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.

Keywords

Radiological weaponTask (project management)Clinical PracticeMedicineArtificial neural networkArtificial intelligenceComputer scienceRadiologyMedical physicsMachine learningPhysical therapyEngineering

Related Publications

ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

Xiaosong Wang , Yifan Peng , Le Lu +9 more

The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases. A tremendous number of X-ray imaging studies ...

2017 2984 citations

Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration

Mohammad R. Arbabshirani , Brandon K. Fornwalt , Gino Mongelluzzo +4 more

Abstract Intracranial hemorrhage (ICH) requires prompt diagnosis to optimize patient outcomes. We hypothesized that machine learning algorithms could automatically analyze compu...

2018 npj Digital Medicine 420 citations

Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

Roberto Cipolla , Yarin Gal , Alex Kendall

Numerous deep learning applications benefit from multitask learning with multiple regression and classification objectives. In this paper we make the observation that the perfor...

2018 2018 IEEE/CVF Conference on Computer ... 2556 citations

Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

Stanislav Nikolov , Sam Blackwell , Alexei Zverovitch +26 more

Over half a million individuals are diagnosed with head and neck cancer each year worldwide. Radiotherapy is an important curative treatment for this disease, but it requires ma...

2018 arXiv (Cornell University) 247 citations

Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks

Ioannis D. Apostolopoulos , Tzani A. Mpesiana

In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detectio...

2020 Physical and Engineering Sciences in ... 2350 citations

Publication Info

Year: 2017
Type: preprint
Citations: 54
Access: Closed

External Links

View on DOI.org

Social Impact

Altmetric

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

PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

OpenAlex

Cite This

APA Style

                            
                                    William A. Gale, 
                                
                                    Luke Oakden‐Rayner, 
                                
                                    Gustavo Carneiro
                                
                                et al.
                            
                            (2017). 
                            Detecting hip fractures with radiologist-level performance using deep neural networks. 
                            arXiv (Cornell University)
                            
                            .
                            https://doi.org/10.48550/arxiv.1711.06504

Identifiers

DOI: 10.48550/arxiv.1711.06504