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. 2019 Apr 23;19(8):1917.
doi: 10.3390/s19081917.

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

Affiliations

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

Shane O'Sullivan et al. Sensors (Basel). .

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.

Keywords: POCT; artificial intelligence; deep learning; microfluidics; mobile phone; photonics.

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Conflict of interest statement

The authors declare no conflict of interest. This paper does not raise any ethical issues.

Figures

Figure 1
Figure 1
Recently developed sensors for molecular diagnostics. (a) Photonic crystal structure based on nanoimprint lithography for immunoassay. (b) An electrophotonic sensor that combines electrochemical and photonic characterization. Reprint from [38,39].
Figure 2
Figure 2
(a) Conceptual representation of SP-IRIS single molecule counting assay for protein biomarker detection; (b) Representative images at 1 pg/mL and 100 pg/mL target concentration; (c) Dilution curve for β-lactoglobulin in unprocessed serum and whole blood. Adapted with permission from Ref. [51]. Copyright (2013) American Chemical Society.
Figure 3
Figure 3
Block diagram of algorithm for nanoparticle detection and counting using z-stacks of incrementally defocused images. Adapted with permission from Ref. [53].
Figure 4
Figure 4
Zika virus sensor. (A) Test tubes placed on Isothermal hot plate are imaged using the smartphone camera with the LED acting as a light source. (B) Smartphone application interface. (C) Measured heat map of surface temperature indicating uniform heating with less than 1 °C temperature variation. (D) Off the shelf PCR polypropylene tubes. (E) Custom made laser-cut reaction wells. (F) Measurements show improved thermal management with custom reaction wells. (G) Smartphone wirelessly controls the heating lamp and excitation source and is also responsible for capturing and analyzing illuminated reagents. Reprint adapted with permission from [63].
Figure 5
Figure 5
Blood type detection using smartphone. (a) Paper sensor with blood-tracks visible in channels. (b) Smartphone app developed for interpreting the sensor’s result. (c) Detected blood type is displayed as text. Reprint adapted with permission from [67]. Copyright 2018 American Chemical Society.
Figure 6
Figure 6
3D printed real-time PCR machine for lentivirus detection and quantification. The small device is capable of quantifying different viral loads using fluorescence measurements of DNA. (a) Top view of the qPCR device with MicroView screen showing the status of the amplification cycle and fluorescence reading. (b) Bottom view of the qPCR machine displaying cartridge bottom, spacer, and inlet for centrifugal fan. (c) Dimensions of the cartridge. (d) Fluorescence measurement illustrating change in intensity for different virus concentrations. (e) Measured Cq for three concentrations of target DNA. Reprint from [24].
Figure 7
Figure 7
Portable genome sequencing for Ebola surveillance in Guinea. The system consisted of three MinION instruments, four laptops, a thermocycler for PCR, and other tools. Panel (a) shows that the authors used aircraft baggage to carry all instruments, reagents and disposable consumables. Panel (b) shows that the authors initially established the genomic surveillance laboratory in Donka Hospital, Conakry. Panel (c) is used to indicate that they later moved the laboratory to a dedicated sequencing laboratory in Coyah prefecture. Panel (d) shows an image of the room inside this laboratory where they separated the sequencing instruments (left side) from the PCR bench (right side). Power to the thermocycler was provided by an un-interruptable power supply, which can be observed in the middle of the room. (Photos were taken by Josh Quick and Sophie Duraffour.) Reprint from [153].

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