Comparative analysis and optimization of multi-model artificial intelligence for microscopic detection of fungal structures in superficial fungal infections

Background

Superficial fungal infections present a growing threat to global public health. Traditional diagnostic techniques, including microscopy and culture, are time-consuming and subjective, often limiting diagnostic accuracy. Artificial intelligence (AI)-driven diagnostic tools offer a promising adjunct to improve efficiency and standardization of microscopic fungal detection.