High Throughput Blood Analysis Based on Deep Learning Algorithm and Self-Positioning Super-Hydrophobic SERS Platform for Non-Invasive Multi-Disease Screening

Abstract

Blood analysis is crucial for early cancer screening and improving patient survival rates. However, developing an effective strategy for early cancer detection using high-throughput blood analysis is still challenging. Herein, a novel automatic super-hydrophobic platform is developed together with a deep learning (DL)-based label-free serum and surface-enhanced Raman scattering (SERS), along with an automatic high-throughput Raman spectrometer to build an effective point-of-care diagnosis system. A total of 695 high-quality serum SERS spectra are obtained from 203 healthy volunteers, 77 leukemia M5, 94 hepatitis B virus, and 321 breast cancer patients. Serum SERS signals from the normal (n = 183) and patient (n = 443) groups are used to assess the DL model, which classify them with a maximum accuracy of 100%. Furthermore, when SERS is combined with DL, it exhibits excellent diagnostic accuracy (98.6%) for the external held-out test set, indicating that this method can be used to develop a high throughput, rapid, and label-free tool for screening diseases.