Next generation mycological diagnosis: Artificial intelligence-based classifier of the presence of Malassezia yeasts in tape strip samples.

BACKGROUND

Malassezia yeasts are almost universally present on human skin worldwide. While they can cause diseases such as pityriasis versicolor, their implication in skin homeostasis and pathophysiology of other dermatoses is still unclear. Their analysis using native microscopy of skin tape strips is operator dependent and requires skill, training and significant amounts of hands-on time.

OBJECTIVES AND

METHODS

To standardise and improve the speed and quality of diagnosis of Malassezia in skin tape strip samples, we sought to create an artificial intelligence-based algorithm for this image classification task. Three algorithms, each using different internal architectures, were trained and validated on a manually annotated dataset of 1113 images from 22 samples.

RESULTS

The Vision Transformer-based algorithm performed the best with a validation accuracy of 94%, sensitivity of 94.0% and specificity of 93.5%. Visualisations providing insight into the reasoning of the algorithm were presented and discussed.

CONCLUSION

Our image classifier achieved very good performance in the diagnosis of the presence of Malassezia yeasts in tape strip samples of human skin and can therefore improve the speed and quality of, and access to this diagnostic test. By expanding data sources and explainability, the algorithm could also provide teaching points for more novice operators in future.