Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing
Abstract
:1. Introduction
2. Patient Need for POCT
2.1. Infectious Diseases
2.2. Non-Communicable Diseases
3. Developments Towards Portable Diagnostics
4. Novel Photonic Systems
4.1. Cavity Enhanced Absorption Spectrometry (CEAS)
4.2. Plasmonics
4.3. Digital Microarrays—Interferometric Reflectance Imaging Sensor (IRIS)
5. Mobile Phone Reader and Device Connectivity for POCT
5.1. Components of Mobile Phones Used in POCT
5.2. Auxiliary Hardware for Mobile Phone-Based POCT
6. Data Analytics for POCT
6.1. Data Management
- Store and allow access to comprehensive health data including the medical history of the patient;
- Harmonize with the workflow of health organizations and provide efficient interaction experience;
- Assist in administrative tasks such as billing, insurance claim filing, and scheduling;
- Allow efficient access and assist in statistical analysis of data.
6.2. Data-Driven Decision-Making Using Machine Learning Techniques
6.3. Application Scenarios
6.4. Case Studies
7. POCT for Clinical Diagnostics within Lower Middle Income Countries (LMICs) and Least Developed Countries (LDCs)
7.1. Overall Framework
7.2. Open Data Platforms for Infectious Diseases
7.3. Nucleic Acid Amplification Techniques
7.4. Rapid DNA/RNA Sequencing for Outbreak Response
7.5. Antimicrobial Resistance
8. Final Considerations and Future Outlook
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Cancer Biomarkers | Biomarker | Normal Values in Blood |
---|---|---|
PSA | 4 ng/mL | |
IL-6 | 6 pg/mL | |
IL-8 | 13–20 pg/mL | |
MMP-2 | 367–770 ng/mL | |
MMP-3 | 15–72 ng/mL | |
Alpha-fetoprotein | <20 ng/mL | |
CEA | 5 ng/mL | |
CA-125 | 35 U/mL | |
Cardiac Biomarkers | CRP | 3 mg/mL |
NT-proBNP | 1 ng/mL | |
CTnT | 0.3 ng/mL | |
CTnl | 0.01–0.1 ng/mL | |
Myoglobin | 50–100 ng/mL |
Title | Analytic Approach | Sample Type | Result and Time | Format |
---|---|---|---|---|
Ultra-Rapid Infection Confirmation and Phenotypical AST by Microbe Mass Measurement [http://www.lifescaleinstruments.com/ https://patents.google.com/patent/US20150072373A1/en] | Microfluidic, mass detection of bacterial growth | blood culture, urine culture demonstrated (extension to urine screen, cerebral spinal fluid, pleural fluid planned) | Phenotypic antibiotic resistance (minimum inhibitory concentration) Time to result: 3–3.5 h | desktop device |
Single Cell Biometric Analysis for Rapid ID/AST [http://klarisdx.com/ https://patents.google.com/patent/US20180172675A1/en] | Microfluidic partitioning of single cells; detection of phenotypic antimicrobial susceptibility with redox-sensitive viability dye | not specified | Phenotypic antibiotic resistance, pathogen ID Time to result: 4 h | desktop device |
Fully Automated Pathogen ID and AST Directly from Blood and Urine [http://www.genefluidics-lifescience.com/] | Electrochemical sandwich hybridization of 16S ribosomal RNA | unprocessed urine demonstrated, (whole blood in development) | Pathogen genus/species by 16S homology Time to result: 30 min for pathogen ID, 90 min for resistance profile (for urine) | desktop device with disposable sensor array chip |
Patient-side, Disposable, Molecular PCR Diagnostic Device for Neisseria Gonorrhea and Drug Resistance Markers [https://www.sbir.gov/sbirsearch/detail/1323659 https://patents.google.com/patent/US9623415B2/en] | Miniaturised PCR | genital tract swab | Pathogen ID, (ciprofloxacin resistance in development) Time to result: 25 min | single-use, disposable POC device |
Host Gene Expression to Classify Viral and Bacterial Infection Using Rapid Multiplex PCR [https://www.predigen.com/] Journal of Clinical Microbiology Jan 2010, 48 (1) 26-33; DOI: 10.1128/JCM.01447-09 | PCR of host gene expression patterns | blood | confirmation of viral-type host response pattern, determination of viral/bacterial co-infection Time to result: 45 min | desktop/multiplex PCR equipment |
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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.; et al. Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing. Sensors 2019, 19, 1917. https://doi.org/10.3390/s19081917
O’Sullivan S, Ali Z, Jiang X, Abdolvand R, Ünlü MS, Plácido da Silva H, Baca JT, Kim B, Scott S, Sajid MI, et al. Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing. Sensors. 2019; 19(8):1917. https://doi.org/10.3390/s19081917
Chicago/Turabian StyleO’Sullivan, Shane, Zulfiqur Ali, Xiaoyi Jiang, Reza Abdolvand, M Selim Ünlü, Hugo Plácido da Silva, Justin T. Baca, Brian Kim, Simon Scott, Mohammed Imran Sajid, and et al. 2019. "Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing" Sensors 19, no. 8: 1917. https://doi.org/10.3390/s19081917
APA StyleO’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. (2019). Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing. Sensors, 19(8), 1917. https://doi.org/10.3390/s19081917