AI-Assisted Microfluidic Wearable Sensor for Tear Biomarkers

The development of an artificial intelligence-assisted wearable microfluidic colorimetric sensor system (AI-WMCS) is a notable advancement in non-invasive health monitoring. This cutting-edge system is designed for the rapid, simultaneous, and accurate detection of key biomarkers in human tears, addressing the need for precise and real-time health monitoring. By integrating a flexible microfluidic epidermal patch with a smartphone-based cloud server data analysis system (CSDAS), the AI-WMCS offers a seamless solution for collecting and analyzing tear samples.

“The utilization of a wearable colorimetric biochemical sensor exhibits potential in achieving swift and concurrent detection of pivotal biomarkers in tears. Nevertheless, challenges arise in the collection, interpretation, and sharing of data from the colorimetric sensor, thereby restricting the practical implementation of this technology. To overcome these challenges, this research introduces an artificial intelligence-assisted wearable microfluidic colorimetric sensor system (AI-WMCS) for rapid, non-invasive, and simultaneous detection of key biomarkers in human tears“, the authors explained.

The AI-WMCS system comprises two main components: a flexible PDMS-based microfluidic device and an advanced data analysis system. The microfluidic chip is designed to fit comfortably under the eye, where it collects tear fluid and initiates colorimetric reactions. This non-invasive method ensures user comfort while providing accurate and rapid sample collection. The collected tear fluid interacts with chromogenic reagents on the patch, resulting in color changes that correspond to the concentrations of specific biomarkers such as vitamin C, pH (H+), Ca2+, and proteins.

A significant innovation in the AI-WMCS is its use of deep learning to enhance data accuracy. The system employs a combination of convolutional neural networks (CNN) and gated recurrent units (GRU), forming a robust neural network model known as CNN-GRU. This model processes the colorimetric data captured by a smartphone, correcting for errors caused by varying pH levels and color temperatures in different measurements. The AI component ensures that the system achieves high determination coefficients (R² values of 0.998 for pH and 0.994 for other biomarkers), indicating exceptional predictive accuracy.

The AI-WMCS system’s efficacy was validated through extensive experimentation. Researchers tested the device using artificial tear samples with varying concentrations of the target biomarkers. The results demonstrated that the system could accurately detect biomarker levels within physiological ranges, with minimal error margins. The AI-WMCS was also tested in real-world conditions with human subjects, where it consistently provided reliable readings, showcasing its potential for practical applications.

Beyond tear analysis, the AI-WMCS system holds promise for monitoring other biofluids such as sweat, saliva, and urine. This versatility makes it a valuable tool for personalized healthcare and precision medicine. By integrating with the Internet of Things (IoT), the AI-WMCS enables continuous, real-time health monitoring, paving the way for advancements in telehealth. This integration allows for seamless data collection and analysis, providing healthcare professionals with critical information for early disease detection and management.

One of the key challenges addressed by the AI-WMCS is the impact of environmental variables on sensor accuracy. Traditional colorimetric sensors can produce variable results due to changes in pH and ambient light conditions. The AI-WMCS overcomes these issues with its deep learning algorithm, which corrects for these variations, ensuring consistent and accurate readings. This capability is crucial for the device’s practical application in diverse settings and conditions.

The AI-assisted wearable microfluidic colorimetric sensor system represents a significant leap forward in non-invasive health monitoring technology. Its combination of a flexible microfluidic patch, advanced AI data analysis, and seamless integration with IoT platforms positions it as a powerful tool for future healthcare applications. By enabling accurate, real-time monitoring of key biomarkers in human tears and other biofluids, the AI-WMCS system supports the ongoing evolution towards personalized medicine and proactive health management. The research behind this innovative technology highlights the potential for AI and microfluidics to transform health monitoring, offering new avenues for early diagnosis and improved patient outcomes.

“The concept of the combination of deep-learning neural network-based artificial intelligence and flexible microfluidic colorimetric device presents a promising, convenient, and low-cost strategy for the assessment of ocular and systemic health through non-invasive monitoring of key biomarkers in human tears, providing research directions to advance the field of telehealth and personalized precision medicine.“, the authors concluded.

Figures are reproduced from Wang, Z., Dong, Y., Sui, X. et al. An artificial intelligence-assisted microfluidic colorimetric wearable sensor system for monitoring of key tear biomarkers. npj Flex Electron 8, 35 (2024). https://doi.org/10.1038/s41528-024-00321-3 under a CC BY 4.0 Attribution 4.0 International license.

Read the original article: An artificial intelligence-assisted microfluidic colorimetric wearable sensor system for monitoring of key tear biomarkers

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