Laser scattering signal amplification and deep learning for detection of Candida culture positivity in women with vaginal symptoms: a preliminary analytical and pilot clinical evaluation

다국어 초록 (Multilingual Abstract)

Background: Accurate diagnosis of vulvovaginal candidiasis (VVC) is challenging because of the limitations of conventional wet-mount microscopy, which requires a trained examiner and offers suboptimal sensitivity. This preliminary study evaluated the analytical performance of laser scattering signal amplification (LSSA) combined with deep learning and its preliminary clinical feasibility for detecting Candida culture positivity in women with vaginal symptoms. Methods: LSSA signal data were acquired using the Bacometer from reference strains of five Candida species (10²–105 colony-forming units (CFU)/mL in Luria–Bertani broth). A twostage convolutional neural network (CNN) classifier was developed: Model 1 discriminated negative, C. albicans-positive, and non-albicans-positive specimens; Model 2 distinguished C. albicans from C. tropicalis among the positive specimens. The models were trained on 320 and 80 vials using 60:15:25 stratified splits and 5-fold cross-validation. Twenty vaginal discharge specimens from women with suspected VVC were assessed using fungal culture as the reference standard. Results: Model 1 achieved a hold-out accuracy of 73.8% (95% confidence interval [CI] 62.7–83.0%) for three-class concentration discrimination, whereas Model 2 achieved 77.8% (95% CI 60.8–89.9%) in cascade evaluation. In the pilot clinical evaluation (5 culture-positive and 15 culture-negative specimens), the classifier correctly identified 1 of 5 culture-positive cases (sensitivity 20.0%, 95% CI 0.5–71.6%) and all 15 culture-negative specimens (specificity 100.0%, 95% CI 78.2–100.0%), yielding an overall accuracy of 80.0% (95% CI 56.3–94.3%), positive predictive value (PPV) 100.0% (95% CI 2.5–100.0%), and negative predictive value (NPV) 78.9% (95% CI 54.4–93.9%). Conclusion: This preliminary study observed that concentration- and species-related scattering signals were partially classifiable under controlled reference strain conditions; however, the clinical sensitivity was insufficient (20.0%) in its current form. Correction of the sample pre-processing protocol is required before further clinical evaluation.