Machine Learning-Based Quantification of Lateral Flow Assay Using Smartphone-Captured Images Article Swipe
Andrew S. Davis
,
Asahi Tomitaka
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/bios15010019
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/bios15010019
Lateral flow assay has been extensively used for at-home testing and point-of-care diagnostics in rural areas. Despite its advantages as convenient and low-cost testing, it suffers from poor quantification capacity where only yes/no or positive/negative diagnostics are achieved. In this study, machine learning and deep learning models were developed to quantify the analyte load from smartphone-captured images of the lateral flow assay test. The comparative analysis identified that random forest and convolutional neural network (CNN) models performed well in classifying the lateral flow assay results compared to other well-established machine learning models. When trained on small-size images, random forest models excelled CNN models in image classification. Contrarily, CNN models outperformed random forest models in classifying noisy images.
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Metadata
- Type
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- Language
- en
- Landing Page
- https://doi.org/10.3390/bios15010019
- OA Status
- gold
- Cited By
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- References
- 28
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4406102618Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/bios15010019Digital Object Identifier
- Title
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Machine Learning-Based Quantification of Lateral Flow Assay Using Smartphone-Captured ImagesWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-04Full publication date if available
- Authors
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Andrew S. Davis, Asahi TomitakaList of authors in order
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https://doi.org/10.3390/bios15010019Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/bios15010019Direct OA link when available
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Random forest, Convolutional neural network, Artificial intelligence, Computer science, Deep learning, Machine learning, Pattern recognition (psychology)Top concepts (fields/topics) attached by OpenAlex
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9Total citation count in OpenAlex
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2025: 9Per-year citation counts (last 5 years)
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28Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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