Transforming Blood Infection Diagnostics: AI and Electronic Noses Target Candida

Candidemia is a severe fungal bloodstream infection with high mortality rates, especially in immunocompromised patients. Traditional diagnostic methods, such as blood cultures, suffer from low sensitivity and long turnaround times, delaying targeted antifungal therapy. In this study, we explore the potential of electronic noses (e-noses) combined with artificial intelligence (AI) models to provide a rapid and accurate diagnosis ofCandidainfections directly from blood culture broth samples. Samples were analyzed using an e-nose, generating datasets that underwent preprocessing steps including outlier removal, cycle equalization, feature transformation, and optional oversampling to address species imbalances. To ensure clinical relevance, we employed a dual-scenario validation strategy, assessing both intra-sample sensor stability and inter-sample biological generalization. We tested both traditional machine learning models and time series classifiers, selecting models based on prior research and performance in similar tasks. The best-performing models were evaluated based on accuracy, precision, recall, F1-score, specificity, and computational efficiency. Results demonstrated that while the framework achieved > 98% consistency in sensor stability tests, the Support Vector Classifier (SVC) emerged as the most robust model for generalization, achieving statistical parity with complex Time Series models like InceptionTime, but with significantly higher computational efficiency. This study highlights the feasibility of AI-enhanced e-noses for rapidCandidadetection, offering a promising alternative to conventional diagnostics in clinical settings.