Evidence map›Paper›PMID 41951574›Full record

ArticleLight, science & applications2026

Deep learning-enhanced dual-mode multiplexed optical sensor for point-of-care diagnostics of cardiovascular diseases.

Gyeo-Re Han, Merve Eryilmaz, Artem Goncharov, Yuzhu Li, Shun Ye, Aoi Tomoeda, Emily Ngo, Margherita Scussat, Xiao Wang, Zixiang Ji and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Light, science & applications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

15 authors.

Gyeo-Re HanElectrical & Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.ORCID http://orcid.org/0000-0003-3584-4433
Merve EryilmazElectrical & Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Artem GoncharovElectrical & Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Yuzhu LiElectrical & Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Shun YeBioengineering Department, University of California, Los Angeles, CA, 90095, USA.ORCID http://orcid.org/0000-0003-3457-9359
Aoi TomoedaChemical and Biomolecular Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Emily NgoDepartment of Psychology, University of California, Los Angeles, CA, 90095, USA.
Margherita ScussatBioengineering Department, University of California, Los Angeles, CA, 90095, USA.
Xiao WangElectrical & Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Zixiang JiDepartment of Computer Science, University of California, Los Angeles, CA, 90095, USA.
Max ZhangDepartment of Chemistry and Biochemistry, University of California, Los Angeles, CA, 90095, USA.
Jeffrey J HsuBioengineering Department, University of California, Los Angeles, CA, 90095, USA.
Omai B GarnerDepartment of Pathology and Laboratory Medicine, University of California, Los Angeles, CA, 90095, USA.
Dino Di CarloBioengineering Department, University of California, Los Angeles, CA, 90095, USA. dicarlo@ucla.edu.
Aydogan OzcanElectrical & Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA. ozcan@ucla.edu.ORCID http://orcid.org/0000-0002-0717-683X

Funding

National Research Foundation of Korea (NRF) NRF-2021R1A6A3A14039885National Science Foundation (NSF) NSF #1648451
6 · The paper itself

Abstract

Rapid and accessible cardiac biomarker testing is essential for the timely diagnosis and risk assessment of myocardial infarction (MI) and heart failure (HF), two interrelated conditions that frequently coexist and drive recurrent hospitalizations with high mortality. However, current laboratory and point-of-care testing systems are limited by long turnaround times, narrow dynamic ranges for the tested biomarkers, and single-analyte formats that fail to capture the complexity of cardiovascular disease. Here, we present a deep learning-enhanced dual-mode multiplexed vertical flow assay (xVFA) with a portable optical reader and a neural network-based quantification pipeline. This optical sensor integrates colorimetric and chemiluminescent detection within a single paper-based cartridge to complementarily cover a large dynamic range (spanning ~6 orders of magnitude) for both low- and high-abundance biomarkers, while maintaining quantitative accuracy. Using 50 µL of serum, the optical sensor simultaneously quantifies cardiac troponin I (cTnI), creatine kinase-MB (CK-MB), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) within 23 min. The xVFA achieves sub-pg/mL sensitivity for cTnI and sub-ng/mL sensitivity for CK-MB and NT-proBNP, spanning the clinically relevant ranges for these biomarkers. Neural network models trained and blindly tested on 92 patient serum samples yielded a robust quantification performance (Pearson's r > 0.96 vs. reference assays). By combining high sensitivity, multiplexing, and automation in a compact and cost-effective optical sensor format, the dual-mode xVFA enables rapid and quantitative cardiovascular diagnostics at the point of care.

Identifiers

PMID41951574
PMCPMC13061988

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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 OpenQuestion graph.