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：为探究是否可以通过部分光学相干断层扫描（OCT）数据区分中间年龄相关性黄斑变性（AMD）与健康对照组，即限制输入B扫描至特定预定义感兴趣区域（ROIs）。本研究共纳入来自269名中间AMD患者和115名正常受试者的15744张B扫描图像（按受试者水平分为80%训练集、10%验证集和10%测试集）。从每张OCT B扫描中提取三个ROI：视网膜、视网膜色素上皮（RPE）与布鲁赫膜（BM）复合体，以及脉络膜（CHO）。这些ROI通过两种方法获取：掩膜法和裁剪法。除上述六个ROI外，还使用完整OCT B扫描及对应RPE-BM复合体分割的二值掩膜。针对每个子集，基于VGG16架构并在ImageNet上预训练的卷积神经网络进行了训练和测试。模型性能通过受试者工作特征曲线下面积（AUROC）、准确率、灵敏度和特异度进行评估。所有训练模型的AUROC、准确率、灵敏度和特异度分别≥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优（AUROC=0.983，准确率=0.927，灵敏度=0.862，特异度=0.913）。采用裁剪法获取ROI训练的模型效果显著优于掩膜法，但视网膜组织除外：裁剪法与掩膜法在该区域无统计学显著差异（p=0.47）。本研究证实，虽然使用完整OCT B扫描对中间AMD分类的准确率最高，但基于特定ROI（如RPE-BM复合体或脉络膜）训练的模型仍可实现高分类性能。