS-OCT; Anterior segment; Cornea; Deep learning; Image analysis.
Copyright © 2022. Published by Elsevier Ltd.