Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing.
Shane O'SullivanZulfiqur AliXiaoyi JiangReza AbdolvandM Selim ÜnlüHugo Plácido da SilvaJustin T BacaBrian KimSimon ScottMohammed Imran SajidSina MoradianHakhamanesh MansoorzareAndreas HolzingerPublished in: Sensors (Basel, Switzerland) (2019)
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.
Keyphrases
- big data
- machine learning
- artificial intelligence
- deep learning
- resting state
- electronic health record
- white matter
- functional connectivity
- high throughput
- gold nanoparticles
- high resolution
- physical activity
- convolutional neural network
- case report
- mental health
- data analysis
- label free
- multiple sclerosis
- liquid chromatography