A pediatric wrist trauma X-ray dataset (GRAZPEDWRI-DX) for machine learning
Eszter Nagy, Michael Janisch, Franko Hržić, Erich Sorantin, Sebastian Tschauner

TL;DR
This paper introduces a large dataset of pediatric wrist X-rays with annotations to help train machine learning models for detecting fractures and other injuries.
Contribution
The GRAZPEDWRI-DX dataset provides a large, annotated collection of pediatric wrist radiographs for advancing automated fracture detection research.
Findings
The dataset includes 6,091 patients with 20,327 images covering multiple projections.
It contains 74,459 image tags and 67,771 labeled objects annotated by pediatric radiologists.
The dataset is publicly available and de-identified for use in computer vision research.
Abstract
Digital radiography is widely available and the standard modality in trauma imaging, often enabling to diagnose pediatric wrist fractures. However, image interpretation requires time-consuming specialized training. Due to astonishing progress in computer vision algorithms, automated fracture detection has become a topic of research interest. This paper presents the GRAZPEDWRI-DX dataset containing annotated pediatric trauma wrist radiographs of 6,091 patients, treated at the Department for Pediatric Surgery of the University Hospital Graz between 2008 and 2018. A total number of 10,643 studies (20,327 images) are made available, typically covering posteroanterior and lateral projections. The dataset is annotated with 74,459 image tags and features 67,771 labeled objects. We de-identified all radiographs and converted the DICOM pixel data to 16-Bit grayscale PNG images. The filenames and…
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Taxonomy
TopicsArtificial Intelligence in Healthcare and Education · Autopsy Techniques and Outcomes · Bone fractures and treatments
