Advances in Deep Learning Applications for Choroidal Images Analysis: A Narrative Review

ABSTRACT The choroid, a critical vascular structure in the eye, is closely associated with a wide range of ocular diseases, and its early and accurate evaluation is fundamental to effective diagnosis and management. Advances in imaging modalities like optical coherence tomography (OCT) have revolutionised choroidal visualisation, yet manual analysis remains labour‐intensive and subjective. This review synthesizes recent developments in deep learning (DL)‐driven solutions for choroidal imaging. DL architectures, including convolutional neural networks (CNNs), U‐Net variants and vision transformers, demonstrate high accuracy in automating choroidal sublayer segmentation, 3D vascular network reconstruction and biomarker extraction, outperforming traditional methods in speed and reproducibility. Innovations such as hybrid loss functions, attention mechanisms, and generative adversarial networks address challenges like class imbalance and boundary ambiguity. At the same time, multimodal DL models integrating OCT, fundus photography and angiography enhance diagnostic precision. However, limitations persist, including data dependency, annotation variability and the lack of standardised protocols for sublayer demarcation. Future directions emphasise image enhancement techniques, self‐supervised learning to reduce annotation burdens and ethical frameworks for clinical translation.