Retrospective analysis of nursing interventions using artificial intelligence multimodal emotion recognition for perioperative depression in head and neck tumors

doi:10.5498/wjp.122781

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Publication Name

World Journal of Psychiatry

ISSN

2220-3206

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Retrospective Study

Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Psychiatry. Nov 19, 2026; 16(11): 122781
Published online Nov 19, 2026. doi: 10.5498/wjp.122781

Retrospective analysis of nursing interventions using artificial intelligence multimodal emotion recognition for perioperative depression in head and neck tumors

Yan-Fang Zhu, Xiao-Lin Xuan, Yi-Qi Li

Yan-Fang Zhu, Xiao-Lin Xuan, Yi-Qi Li, Department of Otolaryngology and Head and Neck Surgery, Affiliated Hospital of Jiangnan University, Wuxi 214100, Jiangsu Province, China

Co-first authors: Yan-Fang Zhu and Xiao-Lin Xuan.

Author contributions: Zhu YF and Xuan XL jointly participated in the design of the research, collection and collation of patient clinical data, statistical analysis of research results, and drafting and revision of the manuscript; Li YQ provided overall research guidance, supervised the entire research process, critically reviewed and revised the manuscript. All authors have read and approved the final version of the manuscript, and agree to the submission of the manuscript to your journal. Zhu YF and Xuan XL (co-first authors) contributed equally to this retrospective study.

AI contribution statement: The authors declare that no AI tools were used in the development or writing of this manuscript and take full responsibility for its integrity, accuracy, and originality.

Institutional review board statement: This study was reviewed and approved by the appropriate Institutional review board of Affiliated Hospital of Jiangnan University, No. 202604.101.

Informed consent statement: All the individuals who participated in this study provided their written informed consent prior to study enrolment.

Conflict-of-interest statement: The authors report no relevant conflicts of interest.

Data sharing statement: No additional data are available.

Corresponding author: Yi-Qi Li, Associate Chief Nurse, Department of Otolaryngology and Head and Neck Surgery, Affiliated Hospital of Jiangnan University, No. 1000 Hefeng Road, Xuelang Street, Wuxi 214100, Jiangsu Province, China. 13159400822@qq.com

Received: June 5, 2026
Revised: July 1, 2026
Accepted: July 17, 2026
Published online: November 19, 2026
Processing time: 145 Days and 15.8 Hours

Abstract

BACKGROUND

Perioperative depression is prevalent among head and neck tumor patients, while traditional emotional assessment methods lag and fail to meet clinical nursing needs.

AIM

To investigate artificial intelligence (AI) multimodal emotion recognition's effects on enhancing individualized nursing care for depression patients during perioperative head and neck tumor surgeries, focusing on emotional fluctuations, nursing response efficiency, and postoperative recovery outcomes.

METHODS

Sixty patients with perioperative depression associated with head and neck tumors admitted to our hospital between January 2023 and December 2025 were selected and divided into two groups: The conventional care group (27 patients.) and the AI individualized care group (33 patients). We compared the baseline data, perioperative emotional indicators, postoperative outcomes, emotional recognition, and nursing responses between the two groups while simultaneously evaluating the recognition performance of the multimodal model.

RESULTS

There were no significant baseline differences between the groups. During observation, the Hamilton Depression Rating Scale (HAMD) score, emotional score, Emotional Mood Index (EMI), and proportion of positive emotions showed dynamic changes with significant group-time interaction. The AI-individualized care group demonstrated lower HAMD and EMI scores, higher comprehensive emotional scores, and a greater proportion of positive emotions, postoperatively. Additionally, hospital stay length was reduced, complications decreased, and Physical Component Summary, Mental Component Summary, and Emotional Recovery Score improved significantly. The AI group outperformed the conventional nursing group in recognizing negative emotions, intervention matching, and emotional improvement within 24 hours. The time for initial recognition and nursing responses was much shorter. The area under the curve, sensitivity, and specificity of the multimodal fusion model were 0.894, 86.364%, and 83.871%, respectively, indicating its superiority over the single-modal model.

CONCLUSION

AI-informed individualized nursing can facilitate earlier identification of abnormal emotions in patients with head and neck tumors perioperatively, enhance nursing efficiency, and positively influence perioperative emotions and recovery.

Keywords: Head and neck tumors; Perioperative depression; Artificial intelligence; Multimodal emotion recognition; Personalized care; Quality of life

Core Tip: Artificial intelligence multimodal emotion recognition has good diagnostic efficacy. Applying it to targeted nursing for head and neck tumor patients can promptly capture perioperative depressive mood, streamline nursing response, and improve emotional status and postoperative rehabilitation.