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

While morphological examination is the most widely used for <i>Aspergillus</i> 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 <i>Aspergillus</i> species. In this proof-of-concept study, using 2813, 2814 and 1240 images from four clinically important <i>Aspergillus</i> species for training, validation and testing, respectively; the performances and accuracies of automatic <i>Aspergillus</i> identification using colonial images by three different convolutional neural networks were evaluated. Results demonstrated that ResNet-18 outperformed Inception-v3 and DenseNet-121 and is the best algorithm of choice because it made the fewest misidentifications (<i>n</i> = 8) and possessed the highest testing accuracy (99.35%). Images showing more unique morphological features were more accurately identified. AI-based image recognition using colonial images is a promising technology for <i>Aspergillus</i> identification. Given its short turn-around-time, minimal demand of expertise, low reagent/equipment costs and user-friendliness, it has the potential to serve as a routine laboratory diagnostic tool after the database is further expanded.