Automating opportunistic screening of osteoporotic fractures in computed tomography (CT) images could reduce the underdiagnosis of vertebral fractures. In this work, we present and evaluate an end-to-end pipeline for the detection of osteoporotic compression fractures of the vertebral body in CT images. The approach works in 2 steps: First, a hierarchical neural network detects and identifies all vertebrae that are visible in the field of view. Second, a feed-forward convolutional neural network is applied to patches containing single vertebrae to decide if an osteoporotic fracture is present or not. The maximum of the classifier’s output scores then allows to classify if there is at least one fractured vertebra in the image. On a per-patient basis our pipeline classifies 145 CT images—annotated by an experienced musculoskeletal radiologist—with a sensitivity of 0.949 and a specificity of 0.815 regarding the presence of osteoporotic fractures. The fracture classifier even distinguishes grade 1 deformities from grade 1 osteoporotic fractures with an area under the ROC-curve of 0.742, a task potentially challenging even for human experts. Our approach demonstrates robust and accurate diagnostic performance and thus could be applied to opportunistic screening.