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Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing

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Department of Pathology, Faculdade de Medicina, Universidade de São Paulo, São Paulo 05508-060, Brazil
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Healthcare Innovation Centre, Teesside University, Middlesbrough TS1 3BX, UK
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Faculty of Mathematics and Computer Science, University Münster, 48149 Münster, Germany
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Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA
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Department of Electrical and Computer Engineering and Biomedical Engineering, Boston University, Boston, MA 02215, USA
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IT—Instituto de Telecomunicações, 1049-001 Lisbon, Portugal
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Department of Emergency Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
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Department of Upper GI Surgery, Wirral University Teaching Hospital, Wirral CH49 5PE, UK
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Institute for interactive Systems and Data Science, Graz University of Technology, 8074 Graz, Austria
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Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, 8036 Graz, Austria
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(8), 1917; https://doi.org/10.3390/s19081917
Received: 29 January 2019 / Revised: 2 April 2019 / Accepted: 2 April 2019 / Published: 23 April 2019
(This article belongs to the Section Biosensors)
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. View Full-Text
Keywords: POCT; deep learning; artificial intelligence; photonics; mobile phone; microfluidics POCT; deep learning; artificial intelligence; photonics; mobile phone; microfluidics
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O’Sullivan, S.; Ali, Z.; Jiang, X.; Abdolvand, R.; Ünlü, M.S.; Plácido da Silva, H.; Baca, J.T.; Kim, B.; Scott, S.; Sajid, M.I.; Moradian, S.; Mansoorzare, H.; Holzinger, A. Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing. Sensors 2019, 19, 1917.

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