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Related Concept Videos

Osteoclasts in Bone Remodeling01:31

Osteoclasts in Bone Remodeling

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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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Osteoclast Derivation from Mouse Bone Marrow
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Artificial intelligence-assisted identification and quantification of osteoclasts.

Thomas Emmanuel1, Annemarie Brüel2, Jesper Skovhus Thomsen2

  • 1Department of Dermatology, Aarhus University Hospital, Denmark.

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|August 26, 2021
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Summary

A new AI-assisted method significantly speeds up osteoclast quantification for bone resorption assessment, offering similar accuracy to manual methods. This AI approach enables automated overnight batch processing of bone histomorphometry slides.

Keywords:
AI-assisted image processingBone histomorphometryOsteoclasts

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Area of Science:

  • Biomedical Engineering
  • Cell Biology
  • Orthopedics

Background:

  • Osteoclast quantification is crucial for assessing bone resorption in bone histomorphometry.
  • Manual counting methods are time-consuming and labor-intensive.
  • Existing automated methods often rely on less advanced segmentation techniques and do not fully leverage artificial intelligence (AI).

Purpose of the Study:

  • To introduce and validate a novel AI-assisted method for quantifying osteoclasts.
  • To compare the efficiency and accuracy of the AI method against conventional manual counting.
  • To provide a detailed guide for implementing the AI-based osteoclast identification.

Main Methods:

  • Tibiae from Wistar rats were processed for osteoclast identification using Tartrate-resistant acid phosphatase (TRAP) staining or cathepsin K immunostaining.
  • Osteoclast-covered surfaces (Oc.S/BS) were quantified using both conventional manual bright-field microscopy and a newly developed AI-assisted method.
  • The AI method utilizes a retrainable AI module for automated analysis.

Main Results:

  • The AI-assisted method significantly reduced the time required for osteoclast quantification compared to manual methods.
  • Estimates of Oc.S/BS obtained by the AI method were comparable to those from the conventional manual method.
  • The AI module facilitated fully automated overnight batch processing of multiple annotated bone sections.

Conclusions:

  • AI-assisted osteoclast identification offers a more efficient and accurate alternative to traditional manual quantification in bone histomorphometry.
  • The developed AI method streamlines the assessment of bone resorption, enabling high-throughput analysis.
  • This technology holds promise for accelerating research in bone diseases and treatments.