Deep Learning-Enabled Swallowing Monitoring and Postoperative Recovery Biosensing System

This study introduces an innovative 3D-printed dry electrode tailored for biosensing in postoperative recovery scenarios. Fabricated through a drop-coating process, the electrode incorporates a novel 2D material, MXene, and PEDOT:PSS on a polylactide (PLA) substrate. The PEDOT:PSS layer functions as an effective oxidation barrier for MXene, thereby enhancing the electrode’s conductivity, biocompatibility, stability, and reusability. The design of the electrode is inspired by the paraboloidal dome-shaped suction cups found on tentacles of the octopus, a feature that substantially increases the surface area. These electrodes have been successfully integrated into a surface electromyography (sEMG) system, designed to monitor postoperative conditions in patients diagnosed with neck cancer or dysphagia. The system leverages a deep learning model to aid physicians in the quantitative assessment of post-surgical conditions of patients. Additionally, the study outlines a novel manufacturing approach for biosensing systems, demonstrating considerable promise in improving the utility in clinical environments.