Abstract
| Original language | English |
|---|---|
| Article number | 1917 |
| Number of pages | 31 |
| Journal | Sensors |
| Volume | 19 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 23 Apr 2019 |
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Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing. / O'Sullivan, Shane; Ali, Zulfiqur; Jiang, Xiaoyi; Abdolvand, Reza; Ünlü, M. Selim; Plácido da Silva, Hugo; Baca, Justin T.; Kim, Brian ; Scott, Simon; Moradian, Sina; Sajid, Mohammed Imran; Mansoorzare, Hakhamanesh; Holzinger, Andreas.
In: Sensors, Vol. 19, No. 8, 1917, 23.04.2019.Research output: Contribution to journal › Article
TY - JOUR
T1 - Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing
AU - O'Sullivan, Shane
AU - Ali, Zulfiqur
AU - Jiang, Xiaoyi
AU - Abdolvand, Reza
AU - Ünlü, M. Selim
AU - Plácido da Silva, Hugo
AU - Baca, Justin T.
AU - Kim, Brian
AU - Scott, Simon
AU - Moradian, Sina
AU - Sajid, Mohammed Imran
AU - Mansoorzare, Hakhamanesh
AU - Holzinger, Andreas
PY - 2019/4/23
Y1 - 2019/4/23
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85065337098&partnerID=8YFLogxK
U2 - 10.3390/s19081917
DO - 10.3390/s19081917
M3 - Article
VL - 19
JO - Sensors
JF - Sensors
SN - 1424-3210
IS - 8
M1 - 1917
ER -