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Bone Remodeling and Repair01:31

Bone Remodeling and Repair

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 bone...

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Real-time closed-loop tissue-specific laser osteotomy using deep-learning-assisted optical coherence tomography.

Yakub A Bayhaqi1, Arsham Hamidi1, Alexander A Navarini2

  • 1Biomedical Laser and Optics Group (BLOG), Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland.

Biomedical Optics Express
|June 21, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel real-time, noninvasive optical coherence tomography (OCT) method for laser osteotomy. This system uses deep learning to accurately detect bone and bone marrow during surgery, improving precision.

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Surgical Technology

Background:

  • Laser osteotomy requires precise control to differentiate between bone and bone marrow.
  • Current methods lack real-time, noninvasive feedback for accurate tissue ablation.

Purpose of the Study:

  • To develop and validate a real-time, noninvasive feedback system for laser osteotomy using optical coherence tomography (OCT).
  • To assess the feasibility of using OCT with deep learning for intraoperative tissue identification during bone surgery.

Main Methods:

  • Implementation of optical coherence tomography (OCT) as an online feedback system for laser osteotomy.
  • Training a deep-learning model to identify bone and bone marrow during laser ablation.
  • Conducting hole ablation experiments to quantify perforation depth and volume loss.

Main Results:

  • The deep-learning model achieved a test accuracy of 96.28% in identifying tissue types.
  • Average maximum perforation depth was 0.216 mm, with an average volume loss of 0.077 mm³.
  • Demonstrated the feasibility of OCT for real-time feedback in laser osteotomy.

Conclusions:

  • Real-time OCT with deep learning offers a promising noninvasive solution for precise laser osteotomy.
  • The contactless nature and high accuracy of OCT support its integration as a feedback system in bone surgery.
  • This technology enhances surgical control and potentially reduces tissue damage during laser ablation procedures.