Abstract
Each year, one million Cryptococcal infections occur among people living with HIV/AIDS, resulting in nearly 625,000 fatalities due to low efficiency of clinical diagnostic procedures and misdiagnosis caused by faulty subjective judgment of manually enumerated imaging-based infection assays. In order to improve the diagnostic efficiency of non-immunodeficient patients infected with Cryptococcus, the present study developed an intelligent diagnostic system combined with a switch-controllable nanocatcher (MNP@PNIPAMAA-CAS) with high capture performance for Cryptococcus and convolutional neural network (CNN)-based artificial intelligence (AI), which was utilized to analyze the collected pathological images for subsequent diagnosis. This system benefitted from the high adsorption efficiency of the MNP@PNIPAMAA-CAS and the objective data processing ability of AI. The system’s detection limit was 1–4 cells /mL, the specificity was 97%, and the detection time was only 6 min. The intelligent diagnostic system will provide significant benefit to the diagnosis of clinical fungi due to its high sensitivity, high specificity, and superfast detection speed.
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