Evaluation of the DendrisKIT®DP for the Diagnosis of Superficial Fungal Infections

Abstract

Conventional diagnosis of fungal infections of the skin, nail, and hair requires both expertise in mycology and prolonged cultures. We evaluated a new molecular tool based on an innovative technology, the DendrisKIT^®DP, combining a pan-fungal PCR, a DNA chip and a decision algorithm using machine learning, for the diagnosis of superficial fungal infections directly from clinical samples. It enables the simultaneous detection of Candida albicans and twelve dermatophytes, providing faster results than conventional techniques. Among 85 clinical samples (50 skin scrapings, 29 nail specimens, and 6 hair specimens) routinely tested by microscopic examination and cultures that were retrospectively tested by the DendrisKIT^®DP, we found a sensitivity of 83.9% and a specificity of 88.9%. This performance appeared satisfactory compared to microscopy and culture, and results were achieved much faster than with cultures, saving time for patient management. Moreover, thanks to the continuous improvement in the identification algorithm due to enriching the database, its performance is likely to be further enhanced.

Keywords: PCR kit; dermatophytosis; machine learning; molecular diagnosis; superficial fungal infection.

Figure 1

Isolate counts for each identified species according to the method. Δ Identified as Trichophyton spp by DendrisKIT^®DP and as T. rubrum (n = 6), T.mentagrophytes complex (n = 3), T. interdigitale (n = 2) by culture.