Deep Learning in Fungal Diagnostics: From Microscopy to Antifungal Resistance Prediction

Invasive fungal infections cause approximately 1.5 billion cases each year, with an estimated 1.6 million deaths. Classic diagnostic methods, such as culture, direct microscopy, and antigen testing, largely rely on professional experience and are often too slow. The field of fungal diagnostics is increasingly using deep learning (DL), a class of artificial intelligence based on multilayered neural architectures. Recent developments in DL applications to clinical mycology are examined in this review, including automated colony morphology analysis, MALDI-TOF mass spectrometry spectral classification, Raman spectroscopy, microscopy, molecular diagnostics, and antifungal resistance prediction. Aspergillus, Candida, Cryptococcus neoformans, Pneumocystis jirovecii, and dermatophytes are among the important pathogens evaluated for validated performance benchmarks. We also address the main barriers to clinical implementation, which include a lack of large annotated datasets, cross-site generalizability, interpretability requirements, and regulatory frameworks. We conclude that, depending on carefully selected open-access datasets and future clinical validation, DL has transformative potential across several mycological diagnostic platforms.