Deep learning enables automated assessments of inflammatory response in zebrafish exposed to different pollutants

TL;DR

This paper introduces a deep learning approach using a Unet-based model to automatically assess inflammatory responses in zebrafish exposed to pollutants, combining image analysis and gene expression data for rapid, accessible toxicology testing.

Contribution

It presents an innovative end-to-end deep learning model for automated inflammation assessment in zebrafish, including a user-friendly executable for broad application.

Findings

Automated segmentation of zebrafish images for inflammation markers

Correlation of image analysis with gene expression data

Deployment of a portable, easy-to-use assessment tool

Abstract

In the field of environmental toxicology, rapid and precise assessment of the inflammatory response to pollutants in biological models is critical. This study leverages the power of deep learning to enable automated assessments of zebrafish, a model organism widely used for its translational relevance to human disease pathways. We present an innovative approach to assessing inflammatory responses in zebrafish exposed to various pollutants through an end-to-end deep learning model. The model employs a Unet-based architecture to automatically process high-throughput lateral zebrafish images, segmenting specific regions and quantifying neutrophils as inflammation markers. Alongside imaging, qPCR analysis offers gene expression insights, revealing the molecular impact of exposure on inflammatory pathways. Moreover, the deep learning model was packaged as a user-friendly executable file (.exe), facilitating widespread application by enabling use on virtually any computer without the need for specialized software or training.