Key PointsQuestionCan a deep learning–based image classification system detect retinal hemorrhage (RH) on head computed tomography (CT) from infants and toddlers with abusive head trauma (AHT)?FindingsIn this diagnostic pilot study with training, validation, and testing on 218 globes with RH and 384 globes without RH from 301 pediatric patients with AHT, a deep learning model identified RHs otherwise not observed by radiologists with high sensitivity and specificity. Thus, RH information can be accessed by deep learning on pediatric head CT images, although the deep learning model may be overfit, and the reported performance may be optimistic in the absence of an external validation data set.MeaningBy screening pediatric head CT images for RHs, deep learning models could assist clinicians in calibrating clinical suspicion for AHT, provide decision support for which patients urgently need fundoscopic examinations, and help involve child protection agencies in a timely manner when ophthalmologic services are not readily available.