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Classification of Bones01:18

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The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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A novel segmentation-based deep learning model for enhanced scaphoid fracture detection.

A Bützow1, T T Anttila1, V Haapamäki2

  • 1Department of Musculoskeletal and Plastic Surgery, University of Helsinki and Helsinki University Hospital, Hartmaninkatu 4, 00029 Helsinki, Finland.

European Journal of Radiology
|July 13, 2025
PubMed
Summary
This summary is machine-generated.

A deep learning (DL) model can detect scaphoid fractures from wrist X-rays, performing comparably to experts. The DL model showed improved accuracy in identifying subtle, occult scaphoid fractures, potentially enhancing patient care.

Keywords:
Artificial intelligenceDeep learningFractureRadiographScaphoid

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Scaphoid fractures are common wrist injuries, often difficult to detect on initial radiographs.
  • Occult fractures, which are not apparent on plain X-rays, pose a diagnostic challenge and can lead to complications if missed.

Purpose of the Study:

  • To develop and evaluate a deep learning (DL) model for detecting both apparent and occult scaphoid fractures from wrist radiographs.
  • To compare the diagnostic performance of the DL model against a panel of clinical experts.

Main Methods:

  • A dataset of 1011 wrist radiographs from 408 patients was curated, with scaphoid fractures confirmed by advanced imaging (MRI/CT).
  • A segmentation-based DL model was trained on annotated images, identifying the scaphoid bone and potential fracture sites.
  • The DL model's performance was evaluated against ground truth and the interpretations of three orthopedic experts.

Main Results:

  • The DL model demonstrated strong diagnostic performance with a sensitivity of 0.86 and specificity of 0.83.
  • The model achieved an overall accuracy of 0.85 and an area under the ROC curve of 0.92.
  • Notably, the DL model detected 41% of occult fractures, significantly outperforming the experts (6.8%-13.7%).

Conclusions:

  • A segmentation-based DL model is a feasible tool for scaphoid fracture detection, comparable to existing DL models.
  • The DL model shows performance on par with experts for apparent fractures and superior accuracy for occult fractures.
  • Enhanced detection of occult scaphoid fractures by DL models could lead to improved patient management and outcomes.