Analysis of optical coherence tomography biomarker probability detection in central serous chorioretinopathy by using an artificial intelligence-based biomarker detector.

Lorenzo Ferro Desideri, Rodrigo Anguita, Lieselotte E Berger, Helena M A Feenstra, Davide Scandella, Raphael Sznitman, Camiel J F Boon, Elon H C van Dijk, Martin S Zinkernagel
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Abstract

Aim: 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).

Methods: 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.

Results: 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).

Conclusion: 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.

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利用基于人工智能的生物标记检测器分析中心性浆液性脉络膜视网膜病变的光学相干断层扫描生物标记概率检测。
目的:采用基于人工智能(AI)光学相干断层扫描(OCT)的新型程序,确定是否存在与中心性浆液性脉络膜视网膜病变(CSC)相关的生物标记物,以及这些标记物能否区分急性和慢性中心性浆液性脉络膜视网膜病变(aCSC 和 cCSC):多中心观察性研究,采用回顾性设计,招募未经治疗的 aCSC 和 cCSC 患者。aCSC和cCSC的诊断是通过多模态成像确定的,本次研究还考虑了后续随访。基线OCT由一个基于人工智能的平台(Discovery® OCT Fluid and Biomarker Detector,RetinAI AG,瑞士)进行分析。该软件可在每一次 OCT 扫描中检测多种不同的生物标记物,包括视网膜下积液(SRF)、视网膜内积液(IRF)、高反射灶(HF)和扁平不规则色素上皮脱落(FIPED)。SRF 的存在被视为进行生物标记分析的必要纳入标准,没有 SRF 存在的 OCT 片被排除在分析之外:共有 144 名 CSC 患者的 160 只眼睛入选,其中 100 只(62.5%)被诊断为 cCSC,60 只(34.5%)被诊断为 aCSC。在显示存在 SRF 的 OCT 切片中,发现在 aCSC 和 cCSC 中,HF 和 FIPED 的生物标志物与临床相关(> 50%)。HF在cCSC组的平均比例为81%(±20),在aCSC组为81%(±15)(p = 0.4295);FIPED在cCSC组的平均比例为88%(±18),在aCSC组为89%(±15)(p = 0.3197):结论:我们的研究表明,如果基线存在HF和FIPED,它们是与CSC正相关的OCT生物标志物。虽然HF和FIPED生物标志物有助于CSC诊断,但它们无法在首次就诊时区分aCSC和cCSC。人工智能辅助生物标志物检测有望减少有创成像需求,但还需要通过纵向研究进一步验证。
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来源期刊
CiteScore
3.50
自引率
4.30%
发文量
81
审稿时长
19 weeks
期刊介绍: International Journal of Retina and Vitreous focuses on the ophthalmic subspecialty of vitreoretinal disorders. The journal presents original articles on new approaches to diagnosis, outcomes of clinical trials, innovations in pharmacological therapy and surgical techniques, as well as basic science advances that impact clinical practice. Topical areas include, but are not limited to: -Imaging of the retina, choroid and vitreous -Innovations in optical coherence tomography (OCT) -Small-gauge vitrectomy, retinal detachment, chromovitrectomy -Electroretinography (ERG), microperimetry, other functional tests -Intraocular tumors -Retinal pharmacotherapy & drug delivery -Diabetic retinopathy & other vascular diseases -Age-related macular degeneration (AMD) & other macular entities
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