Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review - Ping Zhang; Qing Zhang; Xinya Hu; Wei Chi; Weihua Yang (Ophthalmology and Therapy)
Abstract Introduction This review synthesizes advancements in artificial intelligence (AI) applications for central serous chorioretinopathy (CSCR), analyzing challenges and outlining future research directions to guide personalized diagnostic and therapeutic strategies. Methods A systematic literature search was conducted in the Web of Science database using a comprehensive AI-related keyword set (e.g., “deep learning,” “neural networks,” “computer vision”) combined with “central serous chorioretinopathy.” The search yielded 698 records, with 73 original research studies selected after excluding reviews and non-empirical work based on predefined criteria. Results The application of AI in CSCR has progressed from disease classification to dynamic prognostic prediction, leveraging multimodal data fusion (e.g., optical coherence tomography [OCT] and OCT angiography [OCTA] and fundus fluorescein angiography [FFA]) to enhance both qualitative and quantitative diagnostic accuracy. AI models outperform clinical experts in classifying retinal disease subtypes and segmenting lesions. However, clinical translation faces infrastructural barriers (e.g., incompatible PACS systems) and limited physician trust due to “black box” decision-making. New approaches, such as explainable AI (XAI), are being integrated to enhance the transparency and clinical applicability of AI models. Key limitations involve single-center data dependency, interobserver annotation variability, and the inability of static frameworks to capture dynamic lesion progression. Conclusion AI enhances CSCR diagnosis and subtyping efficiency. To optimize clinical translation, future research should focus on multicenter data integration, dynamic visualization frameworks, and standardized guidelines, promoting interdisciplinary collaboration and prospective trials for personalized treatment strategies. Incorporating federated learning for privacy-preserving data sharing and prioritizing explainability will be essential to overcoming barriers to physician adoption and improving trust in AI-driven clinical decision-making. Future efforts should focus on creating dynamic systems that provide real-time insights into lesion progression and integrating them with standardized protocols for wider clinical adoption.