Evidence map›Paper›PMID 39852070›Full record

ArticleBiosensors2025

Machine Learning-Based Quantification of Lateral Flow Assay Using Smartphone-Captured Images.

Anne M Davis, Asahi Tomitaka

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biosensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
  2. Review
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  4. Article
  5. Emerging point-of-care technologies for bacterial pathogen detection.Journal of Zhejiang University. Science. B · 2026
    Review
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  9. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Anne M DavisDepartment of Computer and Information Sciences, University of Houston-Victoria, Victoria, TX 77904, USA.
Asahi TomitakaDepartment of Computer and Information Sciences, University of Houston-Victoria, Victoria, TX 77904, USA.

Funding

University of Houston-Victoria Internal Research Grant
6 · The paper itself

Abstract

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.

Indexed as

Machine LearningSmartphoneDeep LearningHumansNeural Networks, ComputerCNNdeep learninglateral flow assaymachine learning

Identifiers

PMID39852070
PMCPMC11763061

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.