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Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

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Published on: April 8, 2015

Artificial Intelligence in Bone Marrow Histological Diagnostics: Potential Applications and Challenges.

Leander van Eekelen^1,2, Geert Litjens^1,2, Konnie M Hebeda¹

¹Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.

Pathobiology : Journal of Immunopathology, Molecular and Cellular Biology

|February 15, 2023

Summary

This summary is machine-generated.

Artificial intelligence (AI) can enhance bone marrow (BM) histology diagnostics by analyzing digital slides. Future AI applications promise improved cell analysis, disease prediction, and genotype-phenotype correlation in BM biopsies.

Keywords:

Artificial intelligence Bone marrow biopsy Deep learning Pathology Whole-slide image

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Area of Science:

Digital pathology
Computational pathology
Hematopathology

Background:

Digitalization of histological slides enables advanced computational analysis.
Bone marrow (BM) biopsies are crucial for diagnosing hematologic disorders.
Artificial intelligence (AI) offers new possibilities for analyzing complex histopathological data.

Purpose of the Study:

To explore the potential applications of AI in analyzing whole-slide images of BM biopsies.
To outline diagnostic tasks, investigations, and research questions addressable by AI in BM pathology.
To discuss current AI research and implementation challenges in BM diagnostics.

Main Methods:

Review of current AI research in bone marrow biopsy slide analysis.
Perspective on future AI capabilities in histopathological diagnostics.
Illustrative examples of AI applications in BM pathology.

Main Results:

AI can support tasks like cell lineage characterization and quantification.
AI has the potential for disease prediction and detecting genotype-phenotype correlations.
Early AI research shows promise in identifying subtle phenotypic changes.

Conclusions:

AI holds significant potential to revolutionize bone marrow histology diagnostics.
Future AI integration can enhance diagnostic accuracy, efficiency, and discovery in hematopathology.
Addressing implementation challenges is key to realizing AI's full potential in clinical practice.