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 antiVEGF 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 as well as next-visit injection administration.