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