Dual-mode Identification of Ischemic Stroke based on Urine SERS Spectra and Carotid B-Ultrasound

Achieving noninvasive high-frequency monitoring of ischemic stroke (IS) remains a major clinical challenge for timely intervention and precise secondary prevention. Establishing precise correlations between patients' systemic microscopic molecular fingerprints and localized macroscopic organ pathological events is essential to overcome the limitations of single- modal detection and enhance the efficacy of clinical risk assessment. However, due to the complexity of heterogeneous data, effectively integrating the cross-dimensional “molecular-imaging” data remains a critical bottleneck in achieving this goal. Here, we present a method for accurately identification of IS that utilized machine learning (ML) based methods to surface-enhanced Raman spectroscopy (SERS) of urine (one-dimensional) and carotid artery B-ultrasound images (CBI) (two-dimensional). Through a simple ML workflow, we analyzed 10,100 SERS spectra and 481 CBI images from 101 participants. This technology achieved an outstanding identification accuracy of 92% and an AUC value of 0.95, significantly outperforming the evaluation results of SERS spectra alone (AUC 0.92) or CBI alone (AUC 0.88). In addition, SERS spectra combined with liquid chromatography-mass spectrometry (LC-MS) technology identified significant intergroup differences in the levels of arginine, lysine, and aspartic acid between the HC group and the IS group. The multi-dimensional data fusion strategy proposed in this study effectively bridges the information gap between traditional molecular detection and clinical phenotypes by systematically correlating micro-fluidic biomarkers with macro-organ imaging features. This approach provides a novel, non-invasive, and highly accurate tool for risk stratification and clinical decision-making in IS. Keywords: ischemic stroke, urine, surface-enhanced Raman spectroscopy (SERS), carotid artery B-ultrasound image (CBI), machine learning,