# Infrared Spectroscopy as a Promising Tool for Diagnosing and Typing Human Pathogenic Fungi

## TL;DR

Infrared spectroscopy is a fast and affordable method for identifying harmful fungi, which could improve diagnosis and treatment of fungal infections.

## Contribution

The paper reviews how infrared spectroscopy, combined with machine learning, can accurately identify and type fungal pathogens.

## Key findings

- IR spectroscopy can rapidly identify major fungal pathogens without reagents.
- Multivariate analysis and machine learning improve the accuracy of IR-based fungal identification.
- IR spectroscopy can also detect antifungal resistance and track outbreaks.

## Abstract

Fungal infections are increasingly recognised as a global health challenge, responsible for millions of cases annually and substantial mortality, especially in immunocompromised individuals. Yet, the diagnosis of these infections remains notoriously difficult, often delayed by slow culture‐based methods or hindered by the high cost and infrastructure demands of molecular diagnostics. In recent years, infrared (IR) spectroscopy has emerged as a promising alternative, offering rapid, cost‐effective and reagent‐free identification of human pathogenic fungi. This review provides an in‐depth examination of how IR‐based techniques, specifically, mid‐infrared (MIR) and near‐infrared (NIR) spectroscopy, are being applied in medical mycology. We explore the underlying chemical principles and highlight how recent advances in multivariate analysis and machine learning have enhanced their diagnostic accuracy. Studies have demonstrated the capacity of IR spectroscopy to accurately identify and type major fungal pathogens, while also providing insights into antifungal resistance profiles and outbreak tracking. While challenges remain, particularly regarding protocol standardisation and expansion of spectral databases, IR spectroscopy stands out as a valuable diagnostic strategy, especially in resource‐limited settings. By reducing diagnostic time and cost, and expanding accessibility, IR‐based methods have the potential to transform the clinical management of fungal infections, contributing to faster decision‐making and improved patient outcomes.