Evidence map›Paper›PMID 39322430›Full record

ArticleInternational journal of stem cells2025

Application of Deep Neural Networks in the Manufacturing Process of Mesenchymal Stem Cells Therapeutics.

Dat Ngo, Jeongmin Lee, Sun Jae Kwon, Jin Hun Park, Baek Hwan Cho, Jong Wook Chang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of stem cells, 2025. 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

6 authors.

Dat NgoDepartment of Computer Engineering, Korea National University of Transportation, Chungju, Korea.ORCID https://orcid.org/0000-0002-3896-648X
Jeongmin LeeCDMO Technology Institute, ENCell Co., Ltd., Seoul, Korea.ORCID https://orcid.org/0000-0003-3983-3950
Sun Jae KwonCDMO Technology Institute, ENCell Co., Ltd., Seoul, Korea.ORCID https://orcid.org/0009-0008-7966-9706
Jin Hun ParkDepartment of Media and Communication, College of Future Convergence Division of Healthcare Sciences, CHA University, Seongnam, Korea.ORCID https://orcid.org/0009-0009-7633-4545
Baek Hwan ChoDepartment of Biomedical Informatics, School of Medicine, CHA University, Seongnam, Korea.ORCID https://orcid.org/0000-0001-7722-5660
Jong Wook ChangCDMO Technology Institute, ENCell Co., Ltd., Seoul, Korea.ORCID https://orcid.org/0000-0001-9335-5510

Funding

CLC NIH HHS C0000008
6 · The paper itself

Abstract

Current image-based analysis methods for monitoring cell confluency and status depend on individual interpretations, which can lead to wide variations in the quality of cell therapeutics. To overcome these limitations, images of mesenchymal stem cells cultured adherently in various types of culture vessels were captured and analyzed using a deep neural network. Among the various deep learning methods, a classification and detection algorithm was selected to verify cell confluency and status. We confirmed that the image classification algorithm demonstrates significant accuracy for both single- and multistack images. Abnormal cells could be detected exclusively in single-stack images, as multistack culture was performed only when abnormal cells were absent in the single-stack culture. This study is the first to analyze cell images based on a deep learning method that directly impacts yield and quality, which are important product parameters in stem cell therapeutics.

Indexed as

Cell proliferationDeep learningDigital image processingMesenchymal stem cellsNeural network models

Identifiers

PMID39322430
PMCPMC12122248

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.