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Bone Marrow Sampling and Transplants01:22

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Bone marrow transplant is a potential cure for several diseases, including cancer and specific genetic disorders. Notably, this procedure is applicable for patients suffering from aplastic anemia, certain types of leukemia, severe combined immunodeficiency disease (SCID), Hodgkin's disease, non-Hodgkin's lymphoma, multiple myeloma, thalassemia, sickle-cell disease, and certain cancers.
The transplant begins with high doses of chemotherapy and radiation treatment, which aim to destroy...
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Advances in Bone Marrow Evaluation.

Joshua E Lewis1, Olga Pozdnyakova2

  • 1Department of Pathology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02215, USA.

Clinics in Laboratory Medicine
|August 1, 2024
PubMed
Summary
This summary is machine-generated.

Digital pathology and artificial intelligence (AI) offer new ways to evaluate bone marrow specimens for hematologic conditions. While AI shows promise for automated analysis, challenges hinder its widespread clinical adoption.

Keywords:
Artificial intelligenceAspirateBone marrowMachine learningTrephine biopsy

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Area of Science:

  • Hematopathology
  • Digital Pathology
  • Artificial Intelligence in Medicine

Background:

  • Bone marrow aspirate smears and trephine biopsies are crucial for diagnosing hematologic disorders.
  • Traditional evaluation methods are time-consuming and subjective.
  • Digital pathology enables new approaches to specimen analysis.

Purpose of the Study:

  • To review the current status of digital bone marrow evaluation.
  • To explore recent advancements in machine learning for automated bone marrow analysis.
  • To outline the benefits and obstacles for clinical AI implementation in hematopathology.

Main Methods:

  • Review of current digital pathology techniques for bone marrow specimens.
  • Analysis of recent research utilizing machine learning models for automated analysis.
  • Discussion of advantages and barriers to clinical integration of AI tools.

Main Results:

  • Digital pathology and AI models show potential for assisted and automated bone marrow evaluation.
  • Machine learning is being applied to analyze complex cellular morphology in bone marrow samples.
  • Significant barriers exist for the seamless clinical implementation of these technologies.

Conclusions:

  • Digital pathology combined with AI has the potential to transform bone marrow assessment.
  • Overcoming implementation barriers is key to realizing the benefits of AI in hematopathology.
  • Future vision includes AI-assisted and automated workflows for bone marrow diagnostics.