Invasive fungal infections represent a growing global health challenge, making rapid and accurate diagnosis essential for timely initiation of appropriate therapy. Histopathology remains central to distinguishing true infection from colonization or contamination, and morphometric analysis may facilitate fungal identification. Furthermore, advances in digital pathology, artificial intelligence (AI), and machine learning have increased interest in morphometric approaches for fungal differentiation. However, their application requires the identification of morphometric parameters that reliably discriminate between fungal genera.

In the present study 8772 measurements across nine morphometric parameters in 76 human and veterinary histological cases of invasive fungal infections caused by Candida, Aspergillus, and Mucorales were performed .

The aims were to comprehensively characterize fungal morphology, evaluate the diagnostic utility of different morphometric parameters, and develop a simple decision tree for fungal differentiation.

Overall, the measurements made in this study demonstrated good interobserver agreement. Only minor morphometric variation was observed throughout the tissue blocks, indicating that assessment of a single histological section can be sufficient for morphometric analysis. In addition, discriminatory morphometric parameters were identified, enabling the development of a diagnostic decision tree that differentiated Mucorales from Aspergillus and Candida in 95% of the cases.

These findings demonstrate that quantitative morphometry provides reliable and clinically relevant information for fungal identification in histological sections. The identified morphometric parameters may form the basis for future image analysis and machine learning approaches, enabling faster and more accurate diagnosis of invasive fungal infections.