AI-enabled New Sensing Technology: Colorimetric Analysis of Exosomes for Precise Diagnosis of Breast Cancer

Abstract

Colorimetric sensors are widely utilized in biomedical detection owing to simple operation and intuitive visual results. However, their inherent limitation in the output signal makes sensors insufficient in handling multiple biomarker combination information required for the diagnosis of highly heterogeneous diseases like breast cancer. To overcome the limitation, this study draws inspiration from the mechanism how human visual system perceives a myriad of colors using only three cone cell types. So, we propose a novel AI-enabled bionic mixed-color sensing technology for accurate breast cancer subtyping through exosome (EXO) analysis. This technology gives EXO a unique "mixed color fingerprint" by simultaneously binding color-coded nanoprobes (Ab-DyeNPs) targeting different proteins (PD-L1, EpCAM, and HER-2) on the EXO. This mixed-color signal is subsequently recognized by computer vision and analyzed using machine learning algorithms to yield a diagnostic conclusion. For cell line models, this technology accurately differentiates various breast cancer subtypes, reaching up to 100% accuracy. More importantly, it achieved 96.7% accuracy in subtyping clinical samples, outperforming both single-target and multi-target analysis strategies. This study transforms complex multi-dimensional biological information into intuitive visual data, providing a rapid and highly accurate novel tool for precise subtyping of breast cancer, which holds significant potential for clinical application.