Issue

Vol. 16 (2024)

Light-Activated Virtual Sensor Array with Machine Learning for Non-Invasive Diagnosis of Coronary Heart Disease

https://doi.org/10.1007/s40820-024-01481-7
Jiawang Hu
Department of Chemical Engineering, Tsinghua University, Beijing 100084, People’s Republic of China
Hao Qian
Department of Cardiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, People’s Republic of China
Sanyang Han
Institute of Biopharmaceutical and Health Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, People’s Republic of China
Ping Zhang
Department of Cardiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, People’s Republic of China
Yuan Lu
Department of Chemical Engineering, Tsinghua University, Beijing 100084, People’s Republic of China

Corresponding Author: Yuan Lu

yuanlu@tsinghua.edu.cn

Nano-Micro Letters, Vol. 16 (2024), Article Number: 274

Abstract

Early non-invasive diagnosis of coronary heart disease (CHD) is critical. However, it is challenging to achieve accurate CHD diagnosis via detecting breath. In this work, heterostructured complexes of black phosphorus (BP) and two-dimensional carbide and nitride (MXene) with high gas sensitivity and photo responsiveness were formulated using a self-assembly strategy. A light-activated virtual sensor array (LAVSA) based on BP/Ti3C2Tx was prepared under photomodulation and further assembled into an instant gas sensing platform (IGSP). In addition, a machine learning (ML) algorithm was introduced to help the IGSP detect and recognize the signals of breath samples to diagnose CHD. Due to the synergistic effect of BP and Ti3C2Tx as well as photo excitation, the synthesized heterostructured complexes exhibited higher performance than pristine Ti3C2Tx, with a response value 26% higher than that of pristine Ti3C2Tx. In addition, with the help of a pattern recognition algorithm, LAVSA successfully detected and identified 15 odor molecules affiliated with alcohols, ketones, aldehydes, esters, and acids. Meanwhile, with the assistance of ML, the IGSP achieved 69.2% accuracy in detecting the breath odor of 45 volunteers from healthy people and CHD patients. In conclusion, an immediate, low-cost, and accurate prototype was designed and fabricated for the noninvasive diagnosis of CHD, which provided a generalized solution for diagnosing other diseases and other more complex application scenarios.


Highlights:
1 Photoresponsive black phosphorus (BP)/Ti3C2Tx composites were synthesized by a self-assembly strategy.
2 Enhanced gas sensitive property was achieved by visible light modulation.
3 Light activated virtual sensor array was fabricated based on BP/Ti3C2Tx composite.
4 Diagnosis of coronary heart disease was achieved with the help of machine learning.

Keywords

Black phosphorus/MXene heterostructures Light-activated virtual sensor array Diagnosis of coronary heart disease Machine learning
Hu, Jiawang, Hao Qian, Sanyang Han, Ping Zhang, and Yuan Lu. 2024. “Light-Activated Virtual Sensor Array With Machine Learning for Non-Invasive Diagnosis of Coronary Heart Disease”. Nano-Micro Letters 16 (August):274. https://doi.org/10.1007/s40820-024-01481-7.
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