Deep Learning for Estimating Synaptic Health of Primary Neuronal Cell Culture

TL;DR

This paper presents a deep learning model using CNNs with residual connections to accurately classify primary neuronal cells as treated or untreated, aiding drug discovery by assessing synaptic health.

Contribution

Introduces a high-accuracy deep CNN model for classifying neuronal cell health from high-throughput imaging data, advancing automated analysis in neuropharmacology.

Findings

Achieved 99.6% accuracy in distinguishing treated vs. untreated cells.

Demonstrated effectiveness of deep CNNs with residual connections for biological image classification.

Supports drug discovery efforts by enabling rapid assessment of neuronal health.

Abstract

Understanding the morphological changes of primary neuronal cells induced by chemical compounds is essential for drug discovery. Using the data from a single high-throughput imaging assay, a classification model for predicting the biological activity of candidate compounds was introduced. The image recognition model which is based on deep convolutional neural network (CNN) architecture with residual connections achieved accuracy of 99.6$\%$ on a binary classification task of distinguishing untreated and treated rodent primary neuronal cells with Amyloid-$\beta_{(25-35)}$.