Author(s) :

PAVAN TK.

Volume/Issue :

Abstract :

Bacterial biofilms, which are microbial communities embedded in extracellular matrices, contribute to various chronic infections and pose significant challenges in both medical and industrial settings. The formation of biofilms is a dynamic and complex process that can be difficult to study using traditional methods. Time-lapse microscopy provides a valuable tool for observing biofilm development over time, but analyzing the resulting images manually is labor-intensive and prone to error. Recently, artificial intelligence (AI), particularly deep learning models, has emerged as a promising solution to automate the classification of biofilm formation from time-lapse microscopy data. These AI models can classify different stages of biofilm growth, such as initial attachment, microcolony formation, maturation, and dispersion, and also quantify important biofilm characteristics like size, density, and structure. This paper examines the application of AI in classifying bacterial biofilm formation, highlighting the benefits and challenges of implementing these techniques in research and clinical settings.

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