Rapid antifungal phenotypic detection of

Background: Invasive fungal infections have become a significant cause of mortality among immunocompromised and critically patients. Methods: Antifungal susceptibility testing was performed on 85 clinical isolates of Results: The proportion of isolates demonstrating resistance to FLZ was 28.2% (24/85), while to VOR was 21.2% (18/85). With regard to the antifungal susceptibility prediction models, it was found that all machine-learning models achieved effective prediction within 6-8 hours of drug exposure. The FLZ and VOR models demonstrated the highest discriminative ability, with the Logit model for FLZ attaining an AUC of 0.923 and the SVM model for VOR achieving an AUC of 0.900. Notably, both models exhibited F1-scores that surpassed 0.88. Feature-importance analysis revealed that phenotypic signals from 5-8 h contributed most to prediction accuracy, reflecting the time-dependent nature of antifungal effects. Conclusions: Machine-learning models based on early phenotypic data have been shown to be capable of accurately predicting antifungal susceptibility within 8 hours.This represents a significant improvement over conventional methods and provides a promising technical framework for the development of rapid antifungal susceptibility testing and precision anti-infective therapy.