Evidence map›Paper›PMID 42567162›Full record

ArticleCell reports. Medicine2026

Multi-modal AI-enabled steatotic liver disease diagnostics using facial images and metabolomics.

Yuanxu Gao, Kai Wang, Yu Ke, Zixin Zou, Guohui Wei, Fangfei Wang, Winston Wang, Gen Li, Manson Fok, Stephan Beck and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports. Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Yuanxu GaoDepartment of Big Data and Biomedical Artificial Intelligence, College of Future Technology, Peking University, Beijing 100871, China; AI Cross Disciplinary Research Institute and Faculty of Medicine, Macau University of Science and Technology, Taipa 999078, Macau, China. Electronic address: yuanxu.carl@gmail.com.
Kai WangDepartment of Big Data and Biomedical Artificial Intelligence, College of Future Technology, Peking University, Beijing 100871, China; AI Cross Disciplinary Research Institute and Faculty of Medicine, Macau University of Science and Technology, Taipa 999078, Macau, China.
Yu KeThe Tenth Affiliated Hospital (Dongguan People's Hospital), Shenzhen School of Clinical Medical, Southern Medical University, Guangzhou, Guangdong, China; Shenzhen School of Clinical Medical, Southern Medical University, Guangzhou, Guangdong, China.
Zixin ZouAI Cross Disciplinary Research Institute and Faculty of Medicine, Macau University of Science and Technology, Taipa 999078, Macau, China; Guangzhou National Laboratory, Guangzhou, China.
Guohui WeiAI Cross Disciplinary Research Institute and Faculty of Medicine, Macau University of Science and Technology, Taipa 999078, Macau, China; Guangzhou National Laboratory, Guangzhou, China.
Fangfei WangGuangzhou National Laboratory, Guangzhou, China.
Winston WangMayo Clinic Department of Internal Medicine, Rochester, AZ, USA.
Gen LiAI Cross Disciplinary Research Institute and Faculty of Medicine, Macau University of Science and Technology, Taipa 999078, Macau, China; Guangzhou National Laboratory, Guangzhou, China.
Manson FokAI Cross Disciplinary Research Institute and Faculty of Medicine, Macau University of Science and Technology, Taipa 999078, Macau, China.
Stephan BeckUCL Cancer Institute, University College London, London WC1E 6DD, UK.
Io Nam WongAI Cross Disciplinary Research Institute and Faculty of Medicine, Macau University of Science and Technology, Taipa 999078, Macau, China.
Kang ZhangDepartment of Big Data and Biomedical Artificial Intelligence, College of Future Technology, Peking University, Beijing 100871, China; AI Cross Disciplinary Research Institute and Faculty of Medicine, Macau University of Science and Technology, Taipa 999078, Macau, China; Shenzhen School of Clinical Medical, Southern Medical University, Guangzhou, Guangdong, China; Guangzhou National Laboratory, Guangzhou, China. Electronic address: kang.zhang@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Steatotic liver disease (SLD) affects one-third of the global population, yet current non-invasive diagnostic methods are too costly or operator-dependent for population-scale screening. Here, we present 3D-FAICE, a deep learning system that uses three-dimensional facial imaging for non-invasive SLD detection. Trained and tested on 11,456 participants, the facial model achieves robust performance across internal, external, and self-controlled longitudinal cohorts and remains effective in a smartphone-based point-of-care setting. Metabolomic analysis reveals that facial risk scores correlate with glycolipid and amino acid pathways, supporting biological plausibility. Multimodal fusion of facial and metabolomic data further improves accuracy, and a cross-modal distillation strategy significantly elevates the performance of the facial-only model. These findings establish facial image-based AI as a non-invasive, scalable, and privacy-aware tool for SLD screening, with potential applications in self-monitoring and population health management.

Indexed as

Artificial IntelligenceFaceFatty LiverMetabolomicsNon-alcoholic Fatty Liver DiseaseAdultDeep LearningFemaleHumansImaging, Three-DimensionalMaleMiddle Ageddeep learningdisease screeningfacial imagefederated learningmetabolomicsmultimodal artificial intelligencesteatotic liver disease

Identifiers

PMID42567162
PMCPMC13522785

What Socratic holds

Textmetadata
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 Socratic graph.