Automatic identification of clinically important <i>Aspergillus</i> species by artificial intelligence-based image recognition: proof-of-concept study.

ABSTRACT: While morphological examination is the most widely used for Aspergillus identification in clinical laboratories, PCR-sequencing and MALDI-TOF MS are emerging technologies in more financially-competent laboratories. However, mycological expertise, molecular biologists and/or expensive equipment are needed for these. Recently, artificial intelligence (AI), especially image recognition, is being increasingly employed in medicine for fast and automated disease diagnosis. We explored the potential utility of AI in identifying Aspergillus species. In this proof-of-concept study, using 2813, 2814 and 1240 images from four clinically important Aspergillus species for training, validation and testing, respectively; the performances and accuracies of automatic Aspergillus

SUBMITTER: Tsang CC 

PROVIDER: S-EPMC11632928 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature