Song Xian, Xu Qian, Li Haiming, Fan Qian, Zheng Yefeng, Zhang Qiang, Chu Chunyan, Zhang Zhicheng, Yuan Chenglang, Ning Munan, Bian Cheng, Ma Kai, Qu Yi
Department of Geriatrics, Qilu Hospital of Shandong University, Jinan, China.
Tencent Healthcare, Shenzhen, China.
Front Neurosci. 2022 Aug 18;16:952735. doi: 10.3389/fnins.2022.952735. eCollection 2022.
Using deep learning (DL)-based technique, we identify risk factors and create a prediction model for refractory neovascular age-related macular degeneration (nAMD) characterized by persistent disease activity (PDA) in spectral domain optical coherence tomography (SD-OCT) images.
A total of 671 typical B-scans were collected from 186 eyes of 186 patients with nAMD. Spectral domain optical coherence tomography images were analyzed using a classification convolutional neural network (CNN) and a fully convolutional network (FCN) algorithm to extract six features involved in nAMD, including ellipsoid zone (EZ), external limiting membrane (ELM), intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelium detachment (PED), and subretinal hyperreflective material (SHRM). Random forest models were probed to predict 1-year disease activity (stable, PDA, and cured) based on the quantitative features computed from automated segmentation and evaluated with cross-validation.
The algorithm to segment six SD-OCT features achieved the mean accuracy of 0.930 (95% CI: 0.916-0.943), dice coefficients of 0.873 (95% CI: 0.847-0.899), a sensitivity of 0.873 (95% CI: 0.844-0.910), and a specificity of 0.922 (95% CI: 0.905-0.940). The six-metric model including EZ and ELM achieved the optimal performance to predict 1-year disease activity, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.980, the accuracy of 0.930, the sensitivity of 0.920, and the specificity of 0.962. The integrity of EZ and ELM significantly improved the performance of the six-metric model than that of the four-metric model.
The prediction model reveals the potential to predict PDA in nAMD eyes. The integrity of EZ and ELM constituted the strongest predictive factor for PDA in nAMD eyes in real-world clinical practice. The results of this study are a significant step toward image-guided prediction of long-term disease activity in the management of nAMD and highlight the importance of the automatic identification of photoreceptor layers.
运用基于深度学习（DL）的技术，我们识别危险因素并创建一个针对难治性新生血管性年龄相关性黄斑变性（nAMD）的预测模型，该疾病在光谱域光学相干断层扫描（SD - OCT）图像中表现为持续性疾病活动（PDA）。
从186例nAMD患者的186只眼中共收集了671幅典型B扫描图像。使用分类卷积神经网络（CNN）和全卷积网络（FCN）算法分析光谱域光学相干断层扫描图像，以提取nAMD涉及的六个特征，包括椭圆体带（EZ）、外界膜（ELM）、视网膜内液（IRF）、视网膜下液（SRF）、色素上皮脱离（PED）和视网膜下高反射物质（SHRM）。基于自动分割计算出的定量特征，探究随机森林模型以预测1年疾病活动情况（稳定、PDA和治愈），并通过交叉验证进行评估。
用于分割六个SD - OCT特征的算法平均准确率达到0.930（95%置信区间：0.916 - 0.943），骰子系数为0.873（95%置信区间：0.847 - 0.899），灵敏度为0.873（95%置信区间：0.844 - 0.910），特异性为0.922（95%置信区间：0.905 - 0.940）。包含EZ和ELM的六指标模型在预测1年疾病活动方面表现最优，受试者操作特征（ROC）曲线下面积（AUC）为0.980，准确率为0.930，灵敏度为0.920，特异性为0.962。与四指标模型相比，EZ和ELM的完整性显著提高了六指标模型的性能。
该预测模型显示出预测nAMD眼中PDA的潜力。在实际临床实践中，EZ和ELM的完整性构成了nAMD眼中PDA最强的预测因素。本研究结果是朝着在nAMD管理中进行图像引导的长期疾病活动预测迈出的重要一步，并突出了自动识别光感受器层的重要性。
