Xiao Peng, Ma Ke, Ye Xiaoyuan, Wang Gengyuan, Duan Zhengyu, Huang Yuancong, Luo Zhongzhou, Hu Xiaoqing, Chi Wei, Yuan Jin
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
Front Bioeng Biotechnol. 2023 May 2;11:1086347. doi: 10.3389/fbioe.2023.1086347. eCollection 2023.
Vogt-Koyanagi-Harada (VKH) disease is a common and easily blinded uveitis entity, with choroid being the main involved site. Classification of VKH disease and its different stages is crucial because they differ in clinical manifestations and therapeutic interventions. Wide-field swept-source optical coherence tomography angiography (WSS-OCTA) provides the advantages of non-invasiveness, large-field-of-view, high resolution, and ease of measuring and calculating choroid, offering the potential feasibility of simplified VKH classification assessment based on WSS-OCTA. 15 healthy controls (HC), 13 acute-phase and 17 convalescent-phase VKH patients were included, undertaken WSS-OCTA examination with a scanning field of 15 × 9 mm. 20 WSS-OCTA parameters were then extracted from WSS-OCTA images. To classify HC and VKH patients in acute and convalescent phases, two 2-class VKH datasets (HC and VKH) and two 3-class VKH datasets (HC, acute-phase VKH, and convalescent-phase VKH) were established by the WSS-OCTA parameters alone or in combination with best-corrected visual acuity (logMAR BCVA) and intraocular pressure (IOP), respectively. A new feature selection and classification method that combines an equilibrium optimizer and a support vector machine (called SVM-EO) was adopted to select classification-sensitive parameters among the massive datasets and to achieve outstanding classification performance. The interpretability of the VKH classification models was demonstrated based on SHapley Additive exPlanations (SHAP). Based on pure WSS-OCTA parameters, we achieved classification accuracies of 91.61% ± 12.17% and 86.69% ± 8.30% for 2- and 3-class VKH classification tasks. By combining the WSS-OCTA parameters and logMAR BCVA, we achieved better classification performance of 98.82% ± 2.63% and 96.16% ± 5.88%, respectively. Through SHAP analysis, we found that logMAR BCVA and vascular perfusion density (VPD) calculated from the whole field of view region in the choriocapillaris (whole FOV CC-VPD) were the most important features for VKH classification in our models. We achieved excellent VKH classification performance based on a non-invasive WSS-OCTA examination, which provides the possibility for future clinical VKH classification with high sensitivity and specificity.
伏格特-小柳-原田（VKH）病是一种常见且易致盲的葡萄膜炎类型，脉络膜是主要受累部位。VKH病及其不同阶段的分类至关重要，因为它们在临床表现和治疗干预方面存在差异。广角扫频源光学相干断层扫描血管造影（WSS-OCTA）具有非侵入性、大视野、高分辨率以及易于测量和计算脉络膜的优点，为基于WSS-OCTA进行简化的VKH分类评估提供了潜在的可行性。纳入了15名健康对照者（HC）、13名急性期和17名恢复期VKH患者，采用15×9毫米的扫描视野进行WSS-OCTA检查。然后从WSS-OCTA图像中提取20个WSS-OCTA参数。为了对HC以及急性期和恢复期的VKH患者进行分类，分别仅通过WSS-OCTA参数或结合最佳矫正视力（logMAR BCVA）和眼压（IOP）建立了两个2类VKH数据集（HC和VKH）以及两个3类VKH数据集（HC、急性期VKH和恢复期VKH）。采用一种将平衡优化器和支持向量机相结合的新特征选择和分类方法（称为SVM-EO），在大量数据集中选择对分类敏感的参数，并实现出色的分类性能。基于夏普利值加法解释（SHAP）证明了VKH分类模型的可解释性。基于单纯的WSS-OCTA参数，我们在2类和3类VKH分类任务中分别实现了91.61%±12.17%和86.69%±8.30%的分类准确率。通过结合WSS-OCTA参数和logMAR BCVA，我们分别实现了更好的分类性能，即98.82%±2.63%和96.16%±5.88%。通过SHAP分析，我们发现logMAR BCVA和从脉络膜毛细血管全视野区域计算得出的血管灌注密度（VPD）（全视野CC-VPD）是我们模型中VKH分类最重要的特征。我们基于非侵入性的WSS-OCTA检查实现了出色的VKH分类性能，这为未来临床以高灵敏度和特异性进行VKH分类提供了可能性。
