Comprehensive Serum Analysis via an AI-Assisted Magnetically Driven SERS Platform for the Diagnosis and Etiological Differentiation of Childhood Epilepsy

Timely and accurate diagnosis of childhood epilepsy and identification of its etiology are crucial for early interventional treatment, yet still, effective detection methods are lacking. Blood analysis is a promising strategy for disease diagnosis. However, due to the complex composition and lack of definite childhood epilepsy diagnostic markers in serum, comprehensively profiling serum molecular signals to accurately reveal diagnostic information is still challenging. Herein, we developed a novel magnetically driven SERS platform, which utilized specially designed branched Au nanostructure-embedded magnetic microspheres to achieve simultaneous detection of small molecules and biomacromolecules in serum, thus providing comprehensive serum molecular SERS signals. By using this platform, the SERS data sets of serum samples from 90 healthy controls and 585 epileptic patients were collected to train a self-built lightweight convolutional neural network (MLS-CNN) model, which successfully identified the serum epileptic diagnostic and etiological differentiation information, including causes of autoimmune encephalitis, febrile infection, developmental disability, structural brain lesions, and unknown etiology. The MLS-CNN model exhibits excellent diagnostic accuracy (100%) and etiological differentiation accuracy (>89%) for epilepsy. This AI-assisted magnetically driven SERS platform for comprehensively profiling the molecular information on serum might provide a novel strategy for childhood epilepsy diagnosis and etiological identification.