Researchers use optical coherence tomography (OCT) scans to diagnose and evaluate the effectiveness of various treatments and therapies for patients with Age-Related Macular Degeneration (AMD), the leading cause of severe visual impairment in the developed world. While automated evaluation procedures exist, AMD OCT analysis often requires verification or modification by a trained OCT evaluator. With millions of OCT scans performed per year, demand for new evaluation approaches increases. Human computing has proven to be an effective way to crowdsource a variety of scientific problems, as well as leverage human pattern-recognition ability. Video games allow users to interact with the scientific data while also leveraging the elements game developers require to maintain engagement. The authors investigate whether game interactions can train players to evaluate AMD OCT images, creating Eye in the Sky: Defender, which designed gameplay around OCT scan evaluation while supporting the desired outcomes via game mechanics rather than explicit training. In early prototype testing, results suggest player learning within four OCT-image-based game levels. Evaluations of accuracy using the mean user line input reflected a 92% improvement from a players’ initial image evaluation. Spearman rank correlation and Procrustes analysis indicate mean user line accuracy within 10% by image 4 and improved results compared to the automatically generated line in more challenging images. These results suggest Human Computation games can train users to analyze AMD and OCT scans, encouraging expanded research.