Evidence map›Paper›PMID 41109909›Full record

ArticleNpj mental health research2025

Emoface: AI-assisted diagnostic model for differentiating major depressive disorder and bipolar disorder via facial biomarkers.

Jiahui Yu, Jingkai Chen, Yutong Zhang, Hailong Lyu, Tianyu Ma, Huimin Huang, Zhong Wang, Xin Xu, Shaohua Hu, Yingke Xu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Npj mental health research, 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

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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

10 authors.

Jiahui Yu *Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang Key Laboratory of Intelligent Sensing and Advanced Medical Instrument, Zhejiang University, Hangzhou, China.
Jingkai Chen *Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Yutong ZhangInnovation Center for Smart Medical Technologies and Devices, Binjiang Institute of Zhejiang University, Hangzhou, China.
Hailong LyuDepartment of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Tianyu MaInnovation Center for Smart Medical Technologies and Devices, Binjiang Institute of Zhejiang University, Hangzhou, China.
Huimin HuangDepartment of Psychiatry, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Zhong WangDepartment of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Xin XuSchool of Public Health, the Second Affiliated Hospital of School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China. xuxinsummer@zju.edu.cn.
Shaohua HuDepartment of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. dorhushaohua@zju.edu.cn.
Yingke XuDepartment of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang Key Laboratory of Intelligent Sensing and Advanced Medical Instrument, Zhejiang University, Hangzhou, China. yingkexu@zju.edu.cn.

Funding

Cao Guangbiao High-tech Development Fund 2022RC009Key Projects of Hangzhou Science and Technology Bureau 20231203A13Key R&D Program of Zhejiang 2025C01104, 2025C02108 and 2021C03107Leading Talent of Scientific and Technological Innovation of Zhejiang Province 2021R52016National Key Research and Development Program of China 2023YFC2506200National Natural Science Foundation of China 62406280 and 72274170Research Project of Jinan Microecological Biomedicine Shandong Laboratory JNL-2023001BZhejiang Provincial Natural Science Foundation LZ23H180002 and LQ23F030001
6 · The paper itself

Abstract

Affective disorders, including Major Depressive Disorder (MDD) and Bipolar Disorder (BD), exhibit significant mood abnormalities, making rapid diagnosis essential for social stability and healthcare efficiency. Traditional diagnostic solutions, including medical history collection and psychological assessments, often struggle to differentiate their similar clinical presentations, leading to time-consuming, laborious, and a high rate of misdiagnosis. Here, we propose Emoface, an AI-assisted diagnostic model that reads the emotional activities of faces in affective disorders. By analyzing videos from 353 participants exposed to various emotional stimuli, Emoface identified unique facial digital biomarkers distinguishing BD from MDD. Based on this, Emoface contributed to develop the first digital facial mapping for clinical and teaching use. In clinical practice with 347 patients, Emoface achieved precise diagnosis based on various facial visual signals, with accuracy rates of 95.29% for BD and 87.05% for MDD, offering a reliable face-based AI solution in a new era of affective disorders.

Identifiers

PMID41109909
PMCPMC12535589

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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.