Related Experiment Video
Updated: Nov 16, 2025
09:31
Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
Published on: April 8, 2015
11.8K
Machine learning and augmented human intelligence use in histomorphology for haematolymphoid disorders
Ahmad Nanaa1, Zeynettin Akkus2, Winston Y Lee3
1Division of Hematology, Mayo Clinic, Rochester, MN, USA.
Pathology
|March 1, 2021
Summary
Artificial intelligence (AI) offers promising decision support for pathologists in diagnosing blood disorders using digital pathology. This review covers AI
Area of Science:
- Digital pathology and artificial intelligence (AI) in haematopathology.
Background:
- Digital pathology advancements enable AI-driven decision support.
- AI shows promise in aiding the diagnosis of haematological disorders.
Purpose of the Study:
- Review progress in machine learning applications in haematopathology.
- Summarize key studies, limitations, and future trends for AI in haematopathology diagnostics.
Main Methods:
- Literature review of machine learning applications in haematopathology.
- Analysis of studies on AI in diagnosing leukaemia, lymphoma, and flow cytometry.
Main Results:
- AI applications cover benign haematology, leukaemia, lymphoma, and flow cytometry.
- Significant progress has been made in AI for haematopathology.
Conclusions:
- AI holds potential to enhance diagnostic accuracy and efficiency in haematopathology.
- Future trends indicate AI will increasingly support pathologists' diagnostic decisions.
