Deep Learning Assisted Heteroporous Covalent Organic Framework Hydrogels Enable Dual‐Mode Tracking and Analysis to Parkinson's Management

ABSTRACT Resting tremor and dopamine dysregulation are closely linked in the pathophysiology of Parkinson's disease (PD), yet detecting their subtle early manifestations remains highly challenging. Herein, a wearable hydrogel (Gel@COF), designed for the concurrent monitoring of dopamine levels and resting tremor to enable early PD detection and intervention, is reported. The Gel@COF is fabricated by encapsulating a covalent organic framework (COF‐101) functionalized with phenylboronic acid within a self‐crosslinked hydrogel. Notably, the COF‐101 features a unique fractal and heteroporous structure. The fractal morphology provides abundant dopamine recognition sites via the grafted phenylboronic acid, while the heteroporous structure significantly enhances proton conductivity. Thus, Gel@COF achieves high sensitivity at 1% strain and delivers a fast response/recovery time of 16.0 ms. Furthermore, the unique topological structure of COF‐101 endows Gel@COF with visual fluorescence changes. After integrating a convolutional neural network algorithm with Gel@COF, its evaluation and prediction accuracy for PD was rapidly improved to over 95%. This dual‐functional wearable gel offers patients real‐time and convenient rehabilitation monitoring, demonstrating significant potential for translational applications.