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

Preclinical Development: Overview

Preclinical Development: Overview

Preclinical development consists of a series of tests that ensure the safety and efficacy of a new therapeutic compound before it is tested in humans. There are four main phases to this process. First, safety pharmacology tests are conducted to ensure the drug does not produce any acutely harmful effects. These tests examine parameters such as bronchoconstriction, cardiac dysrhythmias, blood pressure changes, and ataxia. Next, preliminary toxicological testing is performed to determine the...

You might also read

Related Articles

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

Sort by

Same author

Morphological fingerprints enable machine learning based inference of neuroblastoma cell states without transcriptomics.

bioRxiv : the preprint server for biology·2026

Same author

Prevalence and Relative Proportions of MS, NMOSD, and MOGAD in the Republic of Korea.

Neurology·2026

Same author

Functional impact of the ATP1A3-p.A813V variant: insights into a calcium-driven hyperexcitability cascade in rapid-onset dystonia-Parkinsonism.

Journal of translational medicine·2026

Same author

Effects of Natural Extract Mixtures on the Quality Characteristics of Sausages during Refrigerated Storage.

Food science of animal resources·2026

Same author

Polyhexamethylene guanidine phosphate exposure induces abnormal behaviors by disrupting synaptic formation and activity in the cerebral cortex.

Journal of neuroinflammation·2026

Same author

Oral repeated dose toxicity study of bisphenol F in rats.

Regulatory toxicology and pharmacology : RTP·2026

Search research articles

Related Experiment Video

Updated: Aug 7, 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

Preparing pathological data to develop an artificial intelligence model in the nonclinical study.

Ji-Hee Hwang¹, Minyoung Lim¹, Gyeongjin Han¹

¹Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114, Korea.

Scientific Reports

|March 8, 2023

Summary

Training artificial intelligence (AI) models with diverse histological image datasets, including varied staining and magnification, significantly improves prediction accuracy for pathological lesions. Mixed datasets enhance AI performance over single-dataset training.

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

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Aug 7, 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

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Area of Science:

Digital pathology
Computational pathology
Histopathology image analysis

Background:

Artificial intelligence (AI) is increasingly used for analyzing digitized histological slides.
Hematoxylin and eosin (H&E) staining is standard, but variations in color tone and magnification can affect AI model performance.
Whole slide images (WSIs) offer comprehensive tissue visualization.

Purpose of the Study:

To investigate the impact of staining color tone and magnification variations on AI model predictions using H&E stained WSIs.
To compare the performance of AI models trained on single versus mixed datasets of varying image characteristics.
To optimize AI model training for consistent and accurate pathological lesion detection.

Main Methods:

Prepared three datasets (N20, B20, B10) of liver tissue WSIs with fibrosis, varying color tones and magnifications.
Trained five Mask R-CNN models using single or mixed combinations of these datasets.
Evaluated model performance on a separate test dataset comprising all three variations.

Main Results:

Models trained on mixed datasets (B20/N20, B10/B20) demonstrated superior performance compared to models trained on single datasets.
The enhanced performance of mixed models was confirmed by actual prediction results on test images.
Variations in staining color tone and magnification were key factors influencing AI prediction outcomes.

Conclusions:

Training AI algorithms with diverse staining color tones and multi-scaled image datasets leads to more robust and consistent performance.
Mixed datasets are crucial for developing reliable AI tools for pathological lesion prediction in digital pathology.
Future AI development should incorporate dataset variability for improved diagnostic accuracy.