Matrix effect resolution in artificial intelligence-driven bacteria detection

Accurate detection of bacteria in complex biological, food, and environmental matrices is essential for infection control, antimicrobial resistance (AMR) surveillance, and public health decision-making. However, matrix effects arising from heterogeneous sample compositions remain a major barrier to the reliable deployment of rapid novel detection technologies. These effects can distort analytical signals, reduce reproducibility, and complicate data interpretation, particularly outside controlled laboratory settings. Recent advances in artificial intelligence (AI) offer new opportunities to overcome matrix-induced variability by enabling data-driven signal correction, pattern recognition, and adaptive learning across diverse sample types. In this review, we synthesized emerging strategies from the last 5 years for matrix-effect resolution in AI-assisted bacterial detection systems, including electrochemical, optical, and spectroscopic platforms. We discuss how AI models can integrate biological variability, environmental context, and sensor heterogeneity to improve robustness, analyte discrimination, and even quantitative accuracy, with emphasis on high-risk pathogens, such as those in the ESKAPE group. The workflow for AI-assisted bacterial detection is critically analyzed, beginning with pretreatment of complex samples, processing of raw data to extract the most relevant features, and then continuing with the implementation of the algorithm.