Deep Learning-based Modeling for Preclinical Drug Safety Assessment.

ABSTRACT: In drug development, assessing the toxicity of candidate compounds is crucial for successfully transitioning from preclinical research to early-stage clinical trials. Drug safety is typically assessed using animal models with a manual histopathological examination of tissue sections to characterize the dose-response relationship of the compound - a time-intensive process prone to inter-observer variability and predominantly involving tedious review of cases without abnormalities. Artificial intelligence (AI) methods in pathology hold promise to accelerate this assessment and enhance reproducibility and objectivity. Here, we introduce TRACE, a model designed for toxicologic liver histopathology assessment capable of tackling a range of diagnostic tasks across multiple scales, including situ

SUBMITTER: Jaume G 

PROVIDER: S-EPMC11291027 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature