Predicting necessary treatment plans for patients with Age-Related Macular Degeneration using characteristics derived from optical coherence tomography B-scans

Age-Related Macular Degeneration (AMD) represents one of the leading causes of blindness for individuals over 65. Optical Coherence Tomography represents a noninvasive examination that not only can help clinicians diagnose multiple retinal abnormalities, AMD included, but also monitor the progression of the disease. There are still unmet needs in terms of personalized treatment for patients suffering from AMD, being a disease that displays individual diversity in terms of its progression and outcomes. We propose a method that will use deep learning methodologies to analyze patients’ disease progression and possible outcomes only based on the OCT scans that are taken during the initial first two examinations. This paper will propose an architecture that will help medical professionals with their involvement in the administration of anti-VEGF injections, which are the standard treatment for advanced neovascular AMD. The above-mentioned architecture is based on features extracted from B-scans of an OCT volume and through transfer learning practices will predict the total amount of injections required for an individual who is under treatment, the next visit decision to administer the injection, as well as visual acuity measured at the last visit.