Vision-threatening conditions, such as age-related macular degeneration (AMD) and central serous chorioretinopathy (CSCR), arise from dysfunctions in the highly vascular choroid layer in the eyeâ\euro™s posterior segment. Optical coherence tomography (OCT) images play a crucial role in diagnosing choroidal structural changes in clinical practice. This review emphasizes the significant efforts in developing precise detection, quantification, and automated disease classification of choroidal biomarkers. The rapid progress of artificial intelligence (AI) has triggered transformative breakthroughs across sectors including medical image analysis. Recently, the integration of AI within the diagnosis and treatment of choroidal diseases has captured significant attention. Multiple studies highlight AIâ\euro™s potential to enhance diagnostic precision and optimize clinical outcomes in this context. The review provides an extensive overview of AIâ\euro™s current applications in choroidal analysis using OCT imaging. It encompasses a diverse array of algorithms and techniques employed for biomarker detection, such as thickness and vascularity index, and for identifying diseases like AMD and CSCR. The overarching goal of this review is to provide an updated and comprehensive exploration of AIâ\euro™s impact on the choroid, highlighting its potential, challenges, and role in driving innovation in the field. Â

THIS WORK HAS BEEN SUBMITTED TO THE MACHINE LEARNING: SCIENCE AND TECHNOLOGY FOR POSSIBLE PUBLICATION. COPYRIGHT MAY BE TRANSFERRED WITHOUT NOTICE, AFTER WHICH THIS VERSION MAY NO LONGER BE ACCESSIBLE. The choroid, positioned behind the retina, nourishes the retina by supplying oxygen and nutrients. Choroidal structural changes are associated with severe vision-threatening conditions including age-related macular degeneration (AMD) and central serous chorioretinopathy (CSCR). Optical Coherence Tomography (OCT) imaging enables the visualization of choroidal changes, and clinicians rely on quantifying choroidal biomarkers through segmentation of the choroid layer in OCT scans for precise diagnosis and disease management. Accordingly, various attempts are made at automated choroid layer segmentation, however, their practicality is constrained by the limited and biased nature of training data. Privacy regulations hinder data aggregation, and supervised machine learning requires substantial annotated data. To tackle this, we propose an innovative image synthesis approach using generative adversarial networks (GANs). It involves a three-step process: generation of choroid-labeled B-scans using a standard GAN architecture, the transformation of these scans to unlabeled B-scans via Pix2Pix-GAN, and the training of a Pix2Pix-GAN choroid segmentation model using the synthesized data. To demonstrate the generalizability and efficacy, we evaluated the proposed choroid segmentation algorithm on the real B-scans from two different OCT imaging devices: enhanced depth imaging (EDI) and swept-source (SS) OCT, yielding Dice coefficient values of84.84% and 85.15%, respectively, buttressing its effective-ness. Further, qualitative performance analysis, including manual grading, confirms that the synthesized choroid-labeled images are distinct from real images, thus ensuring data privacy. The proposed methodology marks an initial step towards developing a comprehensive choroid layer quantification tool using synthetic images, and its adaptability makes it versatile for various medical image segmentation challenges.Â