MACHINE LEARNING TO ASSESS THE CLINICAL SIGNIFICANCE OF VITREOUS FLOATERS

Particular embodiments disclosed herein provide a method for training a machine learning model to estimate the clinical significance of floaters in a patient's eye. One or more images, such as SLO images or en face retinal OCT images, are evaluated to identify shaded regions corresponding to floaters. The shaded regions are measured and the measurements processed using a machine learning model to obtain an estimated significance. The machine learning model is then updated according to a comparison of the estimated significance to a human-assigned clinical significance. The machine learning model may additionally or alternatively be updated by evaluating the estimated category with respect to visibility threshold data, such as one or more visibility threshold surfaces defined with respect to two or more variables.