Title: Detecting hip fractures with radiologist-level performance using deep neural networks,
Abstract: We developed an automated deep learning system to detect hip fractures from
frontal pelvic x-rays, an important and common radiological task. Our system
was trained on a decade of clinical x-rays (~53,000 studies) and can be applied
to clinical data, automatically excluding inappropriate and technically
unsatisfactory studies. We demonstrate diagnostic performance equivalent to a
human radiologist and an area under the ROC curve of 0.994. Translated to
clinical practice, such a system has the potential to increase the efficiency
of diagnosis, reduce the need for expensive additional testing, expand access
to expert level medical image interpretation, and improve overall patient
outcomes.