A pilot study: Incorporating <i>Treponema pallidum</i> antigens into machine learning models for accurate syphilis treatment outcome assessment.

ABSTRACT: As the reliability of nontreponemal tests for evaluating syphilis treatment efficacy is increasingly questioned, we propose an optimized approach using Treponema pallidum (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.

SUBMITTER: Yao J 

PROVIDER: S-EPMC12757571 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature