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Bones of the Upper Limb: Radius

Bones of the Upper Limb: Radius

The radius is longer of the two bones that make up the human antebrachium or forearm. At the proximal end, the radius articulates with the capitulum of the humerus and the radial notch of the ulna to form the elbow joint. At the distal end, the radius articulates with the ulna via the ulnar notch, forming the distal radioulnar joint. Distally, the radius also attaches to the carpal wrist bones (scaphoid and lunate) to form the radiocarpal joint.
The radius has a nail-shaped head, and a...

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Related Experiment Video

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Detecting Distal Radius Fractures Using a Segmentation-Based Deep Learning Model.

Turkka T Anttila¹, Teemu V Karjalainen², Teemu O Mäkelä^3,4

¹Musculoskeletal and Plastic Surgery, Department of Hand Surgery, University of Helsinki and Helsinki University Hospital, Topeliuksenkatu 5B, Helsinki, 00260, Finland. turkka.anttila@helsinki.fi.

Journal of Digital Imaging

|December 21, 2022

Summary

A new deep learning model accurately detects distal radius fractures using pixel-level annotations on wrist radiographs. This AI tool shows promise for improving fracture detection accuracy in clinical settings.

Keywords:

Artificial intelligence Deep learning Diagnostic tests Fractures Radius fractures

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Area of Science:

Radiology
Artificial Intelligence
Orthopedic Surgery

Background:

Accurate detection and assessment of distal radius fractures are crucial for effective treatment.
Current radiographic assessment can be challenging, necessitating improved diagnostic tools.

Purpose of the Study:

To develop and validate a deep learning model for precise distal radius fracture detection using pixel-level annotations.
To evaluate the model's performance in a multi-hospital setting with diverse radiographic data.

Main Methods:

A deep learning model was developed using pixel-level annotations on 3399 wrist radiographs.
The model was trained and validated on a dataset of 3785 emergency wrist radiograph examinations from six hospitals.
Model performance was assessed using the area under the ROC curve, with consensus from three hand surgeons serving as the gold standard.

Main Results:

The deep learning model achieved high accuracy in detecting distal radius fractures, with an area under the ROC curve of 0.97 (CI 0.95-0.98) for examinations without a cast.
Fracture detection was more accurate on postero-anterior radiographs compared to lateral radiographs.
The model demonstrated robust performance across multiple hospitals and radiographic system manufacturers.

Conclusions:

Segmentation-based deep learning models show significant potential for enhancing the accuracy of distal radius fracture detection.
The developed model performed well in a real-world, multi-institutional setting.
Further research involving algorithm comparison and external validation is recommended to solidify clinical utility.