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Related Concept Videos

X-ray Imaging01:24

X-ray Imaging

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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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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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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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Related Experiment Video

Updated: Mar 14, 2026

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Classification of Osteoporosis and Detection of Compression Fractures by Implementing Real-Time Object Detection

An-Chih Chen1,2, Jui-Hung Weng3, Chih-Wei Chen4,5

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International Journal of Rheumatic Diseases
|March 13, 2026
PubMed
Summary

This study uses the YOLOv4 model to detect osteoporosis and compression fractures from X-rays with promising accuracy. This artificial intelligence approach can help screen medical images, aiding diagnosis where advanced tools are unavailable.

Keywords:
compression fracturesimage classificationosteoporosisyou only look once (YOLO)

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Medical image diagnosis increasingly uses AI and machine learning.
  • Database acquisition presents a significant challenge for AI in healthcare.
  • The You Only Look Once (YOLO) technique offers real-time object detection capabilities.

Purpose of the Study:

  • To apply the YOLO technique for constructing an AI inference model for medical image diagnosis.
  • To develop a model for detecting osteoporosis and compression fractures using X-ray images.
  • To address the challenge of database acquisition in AI-driven medical diagnostics.

Main Methods:

  • Implemented the YOLOv4 technique for classifying osteoporosis and detecting compression fractures.
  • Extracted trabecular characteristics from caput femoris in X-ray images for osteoporosis classification.
  • Analyzed lateral spine images for compression fracture detection, using clinical data.

Main Results:

  • Constructed a YOLOv4 model achieving 78.1% accuracy for osteoporosis classification and 68.3% for compression fracture detection.
  • Demonstrated that X-rays can serve as a screening tool for osteoporosis, identifying patients for DXA scans.
  • Highlighted the utility of X-rays in settings lacking DXA facilities.

Conclusions:

  • The developed AI approach shows promise for medical diagnosis, with accuracy nearing 80%.
  • The deep learning model can preliminarily assist in screening large volumes of radiographs for potential positives.
  • This method offers a viable solution for AI-based medical image analysis, overcoming data acquisition hurdles.