https://orcid.org/0000-0003-4059-4758
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Abstract
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.
Tweetable abstract
A high-speed microscopy system was constructed based on deep learning. With this system, fungal cells in blood can be detected and quantified directly with much higher sensitivity than traditional microscopes. Also, the effect of antifungal treatment can be monitored.
Graphical abstract
Supplementary data
To view the supplementary data that accompany this paper please visit the journal website at: www.future-science.com/doi/suppl/10.4155/bio-2023-0193
Author contributions
B Hu and L Wu designed and planned the study concept. R Liu, X Li, Y Liu, L Du and Y Zhu performed experiments and analyzed data. Y Liu, R Liu and Y Zhu drafted the manuscript. All authors jointly revised the content of the study and agreed to submit the manuscript.
Financial disclosure
This work was supported by the National Natural Science Foundation of China (82260715), the Guangxi Natural Science Foundation (2021GXNSFAA075038), the Science and Technology Program of Guangzhou, China (201903010039 and 202201020417) and National Natural Science Foundation Cultivation Project (2020GZRPYQN25). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
Competing interests disclosure
The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending or royalties.
Writing disclosure
No writing assistance was utilized in the production of this manuscript.
Ethical conduct of research
The experimental protocol was established according to the ethical guidelines of the Helsinki Declaration and was approved by the Human Ethics Committee of The Third Affiliated Hospital of Sun Yat-sen University ([2018]02-258-01). Written informed consent was obtained from individual or guardian participants.
Data sharing statement
The data used to support the findings of this study are available from the corresponding authors upon reasonable request.