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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World Journal of Psychiatry

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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

Table 6 Comparison of recognition performance between artificial intelligence multimodal models and single-modal models

Model	AUC (95%CI)	Sensitivity (%)	Specificity (%)	Youden index
Facial modality	0.782 (0.663-0.901)	72.727	74.194	0.469
Speech modality	0.741 (0.616-0.867)	68.182	70.968	0.391
Text modality	0.756 (0.634-0.878)	70.455	72.581	0.430
Multimodal fusion	0.894 (0.812-0.975)	86.364	83.871	0.702

AUC: Area under the curve; CI: Confidence interval.

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Citation: Zhu YF, Xuan XL, Li YQ. Retrospective analysis of nursing interventions using artificial intelligence multimodal emotion recognition for perioperative depression in head and neck tumors. World J Psychiatry 2026; 16(11): 122781
URL: https://www.wjgnet.com/2220-3206/full/v16/i11/122781.htm
DOI: https://dx.doi.org/10.5498/wjp.122781

Zhu YF, Xuan XL, Li YQ. Retrospective analysis of nursing interventions using artificial intelligence multimodal emotion recognition for perioperative depression in head and neck tumors. World J Psychiatry 2026; 16(11): 122781 [DOI: 10.5498/wjp.122781]

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