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

arXiv: https://arxiv.org/abs/1711.06504 · HTML: https://mlsanity.com/paper/1711.06504/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2017.

## Built on by (most cited first, 3 of 3)

- [Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack](https://mlsanity.com/paper/2103.03438/) (2021)
- [MultiNet with Transformers: A Model for Cancer Diagnosis Using Images](https://mlsanity.com/paper/2301.09007/) (2023)
- [Using Multi-modal Data for Improving Generalizability and Explainability of Disease Classification in Radiology](https://mlsanity.com/paper/2207.14781/) (2022)

## Abstract (arXiv)

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.