人工智能在儿童青少年焦虑和抑郁中应用的Scoping综述

doi:10.3969/j.issn.1006-9771.2024.05.003

Abstract

Abstract:

Objective To review the application of artificial intelligence in anxiety and depression among children and adolescents.

Methods PubMed, Web of Science, CNKI and Wanfang data were searched for literature related to artificial intelligence applied in of anxiety and depression among children and adolescents from January, 2014 to January, 2024, and a scoping review was conducted.

Results A total of ten papers were included, originating from the United States, China, Australia and France. These researches were primarily published in journals related to psychology, public health, clinical medicine, neuroscience and rehabilitation, involving six cross-sectional researches, three longitudinal researches and one interview, including 10 880 participants aged from three to 18 years. The types of data mainly included questionnaires/scales, interview data and brain imaging data. Features related to anxiety and depression were extracted from the data, and artificial intelligence were employed to develop models for the identification or prediction of anxiety and depression in children and adolescents.

Conclusion Artificial intelligence is potential for identifying or predicting anxiety and depression in children and adolescents.

Key words: children, adolescents, artificial intelligence, anxiety, depression, scoping review

CLC Number:

R749.7

SU Rufeng, ZHONG Xiaoke, GAO Xiaoyan, JIANG Changhao. Application of artificial intelligence in anxiety and depression among children and adolescents: a scoping review[J]. Chinese Journal of Rehabilitation Theory and Practice, 2024, 30(5): 513-519.

Figures/Tables 2

Figure 1

Table 1

Basic characteristics of included literature"

纳入文献	国家	年龄/岁	被试数量/人	方法与工具	预测指标	结局指标	人工智能模型/算法
Haque等^[18](2021)	澳大利亚	14~17	n = 6310 训练集 = 4417 测试集 = 1893	横断面研究：从Young Minds Matter数据库筛选出与抑郁最相关的11个特征，使用4种模型评估预测效果	抑郁	准确率随机森林：95% GNB：94% 决策树：95% XGBoost：95%	随机森林 GNB 决策树 XGBoot
Zhao等^[19](2021)	中国	15~18	n = 1029 训练集 = 728 测试集 = 301	横断面研究；人口学特征、PPS; 10i-CDRS; FFMQ; GSES; BFPI; LOT; PHQ-9数据，人工神经网络建立识别模型	抑郁	准确率抑郁：81.06%	人工神经网络
Zhang等^[20](2022)	中国	12~18	n = 56 E = 18 C = 38	访谈：通过面对面访谈收集数据，通过MINI进行抑郁评估，提取全局和局部特征并构建模型	抑郁	准确率融合模型：73.1% 细粒度融合模型：71.75% 粗粒度融合模型：69.59%	粗粒度融合细粒度融合
Jin等^[21](2020)	美国	13~15.5	n = 348 训练集 = 174 测试集 = 174	纵向研究；被试先进行RSFC扫描，然后使用IDAS-II评估被试当时和18个月后的抑郁	抑郁	AUC 内部连接模型：0.78 扩展连接模型：0.72 整脑模型：0.69	神经网络模型
Zhou等^[22](2021)	中国	E1=16.22 E2=16.02 C=16.32	n = 300 训练集 = 150 测试集 = 150	横断面研究；所有被试完成结构性磁共振成像扫描，提取68个脑区皮质厚度作为特征，建立分类模型进行区分	抑郁	准确率 MDD vs. 对照：79.21% SCZ vs. 对照：69.88% MDD vs. SCZ：62.93%	支持向量机
Hawes等^[23](2023)	美国	3~15	n = 748 训练集 = 374 测试集 = 374	纵向研究；从3~15岁，每3年进行一次生理和心理的评估，在15岁时进行抑郁和焦虑的识别	焦虑、抑郁	AUC 抑郁：0.669~0.751 焦虑：0.621-0.788	逻辑回归模型
Xiong等^[24](2021)	澳大利亚	6~16	n = 297 将数据集随机分成5个组，依次作为训练集	横断面研究；通过YODA收集被试的焦虑数据，然后用贝叶斯神经网络方法预测青少年的焦虑	焦虑	AUC 分离焦虑：0.8683 社交焦虑：0.9091 广泛性焦虑：0.8769	贝叶斯神经网络机器学习分类器深度学习分类器特征选择器
Sawalha等^[25](2021)	美国	5.5~9.5	n = 45 训练集n = 36 测试集n = 9	横断面研究：焦虑、非焦虑儿童完成情绪面孔处理任务，进行fMRI扫描，比较焦虑与非焦虑儿童颞极区的神经活动差异	焦虑	准确率右侧颞极区可以最准确地区分焦虑儿童和非焦虑儿童(分类准确率81%)	AdaBoost模型
Haque等^[26](2023)	澳大利亚	4~17	n = 1011 训练集 = 708 测试集 = 303	横断面研究；根据澳大利亚青少年心理健康调查的数据库，用3种机器学习模型检测OCD、SAD和ADHD	OCD、SAD、ADHD	准确率 GNB(OCD)：91% GNB(SAD)：79% 随机森林(ADHD)：91%	机器学习随机森林 GNB
Chavanne等^[27](2023)	法国	14~23	n = 736 训练集 = 580 测试集 = 156	纵向研究：测量14岁时的灰质体积，完成DAWBA、GAD、mAD、SURPS、SDQ、LEQ、NEO-FFI和TCI-R，对18~23岁的焦虑进行预测	GAD、mAD、焦虑	AUC GAD：0.6 mAD：0.52 总体焦虑：0.68	随机森林支持向量机逻辑回归

Table 1

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Application of artificial intelligence in anxiety and depression among children and adolescents: a scoping review

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