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Related Experiment Video

Updated: Sep 19, 2025

System for Efficacy and Cytotoxicity Screening of Inhibitors Targeting Intracellular Mycobacterium tuberculosis

System for Efficacy and Cytotoxicity Screening of Inhibitors Targeting Intracellular Mycobacterium tuberculosis

Published on: April 5, 2017

Deep learning-based framework for Mycobacterium tuberculosis bacterial growth detection for antimicrobial

Hoang-Anh T Vo¹, Sang Nguyen¹, Ai-Quynh T Tran¹

¹School of Science, Engineering & Technology (SSET), RMIT University, Ho Chi Minh, Viet Nam.

Computational and Structural Biotechnology Journal

|June 16, 2025

Summary

A new deep learning system, TMAS, accurately detects tuberculosis growth in microtiter plates. This automated tool improves tuberculosis drug susceptibility testing (DST) by reliably analyzing bacterial growth, outperforming existing methods.

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An Automated Culture System for Use in Preclinical Testing of Host-Directed Therapies for Tuberculosis

An Automated Culture System for Use in Preclinical Testing of Host-Directed Therapies for Tuberculosis

Published on: August 16, 2021

Area of Science:

Microbiology
Bioinformatics
Medical Diagnostics

Background:

Tuberculosis (TB) remains a leading infectious cause of death globally, with drug resistance posing a significant challenge.
Accurate and accessible diagnostics are crucial for effective TB treatment and control.
Current high-throughput phenotypic testing methods using 96-well plates can be difficult to interpret, especially with low bacterial growth or poor image quality.

Purpose of the Study:

To develop and validate a novel deep learning framework, the TB Microbial Analysis System (TMAS), for automated detection of *Mycobacterium tuberculosis* growth in 96-well microtiter plates.
To enhance the accuracy and efficiency of Minimum Inhibitory Concentration (MIC) determination in tuberculosis drug susceptibility testing (DST).
To differentiate true bacterial growth from artefacts in plate images.

Main Methods:

Leveraged state-of-the-art deep learning models within the TMAS framework to analyze images of 96-well microtiter plates.
Trained and refined TMAS using a dataset of 4,018 plate images from the CRyPTIC consortium.
Evaluated TMAS performance against established standards for automated growth detection.

Main Results:

TMAS achieved an essential agreement of 98.8% in detecting *M. tuberculosis* growth, significantly exceeding the International Organization for Standardization (ISO) 90% threshold.
The system demonstrated robust performance in identifying true bacterial growth and distinguishing it from artefacts.
TMAS significantly outperformed existing automated algorithms like AMyGDA, particularly in challenging low-growth or low-quality image scenarios.

Conclusions:

TMAS provides a reliable and automated solution for analyzing microbial growth in TB drug susceptibility testing.
The deep learning approach enhances the accuracy and efficiency of MIC determination, supporting expert interpretation.
TMAS has the potential to improve global TB diagnostics, especially in resource-limited settings.