ABSTRACT
Optical coherence tomography (OCT) images semi-transparent tissues noninvasively. Relying on backscatter and interferometry to calculate spatial relationships, OCT shares similarities with other pulse-echo modalities. There is considerable interest in using machine learning techniques for automated image classification, particularly among ophthalmologists who rely heavily on diagnostic OCT. Artificial neural networks (ANN) consist of interconnected nodes and can be employed as classifiers after training on large datasets. Conventionally, OCT scans are rendered as 2D or 3D human-readable images of which the smallest depth-resolved unit is the amplitude-scan reflectivity-function profile which is difficult for humans to interpret. We set out to determine whether amplitude-scan reflectivity-function profiles representing disease signatures could be distinguished and classified by a feed-forward ANN. Our classifier achieved high accuracies after training on only 24 eyes, with evidence of good generalization on unseen data. The repertoire of our classifier can now be expanded to include rare and unseen diseases and can be extended to other disciplines and industries.
