Intelligent Clinical Lab for the Diagnosis of Post-Neurosurgical Meningitis Based on Machine-Learning-Aided Cerebrospinal Fluid Analysis

Post-neurosurgical meningitis (PNM) often leads to serious consequences; unfortunately, the commonly used clinical diagnostic methods of PNM are time-consuming or have low specificity. To realize the accurate and convenient diagnosis of PNM, herein, we propose a comprehensive strategy for cerebrospinal fluid (CSF) analysis based on a machine-learning-aided cross-reactive sensing array. The sensing array involves three Eu³⁺-doped metal-organic frameworks (MOFs), which can generate specific fluorescence responding patterns after reacting with potential targets in CSF. Then, the responding pattern is used as learning data to train the machine learning algorithms. The discrimination confidence for artificial CSF containing different components of molecules, proteins, and cells is from 81.3 to 100%. Furthermore, the machine-learning-aided sensing array was applied in the analysis of CSF samples from post-neurosurgical patients. Only 25 μL of CSF samples was needed, and the samples could be robustly classified into "normal," "mild," or "severe" groups within 40 min. It is believed that the combination of machine learning algorithms with robust data processing capability and a lanthanide luminescent sensor array will provide a reliable alternative for more comprehensive, convenient, and rapid diagnosis of PNM.