Abstract
PURPOSE: To synthesize evidence on deep learning applications for diagnosing central serous chorioretinopathy (CSCR), a macular disorder associated with vision loss, this systematic review categorized studies by diagnostic task and imaging modality. The study evaluates advances in deep learning performance, clinical integration potential, dataset limitations, and the contributions of multimodal imaging and Explainable AI (XAI) to diagnostic accuracy and clinical decision-making. METHODS: We conducted a PRISMA-compliant systematic review of PubMed, Scopus, and IEEE Xplore, including peer-reviewed English-language studies published from January 1990 to February 2024 that reported quantitative deep learning metrics for CSCR diagnosis. A two-stage selection process was applied (Cohen's κ = 0.84), resulting in 96 studies for analysis. Risk of bias was evaluated using the QUADAS-2 tool, and data were synthesized by imaging modality, model architecture, and diagnostic task. RESULTS: Deep learning models demonstrate exceptional performance in CSCR diagnosis. DenseNet architectures applied to optical coherence tomography (OCT) images achieved peak Metrics, including 99.78% accuracy, 99.68% sensitivity, and 100% specificity. Segmentation models for subretinal fluid (SRF) reported Dice scores of up to 0.965, while multimodal models for differential diagnosis achieved an area under the curve (AUC) of 0.999. Despite these advances, clinical adoption remains limited by several challenges: scarce and imbalanced datasets (e.g., SRF/non-SRF ratio of 1:8), lack of open-access datasets and models, risks of overfitting, and insufficient external validation. Emerging approaches, such as few-shot learning and diffusion models, are promising for mitigating data constraints; however, improvements in dataset quality and the implementation of rigorous cross-institutional validation are essential for real-world deployment. CONCLUSIONS: By leveraging OCT and multimodal imaging data, deep learning has the potential to transform CSCR diagnosis through enhanced accuracy and automation. However, translating these advances into routine clinical practice necessitates overcoming key challenges, including limited and heterogeneous datasets and models with restricted generalizability. Future research should prioritize standardized reporting frameworks, transparent model interpretability through XAI, and rigorous large-scale validation. Essential strategies include employing federated learning to leverage distributed data, implementing effective multimodal fusion techniques, and fostering collaborative frameworks to improve diagnostic accuracy, ensure algorithmic fairness, and enable real-world clinical applicability.