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World J Psychiatry. Sep 19, 2026; 16(9): 119308
Published online Sep 19, 2026. doi: 10.5498/wjp.119308
Gamified video-based affective computing framework for adolescent depression screening
Qi-Zhou Li, Li-Yuan-Ke Wang, Ming-Hao Wang, Jing-Yun Li, Xin-Xi Chen, Wei Fang, Zi-Xu Wang, Pei-Dong Chen, Qing-Shu Bai, Pei Sun, Xi-Wang Fan, Rui Zhong, Hua Shi
Qi-Zhou Li, Jing-Yun Li, Xin-Xi Chen, Zi-Xu Wang, Xi-Wang Fan, Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai 200124, China
Li-Yuan-Ke Wang, Department of Psychosomatic Medicine, Suining Central Hospital, Suining 629000, Sichuan Province, China
Ming-Hao Wang, Wei Fang, Beijing Situ Wellbeing Technology Co., Ltd, Beijing 100080, China
Pei-Dong Chen, Department of Rehabilitation Therapy, Qilu Institute of Technology, Jinan 250200, Shandong Province, China
Qing-Shu Bai, Hua Shi, Department of Mental Health and Health Promotion, Chinese PLA Center for Disease Control and Prevention, Beijing 100071, China
Pei Sun, Faculty of Health and Wellness, City University of Macau, Macau 999078, China
Pei Sun, Tsinghua Laboratory of Brain and Intelligence and Department of Psychological and Cognitive Science, Tsinghua University, Beijing 100084, China
Rui Zhong, Mental Health Assessment Center, Shandong Mental Health Center, Jinan 250014, Shandong Province, China
Co-first authors: Qi-Zhou Li and Li-Yuan-Ke Wang.
Co-corresponding authors: Rui Zhong and Hua Shi.
Author contributions: Li QZ led the methodological design, formal analysis, and writing of the original draft, reviewing and editing of the manuscript; Wang LYK and Wang MH contributed to conceptualization, data collection, and writing of the original draft, reviewing and editing; Li JY and Fang W supported the formal analysis and validation; Chen XX, Wang ZX, Chen PD, and Bai QS contributed to data collection; Sun P, Zhong R, and Fan XW supervised the project and provided conceptual guidance; Shi H provided project administration and supervision, and secured funding. Li QZ and Wang LYK contributed equally to this work as co-first authors. Zhong R and Shi H are designated as co-corresponding authors because they made substantial, distinct, and complementary contributions to the conception, supervision, coordination, and academic integrity of this multidisciplinary study. Zhong R provided important conceptual guidance and project supervision, particularly in relation to mental health assessment, clinical interpretation, and the relevance of the proposed screening framework for adolescent depression. His expertise helped ensure that the study design and interpretation of behavioral indicators were clinically meaningful. Shi H was responsible for overall project administration and supervision and played a key role in securing funding support, coordinating institutional collaboration, and ensuring the smooth implementation of the study. Given that the manuscript integrates clinical psychiatry, adolescent mental health screening, affective computing, and machine learning, effective correspondence requires expertise in both clinical-scientific interpretation and project-level coordination. Therefore, the designation of Zhong R and Shi H as co-corresponding authors accurately reflects their shared senior leadership, complementary responsibilities, and accountability for the work.
AI contribution statement: We declare that AI tools were used solely for language polishing at the final stage of manuscript preparation. Specifically, ChatGPT was used for minor linguistic refinement, after which the manuscript underwent additional professional language editing. No AI tools were used in the conception or design of the study, data collection or analysis, interpretation of results, or generation of any substantive manuscript content. All figures were produced using Python and MATLAB.
Institutional review board statement: This study was approved by Ethics Committee of Shanghai Pudong New District Mental Health Center, No. JW-IIT-2022-011GZ1.
Informed consent statement: Informed consent was obtained from all individual participants included in the study. Participants were informed about the study’s purpose, procedures, risks, and benefits, and their participation was voluntary. They were assured of confidentiality and the right to withdraw at any time without consequences.
Conflict-of-interest statement: All authors have completed the Unified Competing Interest form (available on request from the corresponding author) and declare: No support from any organization for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years, no other relationships or activities that could appear to have influenced the submitted work.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: The data used and analyzed in this study are not publicly available because they are the property of the Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University but may be available from the corresponding author on reasonable request.
Corresponding author: Hua Shi, Department of Mental Health and Health Promotion, Chinese PLA Center for Disease Control and Prevention, No. 20 Dongjie Street, Fengtai District, Beijing 100071, China.
placdc@139.com
Received: January 26, 2026
Revised: March 5, 2026
Accepted: June 29, 2026
Published online: September 19, 2026
Processing time: 212 Days and 20.5 Hours
BACKGROUND
Early detection of adolescent depression remains challenging, as conventional assessments rely on self-reporting and clinician-administered interviews, which constrain scalability and ecological validity. Recent advances in affective computing and computer vision enable the extraction of objective, low-cost behavioral markers from naturalistic visual data.
AIM
To develop and evaluate a video-based machine learning framework for adolescent depression screening using visual features captured during a gamified affective task designed to elicit spontaneous emotional and attentional responses.
METHODS
In this cross-sectional screening-model development and validation study, 383 adolescents aged 10-19 years (188 with clinically diagnosed major depressive disorder, 195 healthy controls) were recruited from community and clinical settings in Shanghai, China (March 2024 to August 2025). Participants performed a gamified Whac-A-Mole task while being recorded by a 720 p webcam (16 fps). Forty visual behavioral features, encompassing facial action units, gaze metrics, head pose, and valence-arousal dynamics, were extracted and used to train and evaluate a random forest classifier using stratified 10-fold cross-validation and an internal train-test split.
RESULTS
The model achieved strong performance, with an area under the curve (AUC) of 0.813 on the internal test set and a cross-validation AUC of 0.886 in the full sample. Accuracy, precision, and F1-score in the cross-validation analysis were 0.812, 0.841, and 0.799, respectively. Key discriminative indicators included AU6 (cheek raiser), AU12 (lip corner puller), gaze yaw variability, and valence fluctuation. Probability calibration demonstrated well-balanced confidence estimates and stable performance across validation folds.
CONCLUSION
This study establishes the feasibility of using affective computing-derived visual features from a gamified setting to identify adolescent depression. By leveraging spontaneous, nonverbal behavior in an engaging and stigma-free context, the framework provides a validated, interpretable, and scalable complementary screening tool to support early risk identification and referral, marking an important step toward ethical and accessible digital screening for adolescent mental health.
Core Tip: This study introduces a gamified, video-based affective computing approach for adolescent depression screening that captures spontaneous behavioral cues during an interactive game task. By extracting facial action units, gaze behavior, head pose, and valence-arousal features from short video recordings recorded within the game, we trained a machine learning classifier that reliably distinguished depressed adolescents from healthy controls with strong external generalization. The findings demonstrate that visual behavioral signals alone can support accurate, low-cost, and non-invasive depression screening, highlighting the potential of gamified paradigms for scalable mental health screening in addition to traditional clinical interviews.
