Abstract：
Cystoid macular edema (CME) is a sight-threatening condition often associated with inflammatory and diabetic diseases. Early detection is crucial to prevent irreversible vision loss. Artificial intelligence (AI) has shown promise in automating CME diagnosis through optical coherence tomography (OCT) imaging, but its utility needs critical evaluation. This systematic review assesses the application of AI to diagnosis CME, specifically focusing on disorders like postoperative CME (Irvine Gass syndrome) and retinitis pigmentosa without obvious vasculopathy, using OCT imaging. A comprehensive search was conducted across 6 databases (PubMed, Scopus, Web of Science, Wiley, ScienceDirect, and IEEE) from 2018 to November, 2023. Twenty-three articles met the inclusion criteria and were selected for in-depth analysis. We evaluate AI\'s role in CME diagnosis and its performance in \"detection\", \"classification\" and \"segmentation\" of OCT retinal images. We found that convolutional neural network (CNN)-based methods consistently outperformed other machine learning techniques, achieving an average accuracy of over 96% in detecting and identifying CME from OCT images. Despite certain limitations such as dataset size and ethical concerns, the synergy between AI and OCT, particularly through CNNs, holds promise for significantly advancing CME diagnostics.
摘要：
囊样黄斑水肿（CME）是一种威胁视力的疾病，通常与炎症和糖尿病相关。早期检测对于防止不可逆的视力丧失至关重要。人工智能（AI）已显示出通过光学相干断层扫描（OCT）成像自动化CME诊断的前景。但是它的效用需要严格的评估。这篇系统综述评估了人工智能在诊断CME中的应用，特别关注疾病，如术后CME（IrvineGass综合征）和视网膜色素变性无明显血管病变，使用OCT成像。在6个数据库中进行了全面搜索(PubMed，Scopus,WebofScience,威利,ScienceDirect,和IEEE）从2018年到11月，2023年。23篇文章符合纳入标准，并被选中进行深入分析。我们评估AI在CME诊断中的作用及其在“检测”中的表现，OCT视网膜图像的“分类”和“分割”。我们发现，基于卷积神经网络(CNN)的方法始终优于其他机器学习技术，从OCT图像中检测和识别CME的平均准确率超过96%。尽管存在某些限制，如数据集大小和道德问题，人工智能和OCT之间的协同作用，特别是通过CNN，有望显著推进CME诊断。
