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Related Concept Videos

X-ray Imaging01:24

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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: May 28, 2025

Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography
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Bone Age Assessment Using Various Medical Imaging Techniques Enhanced by Artificial Intelligence.

Wenhao Yuan1,2, Pei Fan3, Le Zhang1

  • 1Information Technology Center, Wenzhou Medical University, Wenzhou 325035, China.

Diagnostics (Basel, Switzerland)
|February 13, 2025
PubMed
Summary

Bone age (BA) assessment is vital for growth and development. Artificial intelligence (AI) offers advanced, automated methods to improve the accuracy and efficiency of skeletal maturity evaluations compared to traditional techniques.

Keywords:
AICTMRIX-ray imagingbone age assessmentultrasound imaging

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Area of Science:

  • Radiology
  • Pediatrics
  • Forensic Anthropology

Background:

  • Bone age (BA) assessment is critical for evaluating skeletal maturity in clinical and forensic settings.
  • Traditional methods like Greulich-Pyle and Tanner-Whitehouse rely on atlas comparisons, but face limitations due to genetic and environmental factors.
  • The need for automated and more accurate BA assessment tools is increasing.

Purpose of the Study:

  • To explore advancements in bone age estimation techniques.
  • To focus on machine learning (ML) and artificial intelligence (AI) driven methods for BA assessment.
  • To provide a historical context and compare the benefits and limitations of various BA estimation approaches across different imaging modalities.

Main Methods:

  • Review of traditional bone age assessment techniques (e.g., Greulich-Pyle, Tanner-Whitehouse).
  • Exploration of automated bone age assessment tools, including early systems like HANDX.
  • Focus on the application of machine learning and deep learning algorithms in modern bone age estimation.

Main Results:

  • Traditional methods, while effective, have limitations in accounting for individual variations.
  • AI-driven tools show promise in enhancing the accuracy and efficiency of bone age evaluations.
  • A gap exists in comparative analyses of AI-based bone age methods across diverse imaging technologies.

Conclusions:

  • Machine learning and AI are pivotal in advancing bone age assessment beyond traditional methods.
  • Further research is needed to compare AI approaches across various imaging modalities for robust clinical application.
  • Accurate bone age estimation using advanced computational methods has significant clinical and forensic implications.