Deep learning algorithms for predicting from neuroimaging data have shown considerable promise. Deep learning models that take advantage of the data's $3D$ structure have been proven to outperform ordinary machine learning on a number of learning tasks[1]. The majority of past research in this area, however, has focused on data from adults. Within the Adolescent Brain and Cognitive Development (ABCD) dataset, a major longitudinal development research, we examine the use of structural MRI data to predict gender and to identify gender related changes in brain structure. The results demonstrate that gender prediction accuracy is extremely high (>94%), and that this accuracy increases with age. Brain regions identified as the most discriminative in the task under study include predominantly frontal regions in addition to temporal lobe. When evaluating gender predictive changes specific to a two year increase in age, a broader set of visual, cingulate, and insular regions are revealed. Overall, our findings show a robust pattern of gender related structural brain changes, even over a small age range. This suggests the potential for evaluating the relationship of these changes to various behavioral and environmental factors to further study how the brain develops during adolescence. Clinical relevance— These results are not focused on clinical relevance currently, but in the future may be useful to characterize interactions between gender and potentially clinically relevant measures in adolescents.