Evidence map›Paper›PMID 39365798›Full record

ArticlePloS one2024

Development of a smartphone enabled, paper-based quantitative diagnostic assay using the HueDx color correction system.

Nidhi Menon, David Beery, Prava Sharma, Adrian Crutchfield, Leah Kim, Aaron Lauer, Ayesha Azimuddin, Brianna Wronko-Stevens

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Hyperspectral Information Extraction With Full Resolution From Arbitrary Photographs.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2025
    Article
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

8 authors.

Nidhi MenonHueDx, Inc., Philadelphia, Pennsylvania, United States of America.ORCID 0000-0001-6340-684X
David BeeryHueDx, Inc., Philadelphia, Pennsylvania, United States of America.
Prava SharmaHueDx, Inc., Philadelphia, Pennsylvania, United States of America.
Adrian CrutchfieldHueDx, Inc., Philadelphia, Pennsylvania, United States of America.
Leah KimHueDx, Inc., Philadelphia, Pennsylvania, United States of America.
Aaron LauerHueDx, Inc., Philadelphia, Pennsylvania, United States of America.
Ayesha AzimuddinHueDx, Inc., Philadelphia, Pennsylvania, United States of America.
Brianna Wronko-StevensHueDx, Inc., Philadelphia, Pennsylvania, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Color correction is an important methodology where a digital image's colors undergo a transformation to more accurately represent their appearance using a predefined set of illumination conditions. Colorimetric measurements in diagnostics are sensitive to very small changes in colors and therefore require consistent, reproducible illumination conditions to produce accurate results, making color correction a necessity. This paper presents an image color correction pipeline developed by HueDx, Inc., using transfer algorithms that improve upon existing methodologies and demonstrates real-world applications of this pipeline in colorimetric clinical chemistry using a smartphone enabled, paper-based total protein diagnostic assay. Our pipeline is able to compensate for a variety of illumination conditions to provide consistent imaging for quantitative colorimetric measurements using white-balancing, multivariate gaussian distributions and histogram regression via dynamic, non-linear interpolating lookup tables. We empirically demonstrate that each point in the color correction pipeline provides a theoretical basis for achieving consistent and precise color correction. To show this, we measure color difference with deltaE (ΔE00), alongside quantifying performance of the HueDx color correction system, including the phone hardware, color sticker manufacturing quality and software correction capabilities. The results show that the HueDx color correction system is capable of restoring images to near-imperceptible levels of difference independent of their original illumination conditions including brightness and color temperature. Comparisons drawn from the paper-based total protein assay calibrated and quantified with and without using the HueDx color correction pipeline show that the coefficient of variation in precision testing is almost twice as high without color-correcting. Limits of blank, detection and quantitation were also higher without color-correction. Overall, we were able to demonstrate the HueDx platform improves reading and outcome of the total protein diagnostic assay and is useful for the development of smartphone-based quantitative colorimetric diagnostic assays for point-of-care testing.

Indexed as

AlgorithmsColorColorimetryPaperSmartphoneHumansSoftware

Identifiers

PMID39365798
PMCPMC11451979

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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.