Rapid Antifungal Susceptibility Classification of Aspergillus Species Using AI-Assisted Analysis of Routine MALDI-TOF...

The increasing prevalence of antifungal-resistant Aspergillus isolates underscores the need for rapid antifungal susceptibility testing. In this proof-of-concept study, we evaluated whether artificial intelligence (AI) can enable rapid antifungal susceptibility classification of Aspergillus species using routine matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS).

Models were developed using MALDI-TOF MS spectra for 240 isolates (Aspergillus flavus N=126, A. fumigatus N=75, Aspergillus lentulus N=39). Their performance in predicting susceptibility to four antifungal agents (voriconazole, posaconazole, itraconazole, and amphotericin B) was evaluated using a validation set comprising A. flavus (N=24), A. fumigatus (N=24), and A. lentulus (N=22), based on categorical agreement (CA), very major error (VME), major error (ME), sensitivity, and specificity.

Although spectrum-level evaluation showed excellent analytical discrimination, isolate-level clinical evaluation demonstrated limited performance across seven species-antifungal agent combinations. Across these combinations, the AI models achieved a mean CA of 56.0%, with VME and ME rates of 53.3% and 45.2%, respectively. Sensitivity ranged from 0% to 100% and specificity from 18.2% to 87.5%, indicating inconsistent susceptibility classification performance across species-antifungal agent combinations.

AI-assisted analysis of routine MALDI-TOF MS spectra showed the feasibility of classifying antifungal susceptibility in Aspergillus spp. However, its clinical performance across species-antifungal agent combinations was limited. Further optimization, evaluation with larger multicenter datasets, and external validation are required before considering clinical adoption.