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Related Concept Videos

Classification of Bones01:18

Classification of Bones

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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.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
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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: Aug 5, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review.

Wilson Ong1, Lei Zhu2, Yi Liang Tan1

  • 1Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore.

Cancers
|March 29, 2023
PubMed
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Artificial intelligence (AI) shows promise in diagnosing bone tumors from medical images, accurately distinguishing benign from malignant lesions across various modalities. Further clinical validation is needed before widespread integration into practice.

Keywords:
artificial intelligencebone malignancydeep learningimagingmachine learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate bone tumor diagnosis is critical for effective treatment planning.
  • Artificial intelligence (AI) and machine learning offer potential tools to aid in the diagnostic workflow for bone tumors.
  • Current diagnostic methods can be enhanced by AI's ability to analyze complex imaging data.

Purpose of the Study:

  • To review recent evidence on AI techniques for bone tumor imaging.
  • To summarize AI's effectiveness in differentiating benign from malignant bone lesions.
  • To explore the potential clinical applications of AI in bone tumor characterization.

Main Methods:

  • Systematic literature search of electronic databases (PubMed, MEDLINE, Web of Science, clinicaltrials.gov).
  • Adherence to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
  • Compilation and summarization of findings from 34 retrieved articles on AI in bone tumor imaging.

Main Results:

  • AI techniques were applied to radiographs, MRI, CT, and PET/CT for bone lesion differentiation.
  • Reported accuracy, sensitivity, and specificity for AI in distinguishing benign vs. malignant lesions ranged from 0.44-0.99, 0.63-1.00, and 0.73-0.96, respectively.
  • Area Under the Curve (AUC) values ranged from 0.73-0.96, indicating good performance.

Conclusions:

  • AI demonstrates good performance in discriminating bone lesions across multiple imaging modalities.
  • AI shows high sensitivity, specificity, and accuracy in differentiating benign from malignant bone lesions in several studies.
  • Further research and clinical validation are essential for integrating AI into routine practice.