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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Osteoclasts are cells responsible for bone resorption and remodeling. They originate from hematopoietic progenitor cells present in the bone marrow. Numerous progenitor cells fuse to form multinucleated cells, each with 10-20 nuclei. A single osteoclast has a diameter of 150 to 200 µM. These cells have ruffled borders that break down the underlying bone tissue and release minerals such as calcium into the blood in bone resorption. Osteoclasts cling to bones with their ruffled edges during...
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Related Experiment Video

Updated: Jul 7, 2025

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model
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Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography.

Wilson Ong1, Ren Wei Liu1, Andrew Makmur1,2

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

Bioengineering (Basel, Switzerland)
|December 23, 2023
PubMed
Summary

Artificial intelligence (AI) analysis of CT scans shows promise for diagnosing osteoporosis and assessing bone mineral density (BMD). This approach could offer an opportunistic method for osteoporosis classification without requiring dual-energy X-ray absorptiometry (DXA).

Keywords:
artificial intelligencecomputed tomographydeep learningimagingmachine learningosteoporosis

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Bone Health

Background:

  • Osteoporosis is a significant health concern characterized by low bone mineral density (BMD) and increased fracture risk.
  • Advancements in medical imaging, particularly CT scans, are enabling novel approaches to osteoporosis diagnosis and assessment.

Purpose of the Study:

  • To review and assess the effectiveness, constraints, and potential impact of using AI analysis of CT scans for osteoporosis classification and BMD stratification.
  • To summarize findings from relevant studies on AI-based osteoporosis assessment via CT.

Main Methods:

  • A systematic literature search was performed across major electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) adhering to PRISMA guidelines.
  • 39 articles were retrieved and analyzed, focusing on CT imaging regions, types, and efficacy in predicting BMD compared to DXA.

Main Results:

  • AI analysis of CT scans demonstrated varying accuracy (61.8%-99.4%), sensitivity (41.0%-100.0%), and specificity (31.0%-100.0%) in classifying osteoporosis.
  • Areas under the curve (AUCs) ranged from 0.582 to 0.994, indicating significant potential for AI in this diagnostic area.

Conclusions:

  • AI-driven analysis of CT scans presents a promising, opportunistic method for predicting and classifying osteoporosis.
  • Further research is needed to validate clinical efficacy and reproducibility before routine integration into clinical practice.