Evidence map›Paper›PMID 39715548›Full record

ArticleJMIR biomedical engineering2024

Pump-Free Microfluidics for Cell Concentration Analysis on Smartphones in Clinical Settings (SmartFlow): Design, Development, and Evaluation.

Sixuan Wu, Kefan Song, Jason Cobb, Alexander T Adams

Abstract read
In one paragraph

Article in JMIR biomedical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

4 authors.

Sixuan WuSchool of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States.ORCID https://orcid.org/0000-0002-8831-5319
Kefan SongWallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States.ORCID https://orcid.org/0000-0001-9788-9878
Jason CobbRenal Medicine, School of Medicine, Emory University, Atlanta, GA, United States.ORCID https://orcid.org/0000-0002-9257-4284
Alexander T AdamsSchool of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States.ORCID https://orcid.org/0000-0001-5811-3153

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCell concentration in body fluid is an important factor for clinical diagnosis. The traditional method involves clinicians manually counting cells under microscopes, which is labor-intensive. Automated cell concentration estimation can be achieved using flow cytometers; however, their high cost limits accessibility. Microfluidic systems, although cheaper than flow cytometers, still require high-speed cameras and syringe pumps to drive the flow and ensure video quality. In this paper, we present SmartFlow, a low-cost solution for cell concentration estimation using smartphone-based computer vision on 3D-printed, pump-free microfluidic platforms.

objectiveThe objective was to design and fabricate microfluidic chips, coupled with clinical utilities, for cell counting and concentration analysis. We answered the following research questions (RQs): RQ1, Can gravity drive the flow within the microfluidic chips, eliminating the need for external pumps? RQ2, How does the microfluidic chip design impact video quality for cell analysis? RQ3, Can smartphone-captured videos be used to estimate cell count and concentration in microfluidic chips?

methodsTo answer the 3 RQs, 2 experiments were conducted. In the cell flow velocity experiment, diluted sheep blood flowed through the microfluidic chips with and without a bottleneck design to answer RQ1 and RQ2, respectively. In the cell concentration analysis experiment, sheep blood diluted into 13 concentrations flowed through the microfluidic chips while videos were recorded by smartphones for the concentration measurement.

resultsIn the cell flow velocity experiment, we designed and fabricated 2 versions of microfluidic chips. The ANOVA test (Straight: F

conclusionsIn conclusion, we demonstrated the importance of the flow velocity in a microfluidic system, and we proposed SmartFlow, a low-cost system for computer vision-based cellular analysis. The proposed system could count the cells and estimate cell concentrations in the samples.

Indexed as

cellcells counting, body fluid analysis, blood test, urinalysis, computer vision, machine learningcellularchipconcentrationfluidmHealthmicrofluidicsmicroscopemobile healthsmartphoneubiquitous health

Identifiers

PMID39715548
PMCPMC11704648

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.