Department of Ophthalmology, University of Lausanne, Jules Gonin Eye Hospital, Fondation Asile des Aveugles, Avenue de France 15, 1001, Lausanne, Switzerland.
Platform for Research in Ocular Imaging, Department of Ophthalmology, University of Lausanne, Jules Gonin Eye Hospital, Fondation Asile des Aveugles, Lausanne, Switzerland.
Sci Rep. 2024 Oct 13;14(1):23940. doi: 10.1038/s41598-024-75275-7.
Central serous chorioretinopathy (CSCR) is a retinal disease characterised by the accumulation of subretinal fluid, which often resolves spontaneously in acute cases. However, approximately one-third of patients experience recurrences that may cause severe and irreversible vision. This study aimed to identify parameters derived from optical coherence tomography (OCT) that are associated with CSCR recurrence. Our dataset included 5211 OCT scans from 344 eyes of 255 patients diagnosed with CSCR. 178 eyes were identified as recurrent, 109 as non-recurrent, and 57 were excluded. We extracted parameters using artificial intelligence algorithms based on U-Nets, convolutional kernels, and morphological operators. We applied inferential statistics to evaluate differences between the recurrent and non-recurrent groups, and we used a logistic regression predictive model, reporting the coefficients as a measure of biomarker importance. We identified nine predictive biomarkers for CSCR recurrence: age, intraretinal fluid, subretinal fluid, pigment epithelial detachments, choroidal vascularity index, integrity of photoreceptors and retinal pigment epithelium layer, choriocapillaris and choroidal stroma thickness, and thinning of the outer nuclear layer, and of the inner nuclear layer combined with the outer plexiform layer. These results could enable future developments in the automatic detection of CSCR recurrence, paving the way for translational medical applications.
中心性浆液性脉络膜视网膜病变(CSCR)是一种以视网膜下液积聚为特征的视网膜疾病,在急性病例中常自行消退。然而,约三分之一的患者会出现复发,可能导致严重且不可逆转的视力丧失。本研究旨在确定与 CSCR 复发相关的光学相干断层扫描(OCT)参数。我们的数据集包括来自 255 名患者的 344 只眼中的 5211 次 OCT 扫描。178 只眼被确定为复发,109 只眼为非复发,57 只眼被排除。我们使用基于 U-Nets、卷积核和形态运算符的人工智能算法提取参数。我们应用推理统计学来评估复发组和非复发组之间的差异,并使用逻辑回归预测模型,报告系数作为生物标志物重要性的衡量标准。我们确定了九个用于 CSCR 复发的预测生物标志物:年龄、视网膜内液、视网膜下液、色素上皮脱离、脉络膜血管指数、光感受器和视网膜色素上皮层的完整性、脉络膜毛细血管和脉络膜基质厚度以及外核层和内核层的变薄与外丛状层相结合。这些结果可能为 CSCR 复发的自动检测的未来发展铺平道路,为转化医学应用开辟道路。