Comparative analysis and optimization of multi-model artificial intelligence for microscopic detection of fungal structures in superficial fungal infections

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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. Results YOLOv11 outperformed all other evaluated architectures. Detection was generally better at 400× than at 200× magnification. Notably, the F1 score for hyphae detection by YOLOv11 increased from 90.42% (200×) to 92.06% (400×). All models achieved higher accuracy for spores than hyphae. Patient-level confusion-matrix analysis confirmed YOLOv11 superiority. After optimizing an Intersection over Union (IoU) threshold of 0.25 and the maximum confidence (Cmax) strategy, 200× hyphae F1 rose to 97.84% and the positive predictive value (PPV) to 98.27%. Subgroup analysis showed robust performance across different diseases, with tinea capitis reaching the highest F1 score (98.53%). Conclusion The optimized YOLOv11 model demonstrates potential as a rapid and standardized supportive tool for the microscopic detection of fungal structures in superficial fungal infections. Integration into an online auxiliary diagnostic platform demonstrates the feasibility of real-time AI-assisted fungal detection, offering a standardized tool to support clinical decision-making. This work supports AI as a potential complement to clinical and laboratory expertise, pending further validation in prospective and multi-center settings, and highlights its potential to streamline fungal infection diagnostic workflows.