Application progress of machine learning in point-of-care testing

Abstract

In recent years, point-of-care testing (POCT) has attracted more and more attention because it is cheap, fast and easy-to-operate. However, detection accuracy and reliability remain challenging for POCT. Machine learning methods are powerful for data processing and analysis, which is the potential to improve the accuracy and reliability of POCT greatly. Additionally, it is also possible to bring breakthroughs in remote medicine and data sharing fields for POCT. In this review, we describe the basic principles of machine learning algorithms and explain their advantages in POCT; then introduce the applications of machine learning in POCT, including paper-based assays, microfluidic lab-on-chip technologies, and wearable devices; after that, we also put forward suggestions on the selection of machine learning algorithms based on the data type and detection targets of POCT tasks; finally, we propose several directions of the future development of machine learning algorithms in POCT.