A Machine Learning Approach to Microcalorimetric Pattern Classification of Pathogens in Synovial Fluid.

ABSTRACT: Isothermal microcalorimetry (IMC) is a promising tool for diagnosing periprosthetic joint infection (PJI), based on real-time measurement of growth-related heat production of pathogens, and faster than conventional microbial cultures. However, the feasibility of identifying specific pathogens in clinical samples using IMC has yet to be proven. This study implements machine learning and transfer learning convolutional neural network (CNN) models to detect and identify pathogens causing PJI, using IMC data alone. IMC data were obtained from synovial fluid samples, including 174 aseptic samples and 239 PJI samples containing five different bacterial strains. XGBoost, multi-layer perceptron, support vector machine, random forest, and three transfer learning CNN models were implemented to detec

SUBMITTER: Lozano-Garcia M 

PROVIDER: S-EPMC12422177 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature