Frontiers in cellular and infection microbiology
Comparative analysis and optimization of multi-model artificial intelligence for microscopic detection of fungal structures in superficial fungal infections
By Liu M, He W, Ni L and 4 others.
Abstract
Background
Superficial fungal infections present a growing threat to global public health. Traditional diagnostic techniques, including microscopy and culture, are time-consuming and subjective, often limiting diagnostic accuracy. Artificial intelligence (AI)-driven diagnostic tools offer a promising adjunct to improve efficiency and standardization of microscopic fungal detection.
Methods
We systematically compared six deep learning models, including the You Only Look Once (YOLO) series, the Convolutional Neural Network (CNN) series and the Detection Transformer (DETR). Three evaluation datasets were constructed. Datasets 1 and 2 (derived from 4000 hyphae and 4500 spore images) were used to assess image-level detection using sensitivity, precision and F1 score. Dataset 1 was manually annotated for the YOLO series and DETR; Dataset 2 (unannotated) served as the CNN series' benchmark. Dataset 3 (1, 870 specimens from 1, 730 patients) was used to evaluate sample-level performance via confusion matrix analysis for the YOLO series and DETR models, employing medical records as the reference standard. The best-performing model was optimized and deployed into an online auxiliary diagnostic platform for real-time clinical decision support.