AI‐Assisted Plasmonic Enhanced Colorimetric Fluidic Device for Hydrogen Peroxide Detection from Cancer Cells

Abstract

Hydrogen peroxide (H2O2) is an essential molecule to various physiological processes and is commonly used for the detection and monitoring of glucose and cell viability. Furthermore, it is identified as a signal of oncogenic growth due to its widespread presence within the cancer cell environment. However, the low concentrations of H2O2 released by cancer cells' metabolism challenge current detection methods' capabilities and their practicality for translation to clinical applications. Colorimetric assays with simple readouts are a promising solution, provided that their sensitivity and rapidity in detecting H2O2 improve. Here, a plasmonic enhanced nanopatterned platform is proposed coupled with an Amplex Red assay to monitor the color change of H2O2 released from cancer cells. The nanopatterned platform embedded into a multiplexed microfluidic device enhances the kinetics of the reaction ≈7 times. This approach has reached a limit of detection of 1 pm when tested in breast (MCF‐7) and prostate (PC‐3) cancer media. The collected color images are processed and analyzed by a machine learning algorithm that categorizes them into “high” or “low‐to‐no” concentrations of H2O2 with 91% accuracy. This study is a step toward developing a device for highly sensitive H2O2 detection that is easily adaptable, user‐friendly, portable, and automated.