METHOD AND SYSTEM FOR DISEASE ANALYSIS AND INTERPRETATION

摘要

Optical coherence tomography (OCT) data can be analyzed with neural networks trained on OCT data and known clinical outcomes to make more accurate predictions about the development and progression of retinal diseases, central nervous system disorders, and other conditions. The methods take 2D or 3D OCT data derived from different light source configurations and analyze it with neural networks that are trained on OCT images correlated with known clinical outcomes to identify intensity distributions or patterns indicative of different retina conditions. The methods have greater predictive power than traditional OCT analysis because the invention recognizes that subclinical physical changes affect how light interacts with the tissue matter of the retina, and these intensity changes in the image can be distinguishable by a neural network that has been trained on imaging data of retinas.