Research Areas Ⅲ
Advanced Quantitative Histopathology and AI Integration
In experimental animal research, histopathological examination remains the final and most decisive step for understanding disease mechanisms and assessing therapeutic outcomes. Yet despite its central role, accurate interpretation often depends on individual experience, and there is a growing shortage of skilled pathologists.
As a result, objectivity and reproducibility remain limited, even as vast amounts of histopathological data accumulate each year; data that are mostly stored but rarely analyzed. To address these challenges, the Laboratory of Laboratory Animal Medicine (LAM) pursues two major missions.
First, we are advancing the standardization and quantitative refinement of pathological evaluation, developing objective diagnostic metrics and training the next generation of experimental pathologists.
Second, we are building AI-assisted quantitative pathology pipelines that convert accumulated histological images into analyzable data. These systems enhance objectivity, reproducibility, and predictive accuracy, allowing consistent interpretation across researchers and institutions.
By improving the precision and data integration of pathological analysis, we aim to reduce unnecessary animal use and maximize the scientific value of every experiment.
