An AI-directed analytical study on the optical transmission microscopic images of Pseudomonas aeruginosa in planktonic and biofilm states

TL;DR

This study develops a deep learning AI model to accurately detect Pseudomonas aeruginosa biofilms in microscopic images, aiding in health and environmental management.

Contribution

It introduces a novel AI-based approach using U-Net with ResNet encoders for precise biofilm image segmentation and detection.

Findings

High accuracy detection of biofilms achieved

ResNet18 and ResNet34 effectively differentiate biofilm structures

Potential for improved biofilm prevention strategies

Abstract

Biofilms are resistant microbial cell aggregates that pose risks to health and food industries and produce environmental contamination. Accurate and efficient detection and prevention of biofilms are challenging and demand interdisciplinary approaches. This multidisciplinary research reports the application of a deep learning-based artificial intelligence (AI) model for detecting biofilms produced by Pseudomonas aeruginosa with high accuracy. Aptamer DNA templated silver nanocluster (Ag-NC) was used to prevent biofilm formation, which produced images of the planktonic states of the bacteria. Large-volume bright field images of bacterial biofilms were used to design the AI model. In particular, we used U-Net with ResNet encoder enhancement to segment biofilm images for AI analysis. Different degrees of biofilm structures can be efficiently detected using ResNet18 and ResNet34 backbones. The potential applications of this technique are also discussed.