Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Rethinking foundation models in pathology.

Nature biomedical engineering·2026

Same author

Artificial intelligence modelling in grading breast phyllodes tumours.

Histopathology·2026

Same author

Association of Kidney Volume With Patient-Reported Outcomes in ADPKD.

Kidney international reports·2026

Same author

Patients With Mild ADPKD by Kidney Imaging but Low Estimated GFR.

Kidney international reports·2025

Same author

Characterizing the spatial patterns and determinants of cerebrospinal fluid pseudorandom flow in the human brain with low b-value diffusion MRI.

Imaging neuroscience (Cambridge, Mass.)·2025

Same author

Differentiating clinical characteristics of perianal inflammatory bowel disease from perianal hidradenitis suppurativa.

International journal of dermatology·2024

Same journal

Evaluation of the BT-50 Automatic Quality Control Unit on the XN9000 Hematology Analyzer.

International journal of laboratory hematology·2026

Same journal

Acquired Myeloperoxidase Deficiency in MDS/MPN-NOS Presenting With Aberrant Muddy Sand-Like Granulocytes.

International journal of laboratory hematology·2026

Same journal

Rapid Centrifugation for Coagulation Testing: A Multianalyzer Validation Study Harmonizing Preanalytical Protocols for Total Laboratory Automation.

International journal of laboratory hematology·2026

Same journal

Abstract Proceedings of ISLH 2026 XXXIXth International Symposium on Technical Innovations in Laboratory Hematology.

International journal of laboratory hematology·2026

Same journal

Altitude-Related Laboratory Changes and a Novel Hemoglobin Estimation Model: A Retrospective Analysis in Children and Adults.

International journal of laboratory hematology·2026

Same journal

Explainable, Generative and Agentic Artificial Intelligence for the Peripheral Blood Film.

International journal of laboratory hematology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 28, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Applied machine learning in hematopathology.

Taher Dehkharghanian^1,2, Youqing Mu², Hamid R Tizhoosh³

¹Department of Nephrology, University Health Network, Toronto, Ontario, Canada.

International Journal of Laboratory Hematology

|May 31, 2023

Summary

This summary is machine-generated.

Machine learning (ML) is advancing digital pathology, particularly in hematopathology, by aiding diagnostic workflows. ML models are being developed to address unique challenges and support pathologists using bone marrow cytology and histopathology data.

Keywords:

artificial intelligence digital pathology hematopathology machine learning whole slide imaging

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

Related Experiment Videos

Last Updated: Jul 28, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

Area of Science:

Digital Pathology
Hematopathology
Machine Learning Applications

Background:

Machine learning (ML) is increasingly applied to digital pathology, especially in hematopathology.
These tools aim to support diagnostic workflows by analyzing diverse data, including digital tissue images.
Hematopathology presents unique challenges for ML compared to other pathology subspecialties.

Purpose of the Study:

To discuss current trends in machine learning for hematopathology.
To review existing ML-enabled medical devices for hematopathology workflows.
To explore research trends, focusing on bone marrow cytology and histopathology.

Main Methods:

Review of current machine learning applications and research in hematopathology.
Analysis of ML-enabled medical devices supporting diagnostic workflows.
Focus on modeling pathologist workflows to address practical problems.

Main Results:

Growing number of ML applications in digital hematopathology.
Development of ML tools to support diagnostic decision-making.
Identification of specific research trends in bone marrow analysis.

Conclusions:

Machine learning holds significant potential to enhance hematopathology diagnostics.
Addressing unique challenges in hematopathology is key for successful ML adoption.
The transition to digital pathology facilitates the integration of new ML tools.