Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Detailed Structure and Function of Lymph Nodes01:23

Detailed Structure and Function of Lymph Nodes

4.5K
Lymph nodes are bean-shaped structures that cluster along the lymphatic vessels in the inguinal, axillary, and cervical regions. Each node is divided into compartments by a capsule that extends trabeculae inward.
From a histological perspective, lymph nodes can be split into two main areas: the superficial cortex and the deep medulla. The outer cortex is populated by dendritic cells, macrophages, and B lymphocytes, which are densely packed into follicles. When these B-lymphocytes are presented...
4.5K
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.1K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.1K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.6K
3.6K
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

100.2K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
100.2K
Metastasis02:30

Metastasis

6.4K
Metastasis is the spread of cancer cells from the original site to distant locations in the body. Cancer cells can spread via blood vessels (hematogenous) as well as lymph vessels in the body.
Epithelial-to-Mesenchymal Transition
The epithelial-to-mesenchymal transition or EMT is a developmental process commonly observed in wound healing, embryogenesis, and cancer metastasis. EMT is induced by transforming growth factor-beta (TGF-β) or receptor tyrosine kinase (RTK) ligands, which further...
6.4K
Accuracy and Precision01:52

Accuracy and Precision

14.3K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.  Highly accurate...
14.3K

You might also read

Related Articles

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

Sort by
Same author

Intra-slide calibration technology improves immunohistochemical harmonization within and between anatomic pathology laboratories.

bioRxiv : the preprint server for biology·2026
Same author

Disease-dependent airway epithelial responses to acute electronic cigarette aerosol exposure: a pilot single-cell analysis.

Toxicological sciences : an official journal of the Society of Toxicology·2026
Same author

CN-RNN: a Deep Learning Framework for Copy Number Variation Detection with Exome Sequencing Data.

bioRxiv : the preprint server for biology·2026
Same author

Weak supervision of H&E slides reveals systems-level biology and functional states that govern therapeutic resistance.

bioRxiv : the preprint server for biology·2026
Same author

Mobile device smartphones for intraoperative diagnosis at the University Hospital Trust of Modena/UNIMORE: from validation process to costs analysis.

Diagnostic pathology·2026
Same author

Epithelioid Fibrous Histiocytoma With an <i>ETV6::NTRK3</i> Fusion in a Child: A Case Expanding the Spectrum of Receptor-Tyrosine Kinase Driven Epithelioid Fibrous Histiocytoma.

Pediatric and developmental pathology : the official journal of the Society for Pediatric Pathology and the Paediatric Pathology Society·2026

Related Experiment Video

Updated: Jan 22, 2026

Author Spotlight: A Model to Study the Systemic and Local Dynamics of CD8+ T Cells During LN Metastasis
07:45

Author Spotlight: A Model to Study the Systemic and Local Dynamics of CD8+ T Cells During LN Metastasis

Published on: January 26, 2024

2.7K

AI for pathologists: a universal lymph node metastasis detection app that enhances efficiency while preserving

Jennifer Vazzano1, Bindu Challa1, Vidya Arole1

  • 1Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA.

The Journal of Pathology. Clinical Research
|January 21, 2026
PubMed
Summary

Artificial intelligence (AI) tools can improve diagnostic accuracy and efficiency for pathologists. An AI application trained on lymph node metastases generalized to detect various cancer types across multiple organs, reducing pathologist workload.

Keywords:
artificial intelligence (AI)cancer diagnosticscomputational pathologydiagnostic efficiencydigital pathologyimage analysislymph node metastasis

More Related Videos

Generation of Lymph Node-fat Pad Chimeras for the Study of Lymph Node Stromal Cell Origin
09:10

Generation of Lymph Node-fat Pad Chimeras for the Study of Lymph Node Stromal Cell Origin

Published on: December 16, 2013

6.4K
Multispectral Real-time Fluorescence Imaging for Intraoperative Detection of the Sentinel Lymph Node in Gynecologic Oncology
06:37

Multispectral Real-time Fluorescence Imaging for Intraoperative Detection of the Sentinel Lymph Node in Gynecologic Oncology

Published on: October 20, 2010

23.8K

Related Experiment Videos

Last Updated: Jan 22, 2026

Author Spotlight: A Model to Study the Systemic and Local Dynamics of CD8+ T Cells During LN Metastasis
07:45

Author Spotlight: A Model to Study the Systemic and Local Dynamics of CD8+ T Cells During LN Metastasis

Published on: January 26, 2024

2.7K
Generation of Lymph Node-fat Pad Chimeras for the Study of Lymph Node Stromal Cell Origin
09:10

Generation of Lymph Node-fat Pad Chimeras for the Study of Lymph Node Stromal Cell Origin

Published on: December 16, 2013

6.4K
Multispectral Real-time Fluorescence Imaging for Intraoperative Detection of the Sentinel Lymph Node in Gynecologic Oncology
06:37

Multispectral Real-time Fluorescence Imaging for Intraoperative Detection of the Sentinel Lymph Node in Gynecologic Oncology

Published on: October 20, 2010

23.8K

Area of Science:

  • Digital pathology
  • Artificial intelligence in medicine
  • Cancer diagnostics

Background:

  • Pathologist workload and shortages contribute to diagnostic errors.
  • Manual tasks like mitosis counting and metastasis detection are time-consuming and unreliable.
  • Digital pathology and AI offer potential solutions for improving efficiency and accuracy.

Purpose of the Study:

  • To evaluate the generalizability of an AI application for lymph node metastasis detection across various cancer types and organs.
  • To assess the impact of AI assistance on pathologist efficiency and diagnostic accuracy.

Main Methods:

  • A commercial AI tool (Visiopharm app), initially trained on breast and colon cancer lymph node metastases, was used.
  • The AI tool analyzed 172 slides from 12 cancer types across 15 organ systems.
  • Pathologist time spent searching for metastasis was measured with and without AI assistance.

Main Results:

  • The AI application successfully detected lymph node metastases from 12 distinct cancer types across 15 organ systems.
  • AI assistance reduced the average time pathologists spent searching for metastasis (54.7s to 42.1s per slide).
  • Diagnostic accuracy was maintained when using AI-generated annotations.

Conclusions:

  • AI applications trained on specific datasets can generalize to detect metastases from multiple cancer types and organs.
  • AI tools can significantly reduce pathologist workload and improve efficiency without compromising accuracy.
  • Integrating AI into digital pathology workflows can help address pathologist shortages and enhance patient care.