Background: Blood-invasive fungal infections can cause the death of patients, while diagnosis of fungal infections is challenging. Methods: A high-speed microscopy detection system was constructed that included a microfluidic system, a microscope connected to a high-speed camera and a deep learning analysis section. Results: For training data, the sensitivity and specificity of the convolutional neural network model were 93.5% (92.7–94.2%) and 99.5% (99.1–99.5%), respectively. For validating data, the sensitivity and specificity were 81.3% (80.0–82.5%) and 99.4% (99.2–99.6%), respectively. Cryptococcal cells were found in 22.07% of blood samples. Conclusion: This high-speed microscopy system can analyze fungal pathogens in blood samples rapidly with high sensitivity and specificity and can help dramatically accelerate the diagnosis of fungal infectious diseases.
- Plain language summary
Blood-invasive fungal infections can be lethal and their diagnosis is challenging. The existing detection methods have shortcomings such as having unsatisfactory sensitivity, being time-consuming and having detection limitations. In this study, a high-speed microscopy system was constructed based on deep learning. With this system, fungal cells in the blood can be detected and quantified directly with much higher sensitivity than traditional microscopes. Also, the effect of antifungal treatment can be monitored.