In this pilot study, we evaluated the potential of computational image analysis of optical coherence tomography (OCT) data to determine the prognosis of patients with diabetic macular edema (DME). Spectral-domain OCT scans with fully automated retinal layer segmentation and segmentation of intraretinal cystoid fluid (IRC) and subretinal fluid of 629 patients receiving anti-vascular endothelial growth factor therapy for DME in a randomized prospective clinical trial were analyzed. The results were used to define 312 potentially predictive features at three timepoints (baseline, weeks 12 and 24) for best-corrected visual acuity (BCVA) at baseline and after one year used in a random forest prediction path. Preliminarily, IRC in the outer nuclear layer in the 3-mm area around the fovea seemed to have the greatest predictive value for BCVA at baseline, and IRC and the total retinal thickness in the 3-mm area at weeks 12 and 24 for BCVA after one year. The overall model accuracy was R2 = 0.21/0.23 (p < 0.001). The outcomes of this pilot analysis highlight the great potential of the proposed machine-learning approach for large-scale image data analysis in DME and other retinal diseases.