Classification of Vogt-Koyanagi-Harada disease using feature selection and classification based on wide-field swept-source optical coherence tomography angiography - Xiao, Peng; Ma, Ke; Ye, Xiaoyuan; Wang, Gengyuan; Duan, Zhengyu; Huang, Yuancong; Luo, Zhongzhou; Hu, Xiaoqing; Chi, Wei; Yuan, Jin (Frontiers in Bioengineering and Biotechnology, 2023)
Background: 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. Methods: 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 mm2. 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 were demonstrated based on SHapley Additive exPlanations (SHAP). Results: 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 task. 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 choriocapillaris (whole FOV CC-VPD) were the most important features for VKH classification in our models. Conclusions: We achieved the excellent VKH classification performance based on non-invasive WSS-OCTA examination, which provides the possibility for future clinical VKH classification with high sensitivity and specificity.