Ferro Desideri Lorenzo, Anguita Rodrigo, Berger Lieselotte E, Feenstra Helena M A, Scandella Davide, Sznitman Raphael, Boon Camiel J F, van Dijk Elon H C, Zinkernagel Martin S
Department of Ophthalmology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 15, Bern, CH-3010, Switzerland.
Department for Bio Medical Research, University of Bern, Murtenstrasse 24, Bern, CH-3008, Switzerland.
Int J Retina Vitreous. 2024 May 31;10(1):42. doi: 10.1186/s40942-024-00560-6.
To adopt a novel artificial intelligence (AI) optical coherence tomography (OCT)-based program to identify the presence of biomarkers associated with central serous chorioretinopathy (CSC) and whether these can differentiate between acute and chronic central serous chorioretinopathy (aCSC and cCSC).
Multicenter, observational study with a retrospective design enrolling treatment-naïve patients with aCSC and cCSC. The diagnosis of aCSC and cCSC was established with multimodal imaging and for the current study subsequent follow-up visits were also considered. Baseline OCTs were analyzed by an AI-based platform (Discovery® OCT Fluid and Biomarker Detector, RetinAI AG, Switzerland). This software allows to detect several different biomarkers in each single OCT scan, including subretinal fluid (SRF), intraretinal fluid (IRF), hyperreflective foci (HF) and flat irregular pigment epithelium detachment (FIPED). The presence of SRF was considered as a necessary inclusion criterion for performing biomarker analysis and OCT slabs without SRF presence were excluded from the analysis.
Overall, 160 eyes of 144 patients with CSC were enrolled, out of which 100 (62.5%) eyes were diagnosed with cCSC and 60 eyes (34.5%) with aCSC. In the OCT slabs showing presence of SRF the presence of biomarkers was found to be clinically relevant (> 50%) for HF and FIPED in aCSC and cCSC. HF had an average percentage of 81% (± 20) in the cCSC group and 81% (± 15) in the aCSC group (p = 0.4295) and FIPED had a mean percentage of 88% (± 18) in cCSC vs. 89% (± 15) in the aCSC (p = 0.3197).
We demonstrate that HF and FIPED are OCT biomarkers positively associated with CSC when present at baseline. While both HF and FIPED biomarkers could aid in CSC diagnosis, they could not distinguish between aCSC and cCSC at the first visit. AI-assisted biomarker detection shows promise for reducing invasive imaging needs, but further validation through longitudinal studies is needed.
采用一种基于新型人工智能(AI)光学相干断层扫描(OCT)的程序,以识别与中心性浆液性脉络膜视网膜病变(CSC)相关的生物标志物的存在情况,以及这些生物标志物是否能够区分急性和慢性中心性浆液性脉络膜视网膜病变(aCSC和cCSC)。
采用多中心、回顾性观察研究,纳入未经治疗的aCSC和cCSC患者。aCSC和cCSC的诊断通过多模态成像确定,在本研究中,后续的随访也纳入考虑。基线OCT图像由一个基于AI的平台(Discovery® OCT Fluid and Biomarker Detector,瑞士RetinAI AG公司)进行分析。该软件能够在每次单独的OCT扫描中检测几种不同的生物标志物,包括视网膜下液(SRF)、视网膜内液(IRF)、高反射灶(HF)和扁平不规则色素上皮脱离(FIPED)。SRF的存在被视为进行生物标志物分析的必要纳入标准,不存在SRF的OCT层面被排除在分析之外。
总体而言,纳入了144例CSC患者的160只眼,其中100只眼(62.5%)被诊断为cCSC,60只眼(34.5%)为aCSC。在显示存在SRF的OCT层面中,发现生物标志物的存在在aCSC和cCSC中具有临床相关性(>50%),对于HF和FIPED而言。HF在cCSC组中的平均百分比为81%(±20),在aCSC组中为81%(±15)(p = 0.4295),FIPED在cCSC中的平均百分比为88%(±18),在aCSC中为89%(±15)(p = 0.3197)。
我们证明,当HF和FIPED在基线时存在时,它们是与CSC呈正相关的OCT生物标志物。虽然HF和FIPED这两种生物标志物都有助于CSC的诊断,但在初次就诊时它们无法区分aCSC和cCSC。AI辅助的生物标志物检测显示出减少侵入性成像需求的前景,但需要通过纵向研究进行进一步验证。