Machine learning in point-of-care testing: innovations, challenges, and opportunities
University of California, Los Angeles · California NanoSystems Institute · +5 more institutions
Abstract
The landscape of diagnostic testing is undergoing a significant transformation, driven by the integration of artificial intelligence (AI) and machine learning (ML) into decentralized, rapid, and accessible sensor platforms for point-of-care testing (POCT). The COVID-19 pandemic has accelerated the shift from centralized laboratory testing but also catalyzed the development of next-generation POCT platforms that leverage ML to enhance the accuracy, sensitivity, and overall efficiency of point-of-care sensors. This Perspective explores how ML is being embedded into various POCT modalities, including lateral flow assays, vertical flow assays, nucleic acid amplification tests, and imaging-based sensors,…
Citation impact
- FWCI
- 83.15
- Percentile
- 100%
- References
- 233
Authors
15- GHGyeo‐Re HanCorresponding
University of California, Los Angeles
- AGArtem Goncharov
University of California, Los Angeles
- MEMerve Eryılmaz
University of California, Los Angeles
- SYShun Ye
California NanoSystems Institute, University of California, Los Angeles
- BPBarath Palanisamy
California NanoSystems Institute, University of California, Los Angeles
Topics & keywords
- Point-of-care testing
- Computer science
- Leverage (statistics)
- Coronavirus disease 2019 (COVID-19)
- Modalities
- Health care
- Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
- Point of care