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.

我们开发了一种自动深度学习系统，可以从前面的骨盆 X 光片中检测髋骨骨折，它可以应用于临床数据，并演示了类似于人类放射科医师的诊断性能，将 ROC 曲线下面积达到 0.994。该系统有望提高诊断效率，减少昂贵的额外检查，扩大专家级医学图像解释的接入，改善患者整体效果。