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

Three-Dimensional Microscopy in Microbiology

Three-Dimensional Microscopy in Microbiology

Three-dimensional imaging techniques are essential in cell biology, allowing researchers to visualize intricate cellular structures with high resolution. Two prominent methods, Differential Interference Contrast Microscopy (DIC) and Confocal Scanning Laser Microscopy (CSLM), provide distinct advantages for imaging live and thick specimens, respectively.Differential Interference Contrast MicroscopyDIC microscopy enhances contrast in transparent, unstained samples by converting phase...

You might also read

Related Articles

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

Sort by

Same author

DNA methylation signatures distinguish extranodal NK/T-cell lymphoma and EBV-positive nodal T/NK-cell lymphoma and identify prognostic subgroups.

Leukemia·2026

Same author

Tet2 deficiency alters CD4+ T cell function and promotes T cell lymphoma with a TFH cell immunophenotype.

The Journal of experimental medicine·2026

Same author

World Health Organization classification of tumours of the breast 6th edition 2026.

Histopathology·2026

Same author

A Rare Case of Cutaneous Extramedullary Hematopoiesis in Chronic Myeloid Leukemia.

Journal of cutaneous pathology·2026

Same author

The WHO Classification of Genetic Tumour Syndromes: Considerations for histopathology.

Histopathology·2026

Same author

Changes in the 6th edition of the World Health Organization classification of tumours of the digestive system.

Histopathology·2026

Search research articles

Related Experiment Video

Updated: Dec 25, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology.

Hanadi El Achi¹, Joseph D Khoury²

¹Department of Pathology and Laboratory Medicine, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.

|April 1, 2020

Summary

Digital pathology converts glass slides to digital images, enabling deep learning/artificial intelligence (DL/AI) analysis. This review explores DL/AI applications in diagnostic hematology and lymphoproliferative diseases.

Keywords:

artificial intelligence digital pathology hematopathology leukemia lymphoma

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

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

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

Published on: April 8, 2015

Related Experiment Videos

Last Updated: Dec 25, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

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

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

Published on: April 8, 2015

Area of Science:

Digital pathology
Artificial intelligence in medicine
Computational pathology

Background:

Digital pathology digitizes glass slides for enhanced data management and analysis.
Digitization enables the application of advanced computational techniques like deep learning/artificial intelligence (DL/AI).
The clinical integration and regulatory landscape for DL/AI in pathology are still developing.

Purpose of the Study:

To review emerging DL/AI technologies for digital pathology.
To focus on applications in diagnostic hematology.
To explore DL/AI's role in evaluating lymphoproliferative diseases.

Main Methods:

Review of recent studies utilizing whole-slide images and DL/AI.
Analysis of DL/AI algorithms for detecting histologic abnormalities.
Focus on applications relevant to hematologic malignancies.

Main Results:

DL/AI applied to digital pathology shows promising results in detecting abnormalities, particularly cancers.
Encouraging outcomes reported in recent studies using whole-slide imaging.
Emerging DL/AI tools demonstrate potential for diagnostic hematology.

Conclusions:

DL/AI holds significant promise for advancing digital pathology.
Further development is needed for clinical deployment in hematology and lymphoproliferative disease evaluation.
The integration of DL/AI is poised to transform diagnostic capabilities in pathology.