Work

A pilot study: Incorporating Treponema pallidum antigens into machine learning models for accurate syphilis treatment outcome assessment

Year: 2025

Type: article

Abstract: (Tp) antigens (Tp0134, Tp0768, Tp0971, Tp0462, and Tp92) combined with a machine learning (ML) model. Analysis of 509 serum samples (including paired pre- and post-treatment samples) employed an established ELISA assay to dynamically monitor antibody changes. Results demonstrated that post-treatment antibody reduction for potential infection-stage-dependent antigens (pIDAs) (especially Tp0134 and Tp0768) was markedly higher than for the non-infection-stage-dependent antigen Tp92 and traditional methods. Utilizing nested cross-validation to train an array of ML models, ultimately chosen random forest model (AUC = 0.815) demonstrated enhanced efficacy in accurately distinguishing between infection and cure. Specifically, Tp0768, Tp92, and Tp0134 were identified as the pivotal features. Combining Tp antigens with ML provides a more accurate and dynamic tool for treatment efficacy assessment, enabling a more effective evaluation of syphilis treatment outcomes in the future.

Source: iScience

DOI: 10.1016/j.isci.2025.114275