Antibiotic profile classification of Proteus mirabilis using machine learning: An investigation into multidimensional radiomics features

Abstract

Antimicrobial resistance (AMR) presents a significant threat to global healthcare. Proteus mirabilis causes catheter-associated urinary tract infections (CAUTIs) and exhibits increased antibiotic resistance. Traditional diagnostics still rely on culture-based approaches, which remain time-consuming. Here, we study the use of machine learning (ML) to classify bacterial resistance profiles using straightforward microscopic imaging of P. mirabilis for resistance classification integrated with radiomics feature analysis and ML models. From 150 P. mirabilis strains isolated from catheters of patients diagnosed with a CAUTI, 30 displayed multidrug resistance using the standardized disk diffusion method, and 60 showed strong biofilm activity in microtiter plate assays. As a more rapid alternative, we introduce wavelet-based and regular microscopy imaging with feature extraction/selection, following image preprocessing steps (image denoising, normalization, and mask creation). These features enable training and testing different ML models with 5-fold cross-validation for P. mirabilis resistance classification. From these models, the Random Forest (RF) algorithm exhibited the highest performance with ACC = 0.95, specificity = 0.97, sensitivity = 0.88, and AUC = 0.98 among the other ML algorithms considered in this study for P. mirabilis resistance classification. This successful application of wavelet-based feature Radiomics analysis with RF model represents a crucial step towards a precise, rapid, and cost-effective method to distinguish antibiotic resistant P. mirabilis strains.