AI-Assisted Diagnosis of Trichomonas vaginalis from Routine Gram-Stained Vaginal Smears

Background/Objectives: Trichomonas vaginalis is one of the most prevalent non-viral sexually transmitted infections worldwide. Although Gram staining is routinely performed in clinical microbiology laboratories for the evaluation of vaginal samples, it is not considered a diagnostic method for T. vaginalis, which represents a missed diagnostic opportunity in routine practice. This study aimed to evaluate an artificial intelligence (AI)-assisted diagnostic approach for the identification of T. vaginalis directly from routine Gram-stained vaginal smears. Methods: A retrospective dataset of Gram-stained vaginal smear images was analysed using a cascaded AI-based framework combining image processing and classification. The image selection and quality control were performed under the supervision of a specialised clinical microbiologist. All cases were independently confirmed by polymerase chain reaction (PCR), which served as the reference diagnostic standard. Model performance was assessed using standard diagnostic metrics, including accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), Cohen's kappa, and Matthews correlation coefficient (MCC). Held-out independent testing was used to assess generalisability beyond the internal validation subset. Results: The proposed AI-assisted approach demonstrated high diagnostic performance for the identification of T. vaginalis, achieving an AUC of 0.973, Cohen's kappa of 0.87, and an MCC of 0.87. The system showed high diagnostic concordance with PCR results across both internal and external validation datasets, supporting the feasibility and reproducibility of the approach under routine laboratory conditions. Conclusions: This study shows that artificial intelligence may enhance the diagnostic utility of routinely performed Gram-stained vaginal smears by enabling reliable identification of T. vaginalis. The proposed approach could be integrated into standard microbiology workflows as an objective decision-support or triage adjunct, facilitating early identification and supporting clinical decision-making without altering existing laboratory procedures.