Detecting hip fractures with radiologist-level performance using deep neural networks
This system addresses the need for efficient and accurate hip fracture diagnosis in radiology, potentially improving patient outcomes and reducing costs, though it is incremental as it applies existing deep learning methods to a specific medical imaging task.
The authors tackled the problem of detecting hip fractures from frontal pelvic x-rays by developing an automated deep learning system, achieving diagnostic performance equivalent to a human radiologist with an area under the ROC curve of 0.994.
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.