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

Research output: Contribution to journalArticleResearchpeer-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 languageEnglish
Article number1917
JournalSensors (Switzerland)
Volume19
Issue number8
DOIs
Publication statusPublished - 2 Apr 2019

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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

Cite this

O’Sullivan, S., Ali, Z., Jiang, X., Abdolvand, R., Ünlü, M. S., Silva, H. P. D., ... Holzinger, A. (2019). Developments in transduction, connectivity and AI/machine learning for point-of-care testing. Sensors (Switzerland), 19(8), [1917]. https://doi.org/10.3390/s19081917