Evidence map›Paper›PMID 42022163›Full record

ArticleFrontiers in bioengineering and biotechnology2026

AI-assisted quantitative analysis for evaluating melanin distribution in 3D pigmented epidermis-on-a-chip models.

Yu Yao, Xuan Du, Yanhui Li, Yuchen Ma, Yuchen Li, Zilin Zhang, Boyang Song, Xiaoran Li, Jing Zhang, Jun Ouyang and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 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. Article
  2. Regenerative biomaterials · 2026
    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

15 authors.

Yu Yao *Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xuan Du *Skin-on-a-Chip Translational Medicine Center, Joint Laboratory of Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Southeast University, Beijing, China.
Yanhui Li *State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China.
Yuchen Ma *Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yuchen LiSkin-on-a-Chip Translational Medicine Center, Joint Laboratory of Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Southeast University, Beijing, China.
Zilin ZhangState Key Laboratory of Digital Medical Engineering, Institute of Microphysiological Systems, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
Boyang SongState Key Laboratory of Digital Medical Engineering, Institute of Microphysiological Systems, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
Xiaoran LiSkin-on-a-Chip Translational Medicine Center, Joint Laboratory of Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Southeast University, Beijing, China.
Jing ZhangSkin-on-a-Chip Translational Medicine Center, Joint Laboratory of Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Southeast University, Beijing, China.
Jun OuyangSkin-on-a-Chip Translational Medicine Center, Joint Laboratory of Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Southeast University, Beijing, China.
Nuo SiPlastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Ningbei YinPlastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Qianqian HanNational Institutes for Food and Drug Control, Beijing, China.
Zhongze GuSkin-on-a-Chip Translational Medicine Center, Joint Laboratory of Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Southeast University, Beijing, China.
Zaozao ChenSkin-on-a-Chip Translational Medicine Center, Joint Laboratory of Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Southeast University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Abnormal pigmentation plays an important role in various skin diseases and in studies of whitening efficacy.Three-dimensional pigmented epidermis-on-a-chip models provide a crucial in vitro platform for exploring melanin production and regulation in skin. However, dynamic and non-invasive quantitative assessment of melanin distribution remains difficult with traditional histological methods. Methods: In this study, an AI-assisted objective evaluation framework was established for three-dimensional pigmented epidermis-on-a-chip models based on brightfield images. Melanin regions were segmented using the MEM-ViT algorithm, and their morphological features were extracted to build a multi-indicator comprehensive analysis system for determining the "good/poor" status of the model. Results: The results showed 98% consistency between algorithmic predictions and manual annotations, demonstrating the reliability and generalization capability of the proposed method. The framework enabled accurate segmentation of melanin regions and standardized evaluation of model quality without staining. Discussion: This method provides a rapid, non-invasive, and standardized approach for evaluating 3D pigmented epidermis-on-a-chip models. It offers a useful technical pathway for drug efficacy research, whitening mechanism analysis, and objective assessment of skin pigmentation-related disorders.

Indexed as

AI-based quantitative evaluationmelanin distributionpigmented epidermis-on-a-chipsemantic segmentationvision transformer (ViT)

Identifiers

PMID42022163
PMCPMC13096039

What OpenQuestion holds

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LicenceCC BY
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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.