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We developed an automated deep learning system to detect hip fractures fromfrontal pelvic x-rays, an important and common radiological task. Our systemwas trained on a decade of clinical x-rays (~53,000 studies) and can be appliedto clinical data, automatically excluding inappropriate and technicallyunsatisfactory studies. We demonstrate diagnostic performance equivalent to ahuman radiologist and an area under the ROC curve of 0.994. Translated toclinical practice, such a system has the potential to increase the efficiencyof diagnosis, reduce the need for expensive additional testing, expand accessto expert level medical image interpretation, and improve overall patientoutcomes.

Detecting hip fractures with radiologist-level performance using deep neural networks