人工智能在中心性浆液性脉络膜视网膜病变中的研究进展
Research Advances of Artificial Intelligence in Central Serous Chorioretinopathy
DOI: 10.12677/acm.2026.163817, PDF,   
作者: 夏玉佳：暨南大学附属爱尔眼科医院，广东 广州；暨南大学附属第一医院，广东 广州；马红婕*：暨南大学附属爱尔眼科医院，广东 广州
关键词: 中心性浆液性脉络膜视网膜病变；深度学习；光学相干断层扫描；多模态影像；生物标志物；预后预测；Central Serous Chorioretinopathy； Deep Learning； Optical Coherence Tomography； Multimodal Imaging； Biomarkers； Prognostic Prediction
摘要: 中心性浆液性脉络膜视网膜病变(Central Serous Chorioretinopathy, CSC)是常见的致视力损害黄斑疾病，其诊断与治疗监测高度依赖光学相干断层扫描(OCT)及其血管成像(OCTA)等多模态影像。近年来，人工智能(Artificial Intelligence, AI)尤其是深度学习，在医学影像自动分析方面进展迅速，为CSC的筛查、分型与疗效评估提供了新的技术路径。本文系统综述深度学习在CSC自动筛查、病灶分割与量化、疾病分期、疗效预测及预后评估中的研究现状，归纳常用模型、应用场景与验证结果，并讨论其外部验证、可解释性与临床转化等关键挑战，以期为后续研究与临床落地提供参考。
Abstract: Central serous chorioretinopathy (CSC) is a common vision-threatening macular disorder, and its diagnosis and treatment monitoring rely heavily on multimodal imaging, particularly optical coherence tomography (OCT) and OCT angiography (OCTA). In recent years, artificial intelligence, especially deep learning—has advanced rapidly in automated medical image analysis, providing new technical approaches for CSC screening, subtyping, and treatment-response assessment. This review systematically summarizes the current research on deep learning applications in CSC, including automated screening, lesion segmentation and quantification, disease staging, therapeutic response prediction, and prognostic evaluation. We synthesize commonly used models, clinical application scenarios, and validation results, and discuss key challenges such as external validation, model interpretability, and clinical translation, with the aim of informing future research and facilitating real-world implementation.
文章引用：夏玉佳, 马红婕. 人工智能在中心性浆液性脉络膜视网膜病变中的研究进展[J]. 临床医学进展, 2026, 16(3): 507-515. https://doi.org/10.12677/acm.2026.163817

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