Fig. 2. Performance of the two-stage laser scattering signal amplification (LSSA) deep-learning classifier on hold-out reference-strain and preliminary clinical specimens. (A) Confusion matrix for Model 1, a three-class classifier (Negative, <10³ colony-forming units [CFU] /mL; Positive—C. albicans, ≥10⁴ CFU/mL; Positive—Non-albicans, ≥10⁴ CFU/mL), evaluated on the 25% hold-out test partition of the Model 1 reference-strain dataset (n = 80 vials; reference strains: C. albicans ATCC 14053, N. glabratus KCTC 7219, C. tropicalis KCTC 7212, C. parapsilosis ATCC 22019, P. kudriavzevii ATCC 6258). Rows indicate culture-confirmed true class; columns indicate model prediction. Overall accuracy: 73.8% (95% confidence interval [CI] 62.7–83.0%). (B) Confusion matrix for Model 2, a binary classifier (C. albicans vs. C. tropicalis), evaluated in a cascade manner on reference-strain samples predicted as Positive by Model 1 (n = 36; cascade evaluation). Overall accuracy: 77.8% (95% CI 60.8–89.9%). (C) Confusion matrix for the preliminary clinical evaluation, comparing patient-level LSSA prediction against the Candida culture result for 20 vaginal discharge specimens (5 Candida culture-positive, 15 Candida culture-negative). Absolute counts are printed inside each cell. Cell shading intensity is proportional to the cell count within each sub-panel. All 95% CIs were calculated by the exact ClopperPearson binomial method.

Ann Clin Microbiol 2025;29(2):9. 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

Fig. 2. Performance of the two-stage laser scattering signal amplification (LSSA) deep-learning classifier on hold-out reference-strain and preliminary clinical specimens. (A) Confusion matrix for Model 1, a three-class classifier (Negative, <10³ colony-forming units [CFU] /mL; Positive—C. albicans, ≥10⁴ CFU/mL; Positive—Non-albicans, ≥10⁴ CFU/mL), evaluated on the 25% hold-out test partition of the Model 1 reference-strain dataset (n = 80 vials; reference strains: C. albicans ATCC 14053, N. glabratus KCTC 7219, C. tropicalis KCTC 7212, C. parapsilosis ATCC 22019, P. kudriavzevii ATCC 6258). Rows indicate culture-confirmed true class; columns indicate model prediction. Overall accuracy: 73.8% (95% confidence interval [CI] 62.7–83.0%). (B) Confusion matrix for Model 2, a binary classifier (C. albicans vs. C. tropicalis), evaluated in a cascade manner on reference-strain samples predicted as Positive by Model 1 (n = 36; cascade evaluation). Overall accuracy: 77.8% (95% CI 60.8–89.9%). (C) Confusion matrix for the preliminary clinical evaluation, comparing patient-level LSSA prediction against the Candida culture result for 20 vaginal discharge specimens (5 Candida culture-positive, 15 Candida culture-negative). Absolute counts are printed inside each cell. Cell shading intensity is proportional to the cell count within each sub-panel. All 95% CIs were calculated by the exact ClopperPearson binomial method.