A Smart Lab on a Wearable Microneedle Patch with Convolutional Neural Network-Enhanced Colorimetry for Early Warning of Syndrome of Inappropriate Antidiuretic Hormone Secretion

Frequent nocturia-induced nighttime visits aggravate falls in seniors, requiring synthetic antidiuretic drugs that risk the dangerous syndrome of inappropriate antidiuretic hormone secretion (SIADH), thus the detection of sodium ion and uric acid alterations during treatment is obligatory for drug-safe management. Herein, we design a convolutional neural network (CNN)-enhanced smart wearable microneedle array-based colorimetric (WMNC) sensor to independently detect in vivo interstitial fluid (ISF) sodium ions and uric acid alterations. The WMNC sensor is composed of a vacuum tube-driven microneedle array patch and a built-in colorimetric sensing paper, enabling an efficient ISF extraction and rapid colorimetric assay. Furthermore, leveraging self-designed CNNs, the WMNC sensor efficiently eliminates the influence of ambient light on colorimetric outcomes, facilitating a rapid and accurate colorimetric result classification. This study provides an ISF-based rapid, intuitionistic, user-friendly, wearable point-of-care technique for the elderly suffering from nocturia in monitoring their health status for early warnings of SIADH. A convolutional neural network (CNN)-enhanced smart wearable microneedle array-based colorimetric (WMNC) sensor is developed to detect in vivo interstitial fluid (ISF) sodium ions and uric acid alterations, aiming at the assistance of the elderly suffering from nocturia in monitoring their health status, ensuring drug-use safety. image