Evidence map›Paper›PMID 42322531›Full record

ArticleDermatology and therapy2026

Predicting Age-Related Facial Hyperpigmentation via Dermatologist Knowledge Elicitation and Generative Modeling.

Edouard Raynaud, Laudine Bertrand, Frederic Flament, Jennifer Bourland, Emmanuelle Tancrède-Bohin, Tao Li, Hussein Jouni

Abstract read
In one paragraph

Article in Dermatology and therapy, 2026. 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. 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

7 authors.

Edouard RaynaudBioclinical Research Center (CRB) of L'Oréal Advanced Research, Saint-Louis Hospital, Paris, France.
Laudine BertrandL'Oréal Research and Innovation, Clichy, France.
Frederic FlamentL'Oréal Research and Innovation, Clichy, France.
Jennifer BourlandL'Oréal Research and Innovation, Aulnay-sous-Bois, France.
Emmanuelle Tancrède-BohinDermatology Department, St Louis Hospital, APHP, Paris, France.
Tao LiL'Oréal Research and Innovation, Clichy, France.
Hussein JouniL'Oréal Research and Innovation, Clichy, France. hussein.jouni@loreal.com.ORCID http://orcid.org/0000-0003-4641-3161

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionFacial hyperpigmentation is a primary marker of skin aging in Asian populations. While many artificial intelligence (AI)-based aging simulators exist on social media, they often lack scientific transparency and dermatological validation. This study introduces and validates a novel facial aging simulator specifically engineered to provide personalized, evidence-based predictions of pigmentary spot progression according to varying levels of ultraviolet (UV) exposure and photoprotection.

methodsThe simulator utilizes a dual-tool framework: Elicitation Based Aging Simulator (EBAS) and AgingMapGAN (AMGAN). EBAS leverages the collective expertise of 28 dermatologists via a structured Delphi process and causal Bayesian belief networks (BBNs) to model skin aging trajectories. AMGAN, a conditional generative adversarial network, provides high-resolution visual representations of these trajectories. The model was trained on a standardized dataset of 600 individuals and focused on the density of pigmentary spots (DPS) on the cheek. Model performance was benchmarked against expert clinical grading using correlation metrics.

resultsThe simulator demonstrated high accuracy in replicating expert logic (Pearson's correlation 0.96). In a case study of a 38-year-old female of Chinese descent, the model predicted the 15-year probability of a clinically significant progression (a two-grade increase in DPS). In the absence of photoprotection, the probability of reaching this elevated grade by age 53 was 71.35%, whereas regular daily application of SPF 50+ sunscreen reduced this probability to 39.24%.

conclusionsThis framework represents the application of a scientifically validated image-generation tool for predicting age-related hyperpigmentation based on individual exposome factors. By integrating dermatological knowledge, this simulator provides a robust educational and research resource for personalized skincare strategies. The findings confirm that consistent photoprotection significantly mitigates the 15-year trajectory of premature facial aging, offering a scientifically validated foundation for public health communication on prevention.

Indexed as

AIArtificial intelligenceElicitationFacial hyperpigmentationPhotoprotectionPredictive modelSkinSkin agingSolar lentigo

Identifiers

PMID42322531
PMCPMC13493717

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.