Title
Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing.
Abstract
We review some emerging trends in transduction, connectivity and data analytics for Point-of-Care Testing (POCT) of infectious and non-communicable diseases. The patient need for POCT is described along with developments in portable diagnostics, specifically in respect of Lab-on-chip and microfluidic systems. We describe some novel electrochemical and photonic systems and the use of mobile phones in terms of hardware components and device connectivity for POCT. Developments in data analytics that are applicable for POCT are described with an overview of data structures and recent AI/Machine learning trends. The most important methodologies of machine learning, including deep learning methods, are summarised. The potential value of trends within POCT systems for clinical diagnostics within Lower Middle Income Countries (LMICs) and the Least Developed Countries (LDCs) are highlighted.
Year
DOI
Venue
2019
10.3390/s19081917
SENSORS
Keywords
Field
DocType
POCT,deep learning,artificial intelligence,photonics,mobile phone,microfluidics
Point-of-care testing,Data structure,Patient need,Data analysis,Artificial intelligence,Deep learning,Mobile phone,Engineering,Machine learning
Journal
Volume
Issue
ISSN
19
8.0
1424-8220
Citations 
PageRank 
References 
0
0.34
0
Authors
13