Developments in transduction, connectivity and AI/machine learning for point-of-care testing

Shane O’Sullivan; Zulfiqur Ali; Xiaoyi Jiang; Reza Abdolvand; M. Selim Ünlü; Hugo Plácido Da Silva; Justin T. Baca; Brian Kim; Simon Scott; Mohammed Imran Sajid; Sina Moradian; Hakhamanesh Mansoorzare; Andreas Holzinger

doi:10.3390/s19081917

Developments in transduction, connectivity and AI/machine learning for point-of-care testing

Shane O’Sullivan^*, Zulfiqur Ali, Xiaoyi Jiang, Reza Abdolvand, M. Selim Ünlü, Hugo Plácido Da Silva, Justin T. Baca, Brian Kim, Simon Scott, Mohammed Imran Sajid, Sina Moradian, Hakhamanesh Mansoorzare, Andreas Holzinger

^*Corresponding author for this work

Institute of Interactive Systems and Data Science (7060)

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	1917
Journal	Sensors
Volume	19
Issue number	8
DOIs	https://doi.org/10.3390/s19081917
Publication status	Published - 2 Apr 2019

Keywords

Artificial intelligence
Deep learning
Microfluidics
Mobile phone
Photonics
POCT

ASJC Scopus subject areas

Analytical Chemistry
Biochemistry
Atomic and Molecular Physics, and Optics
Instrumentation
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3390/s19081917Licence: CC BY 4.0

Cite this

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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.",

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AU - Ali, Zulfiqur

AU - Jiang, Xiaoyi

AU - Abdolvand, Reza

AU - Ünlü, M. Selim

AU - Silva, Hugo Plácido Da

AU - Baca, Justin T.

AU - Kim, Brian

AU - Scott, Simon

AU - Sajid, Mohammed Imran

AU - Moradian, Sina

AU - Mansoorzare, Hakhamanesh

AU - Holzinger, Andreas

PY - 2019/4/2

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KW - Deep learning

KW - Microfluidics

KW - Mobile phone

KW - Photonics

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Developments in transduction, connectivity and AI/machine learning for point-of-care testing

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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