Evidence map›Paper›PMID 39910838›Full record

ArticleSmall (Weinheim an der Bergstrasse, Germany)2025

Deep Learning-Enhanced Chemiluminescence Vertical Flow Assay for High-Sensitivity Cardiac Troponin I Testing.

Gyeo-Re Han, Artem Goncharov, Merve Eryilmaz, Shun Ye, Hyou-Arm Joung, Rajesh Ghosh, Emily Ngo, Aoi Tomoeda, Yena Lee, Kevin Ngo and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Small (Weinheim an der Bergstrasse, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
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  5. Review
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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

14 authors.

Gyeo-Re HanElectrical & Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.ORCID 0000-0003-3584-4433
Artem GoncharovElectrical & Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Merve EryilmazElectrical & Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Shun YeBioengineering Department, University of California, Los Angeles, CA, 90095, USA.
Hyou-Arm JoungElectrical & Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Rajesh GhoshBioengineering Department, University of California, Los Angeles, CA, 90095, USA.
Emily NgoDepartment of Psychology, University of California, Los Angeles, CA, 90095, USA.
Aoi TomoedaChemical and Biomolecular Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Yena LeeMechanical and Aerospace Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Kevin NgoBioengineering Department, University of California, Los Angeles, CA, 90095, USA.
Elizabeth MeltonBiomedical Engineering Department, University of California, Davis, CA, 95616, USA.
Omai B GarnerDepartment of Pathology and Laboratory Medicine, University of California, Los Angeles, CA, 90095, USA.ORCID 0000-0002-7366-2692
Dino Di CarloBioengineering Department, University of California, Los Angeles, CA, 90095, USA.ORCID 0000-0003-3942-4284
Aydogan OzcanElectrical & Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.ORCID 0000-0002-0717-683X

Funding

Ministry of Education NRF-2021R1A6A3A14039885National Science Foundation NSF #1648451
6 · The paper itself

Abstract

Democratizing biomarker testing at the point-of-care requires innovations that match laboratory-grade sensitivity and precision in an accessible format. Here, high-sensitivity detection of cardiac troponin I (cTnI) is demonstrated through innovations in chemiluminescence-based sensing, imaging, and deep learning-driven analysis. This chemiluminescence vertical flow assay (CL-VFA) enables rapid, low-cost, and precise quantification of cTnI, a key cardiac protein for assessing heart muscle damage and myocardial infarction. The CL-VFA integrates a user-friendly chemiluminescent paper-based sensor, a polymerized enzyme-based conjugate, a portable high-performance CL reader, and a neural network-based cTnI concentration inference algorithm. The CL-VFA measures cTnI over a broad dynamic range covering six orders of magnitude and operates with 50 µL of serum per test, delivering results in 25 min. This system achieves a detection limit of 0.16 pg mL

Indexed as

Deep LearningLuminescent MeasurementsMyocardiumTroponin IBiomarkersHumansLimit of DetectionMyocardial InfarctionBiomarkersTroponin Ichemiluminescencedeep learning‐enabled sensinghigh‐sensitivity troponinpoint‐of‐care testingvertical flow assays

Identifiers

PMID39910838
PMCPMC11922022

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.