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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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Related Experiment Video

Updated: Jun 13, 2025

Author Spotlight: Enhancing Rheumatoid Arthritis Research Through HR-pQCT Imaging Analysis
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RA-XTNet: A Novel CNN Model to Predict Rheumatoid Arthritis from Hand Radiographs and Thermal Images: A Comparison

Ahalya R Kesavapillai1,2, Shabnam M Aslam3, Snekhalatha Umapathy1

  • 1Department of Biomedical Engineering, SRM Institute of Science and Technology, College of Engineering and Technology, Chennai 603203, India.

Diagnostics (Basel, Switzerland)
|September 14, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an AI and quantum computing system for rheumatoid arthritis (RA) diagnosis using hand X-rays and thermal images. The RA-XTNet model achieved high accuracy, offering a rapid and effective automated diagnostic method.

Keywords:
UNet++convolutional neural networksrheumatoid arthritistransformers

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Quantum Computing

Background:

  • Rheumatoid arthritis (RA) diagnosis relies on imaging, but automated systems can improve speed and accuracy.
  • Integrating artificial intelligence (AI) and quantum computing offers novel approaches for medical image analysis.

Purpose of the Study:

  • To develop an automated diagnostic system for rheumatoid arthritis (RA) prediction.
  • To utilize AI and quantum computing for analyzing hand radiographs and thermal images.

Main Methods:

  • Image segmentation using UNet++ and k-means clustering.
  • Feature extraction with Speeded-Up Robust Features (SURF).
  • Classification using k-star, Hoeffding, Quantum Support Vector Machine (QSVM), and Vision Transformer (ViT).

Main Results:

  • UNet++ segmentation achieved 98.75% pixel-wise accuracy.
  • The custom RA-X-ray thermal imaging (XTNet) model showed 90% (X-ray) and 93% (thermal) accuracy.
  • QSVM yielded 93.75% (X-ray) and 87.5% (thermal) accuracy.
  • Vision Transformer (ViT) achieved 80% (X-ray) and 90% (thermal) accuracy.

Conclusions:

  • The RA-XTNet model demonstrates effectiveness for accurate and rapid RA diagnosis.
  • AI and quantum computing approaches show significant potential in medical diagnostics for RA.