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Related Concept Videos

Bones of the Upper Limb: Radius01:09

Bones of the Upper Limb: Radius

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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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Bones of the Upper Limb: Ulna01:15

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The ulna and radius are parallel bones of the antebrachium or the forearm. The ulna lies medially and consists of a bony tip called the olecranon process at its proximal end. This hook-like projection articulates with the olecranon fossa of the humerus and forms the "hinged" ulnohumeral part of the elbow joint. This joint facilitates forearm extension and flexion while preventing its hyperextension. Similarly, the coronoid process, another bony projection on the proximal/anterior side...
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Assessment of Radial Pulse
The radial pulse, located at the wrist, is often the preferred site for assessing peripheral pulse because of its accessibility and dependability. The process of determining the radial pulse involves several steps:
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Detecting Distal Radial Fractures from Wrist Radiographs Using a Deep Convolutional Neural Network with an Accuracy

Takeshi Suzuki1, Satoshi Maki2,3, Takahiro Yamazaki4

  • 1Department of Orthopedic Surgery, Tonosho Hospital, Chiba, Japan.

Journal of Digital Imaging
|December 16, 2021
PubMed
Summary

A convolutional neural network (CNN) accurately diagnoses distal radius fractures (DRFs) from wrist X-rays. The AI model achieved high sensitivity and specificity, matching or exceeding orthopedic surgeons' diagnostic performance.

Keywords:
Convolutional neural networkDeep learningDistal radial fracturesRadiograph

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Orthopedic Diagnostics

Background:

  • Convolutional Neural Networks (CNNs) show promise in medical image analysis.
  • Distal radius fractures (DRFs) are common wrist injuries requiring accurate diagnosis.

Purpose of the Study:

  • To evaluate a CNN's ability to diagnose distal radius fractures using frontal and lateral wrist radiographs.
  • To compare the diagnostic performance of the CNN against orthopedic surgeons.

Main Methods:

  • A CNN model was implemented using Keras and TensorFlow, fine-tuned with EfficientNets.
  • Frontal and lateral wrist radiographs from 503 DRF cases and 289 controls were used for training and evaluation.
  • Diagnostic performance was assessed using sensitivity, specificity, accuracy, and Area Under the ROC Curve (AUC).

Main Results:

  • The CNN achieved 99.3% accuracy, 98.7% sensitivity, and 100% specificity using both frontal and lateral views.
  • The CNN's diagnostic accuracy was comparable to or better than three orthopedic surgeons.
  • The Area Under the ROC Curve (AUC) for the combined views was 0.993.

Conclusions:

  • CNNs demonstrate high accuracy in diagnosing distal radius fractures from plain radiographs.
  • AI-powered diagnostic tools have the potential to support orthopedic fracture assessment.