# AssiST: convolutional neural network for analysis of antibiotic susceptibility testing

## TL;DR

AssiST is a CNN-based tool that automates antibiotic susceptibility testing using images of microdilution plates, enabling fast and reproducible drug sensitivity analysis.

## Contribution

AssiST introduces a flexible and scalable CNN pipeline for antibiotic susceptibility testing with customizable phenotype-to-susceptibility mapping.

## Key findings

- AssiST classifies microbial growth in 96-well plates with high accuracy.
- The tool supports diverse microorganisms, media types, and drugs through user-configurable settings.
- AssiST produces reproducible results using standard flatbed scanner images and a personal computer.

## Abstract

Antibiotic susceptibility testing (AST) is routinely used to evaluate microbial responses to antimicrobials. We present AssiST, a convolutional neural network (CNN) pipeline that classifies microbial growth in scanned 96-well broth microdilution plates to infer drug susceptibility at scale. AssiST accommodates diverse growth morphologies and supports a user-configurable mapping from phenotype to susceptibility calls, enabling flexible use across microorganism species, media types, and drugs. AssiST allows labs to convert flatbed-scanner images into reproducible drug sensitivity readouts with a standard personal computer.

AssiST is distributed as a MATLAB library and is freely available for non-commercial use. Code, documentation, and training/inference instructions are available at https://github.com/Mitchell-SysBio/AssiST/. We also provide pre-trained models and a library of sample images. The software accepts image files from standard flatbed scanners. We commit to maintaining the repository for at least 2 years post-publication.