A high-speed microscopy system based ondeep learning to detect yeast-like fungi cellsin blood: supplementary materials

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.