Valsecchi Nicola, Sadeghi Elham, Davis Elli, Ibrahim Mohammed Nasar, Hasan Nasiq, Bollepalli Sandeep Chandra, Singh Sumit Randhir, Fontana Luigi, Sahel Jose Alain, Vupparaboina Kiran Kumar, Chhablani Jay
Department of Ophthalmology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Ophthalmology Unit, Dipartimento di Scienze Mediche e Chirurgiche, Alma Mater Studiorum University of Bologna, Bologna, Italy.
Sci Rep. 2025 Jan 3;15(1):714. doi: 10.1038/s41598-025-85189-7.
To assess the choroidal vessels in healthy eyes using a novel three-dimensional (3D) deep learning approach. In this cross-sectional retrospective study, swept-source OCT 6 × 6 mm scans on Plex Elite 9000 device were obtained. Automated segmentation of the choroidal layer was achieved using a deep-learning ResUNet model along with a volumetric smoothing approach. Phansalkar thresholding was employed to binarize the choroidal vasculature. The choroidal vessels were visualized in 3D maps, and divided into five sectors: nasal, temporal, superior, inferior, and central. Choroidal thickness (CT) and choroidal vascularity index (CVI) of the whole volumes were calculated using the automated software. The three vessels for each sector were measured, to obtain the mean choroidal vessel diameter (MChVD). The inter-vessel distance (IVD) was defined as the distance between the vessel and the nearest non-collateral vessel. The choroidal biomarkers obtained were compared between different age groups (18 to 34 years old, 35 to 59 years old, and ≥ 60) and sex. Linear mixed models and univariate analysis were used for statistical analysis. A total of 80 eyes of 53 patients were included in the analysis. The mean age of the patients was 44.7 ± 18.5 years, and 54.7% were females. Overall, 44 eyes of 29 females and 36 eyes of 24 males were included in the study. We observed that 33% of the eyes presented at least one choroidal vessel larger than 200 μm crossing the central 3000 μm of the macula. Also, we observed a significant decrease in mean CVI with advancing age (p < 0.05), whereas no significant changes in mean MChVD and IVD were observed (p > 0.05). Furthermore, CVI was increased in females compared to males in each sector, with a significant difference in the temporal sector (p < 0.05). MChVD and IVD did not show any changes with increasing age, whereas CVI decreased with increasing age. Also, CVI was increased in healthy females compared to males. The 3D assessment of choroidal vessels using a deep-learning approach represents an innovative, non-invasive technique for investigating choroidal vasculature, with potential applications in research and clinical practice.
使用一种新型的三维（3D）深度学习方法评估健康眼睛的脉络膜血管。在这项横断面回顾性研究中，在Plex Elite 9000设备上获得了6×6毫米的扫频源光学相干断层扫描（swept-source OCT）图像。使用深度学习ResUNet模型和体积平滑方法实现脉络膜层的自动分割。采用Phansalkar阈值法对脉络膜血管进行二值化处理。脉络膜血管在三维地图中可视化，并分为五个扇区：鼻侧、颞侧、上方、下方和中央。使用自动化软件计算整个体积的脉络膜厚度（CT）和脉络膜血管指数（CVI）。测量每个扇区的三根血管，以获得平均脉络膜血管直径（MChVD）。血管间距离（IVD）定义为血管与最近的非侧支血管之间的距离。比较不同年龄组（18至34岁、35至59岁和≥60岁）和性别之间获得的脉络膜生物标志物。采用线性混合模型和单变量分析进行统计分析。分析共纳入53例患者的80只眼睛。患者的平均年龄为44.7±18.5岁，女性占54.7%。总体而言，研究纳入了29名女性的44只眼睛和24名男性的36只眼睛。我们观察到，33%的眼睛至少有一根大于200μm的脉络膜血管穿过黄斑中心3000μm区域。此外，我们观察到平均CVI随着年龄增长而显著降低（p<0.05），而平均MChVD和IVD未观察到显著变化（p>0.05）。此外，每个扇区中女性的CVI均高于男性，颞侧扇区存在显著差异（p<0.05）。MChVD和IVD未随年龄增长而出现任何变化，而CVI随年龄增长而降低。此外，健康女性的CVI高于男性。使用深度学习方法对脉络膜血管进行三维评估是一种创新的、非侵入性的研究脉络膜血管系统的技术，在研究和临床实践中具有潜在应用价值。
Zotero 插件
