Integrating AI-driven technologies and facial-semantic features for depression detection: A cross-sectional study.

Saved in:
Bibliographic Details
Title: Integrating AI-driven technologies and facial-semantic features for depression detection: A cross-sectional study.
Authors: Lin MF; Department of Nursing, College of Medicine, National Cheng Kung University, Tainan, Taiwan., Pan YC; Department of Nursing, College of Medicine, National Cheng Kung University, Tainan, Taiwan., Liu FP; Department of Nursing, College of Medicine, National Cheng Kung University, Tainan, Taiwan., Shen HJ; Graduate Institute of Electrical Engineering, National Central University, Taiwan., Lu WH; Institute of Microelectronics, National Cheng Kung University, Tainan, Taiwan., Mudiyanselage SPK; Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Operation Theatre Department, The National Hospital of Sri Lanka, Colombo, Sri Lanka. Electronic address: pkadawatha@gmail.com., Tseng HH; Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Department of Psychiatry, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Department of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan. Electronic address: hhtseng@mail.ncku.edu.tw.
Source: Journal of affective disorders [J Affect Disord] 2026 Apr 01; Vol. 398, pp. 120889. Date of Electronic Publication: 2025 Dec 18.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier/North-Holland Biomedical Press Country of Publication: Netherlands NLM ID: 7906073 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-2517 (Electronic) Linking ISSN: 01650327 NLM ISO Abbreviation: J Affect Disord Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1573-2517
DOI:10.1016/j.jad.2025.120889