To study whether it is possible to differentiate intermediate age-related macular degeneration (AMD) from healthy controls using partial optical coherence tomography (OCT) data, that is, restricting the input B-scans to certain pre-defined regions of interest (ROIs). A total of 15744 B-scans from 269 intermediate AMD patients and 115 normal subjects were used in this study (split on subject level in 80% train, 10% validation and 10% test). From each OCT B-scan, three ROIs were extracted: retina, complex between retinal pigment epithelium (RPE) and Bruch membrane (BM), and choroid (CHO). These ROIs were obtained using two different methods: masking and cropping. In addition to the six ROIs, the whole OCT B-scan and the binary mask corresponding to the segmentation of the RPE-BM complex were used. For each subset, a convolutional neural network (based on VGG16 architecture and pre-trained on ImageNet) was trained and tested. The performance of the models was evaluated using the area under the receiver operating characteristic (AUROC), accuracy, sensitivity, and specificity. All trained models presented an AUROC, accuracy, sensitivity, and specificity equal to or higher than 0.884, 0.816, 0.685, and 0.644, respectively. The model trained on the whole OCT B-scan presented the best performance (AUROC = 0.983, accuracy = 0.927, sensitivity = 0.862, specificity = 0.913). The models trained on the ROIs obtained with the cropping method led to significantly higher outcomes than those obtained with masking, with the exception of the retinal tissue, where no statistically significant difference was observed between cropping and masking (p = 0.47). This study demonstrated that while using the complete OCT B-scan provided the highest accuracy in classifying intermediate AMD, models trained on specific ROIs such as the RPE-BM complex or the choroid can still achieve high performance.

翻译：本研究旨在探讨利用部分光学相干断层扫描（optical coherence tomography, OCT）数据（即仅将输入B扫描限制在特定预定义感兴趣区域（ROIs）内）区分中期年龄相关性黄斑变性（intermediate age-related macular degeneration, AMD）与健康对照的可行性。研究纳入269例中期AMD患者与115例正常受试者的共15744张B扫描图像（按受试者层级分为80%训练集、10%验证集与10%测试集）。从每张OCT B扫描中提取三种ROIs：视网膜、视网膜色素上皮（retinal pigment epithelium, RPE）与Bruch膜（BM）复合体、脉络膜（CHO）。这些ROIs采用掩膜与裁剪两种方法获取。除六种ROIs外，还使用了完整OCT B扫描及对应的RPE-BM复合体分割二值掩膜。针对每个子集，训练并测试了基于VGG16架构（预训练于ImageNet）的卷积神经网络模型。采用受试者工作特征曲线下面积（AUROC）、准确率、敏感性和特异性评估模型性能。所有训练模型的AUROC、准确率、敏感性和特异性分别不低于0.884、0.816、0.685和0.644。基于完整OCT B扫描训练的模型表现最佳（AUROC=0.983，准确率=0.927，敏感性=0.862，特异性=0.913）。除视网膜组织外（裁剪与掩膜无统计学显著差异，p=0.47），采用裁剪方法获取ROIs训练的模型性能显著优于掩膜方法。本研究证明，虽然使用完整OCT B扫描分类中期AMD的准确率最高，但基于特定ROIs（如RPE-BM复合体或脉络膜）训练的模型仍能达到较高性能。