对于注意力缺陷/多动症儿童的人工智能:一个范围审查
Bo Sun1,2, Fei Cai2, Huiman Huang2
1Department of Neonatology, General Hospital of Northern Theater Command, Shenyang, Liaoning, China.
概括
人工智能 (AI) 有助于诊断和分类注意力缺陷/多动障碍 (ADHD). 机器学习,特别是支持向量机和卷积神经网络,在ADHD预测和早期检测方面表现出高准确性.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 医疗信息学 医疗信息学
背景情况:
- 注意缺陷/多动障碍 (ADHD) 是一种流行的神经精神疾病,影响全球5-7%的儿童.
- 人工智能 (AI) 为提高ADHD诊断,预测和分类提供了复杂的工具.
研究的目的:
- 对用于ADHD预测,早期诊断和分类的AI模型和算法进行范围审查.
- 综合当前的研究趋势,并确定ADHD研究中突出的AI技术.
主要方法:
- 根据PRISMA-ScR指南进行全面的范围审查.
- 从1994年的出版物中对52项研究进行了系统的搜索和纳入,这些研究专注于ADHD中的AI应用.
主要成果:
- 人工智能主要用于ADHD诊断 (79%).
- 磁共振成像 (MRI) 是最常见的数据来源 (38%),大多数研究使用数据集<1,000个样本 (54%).
- 机器学习模型,特别是支持矢量机器 (SVM),被广泛使用 (65%),而卷积神经网络 (CNN) 实现了高精度 (98.23%).
结论:
- 人工智能,特别是像SVM和CNN这样的机器学习算法,在提高ADHD诊断和分类的准确性和效率方面显示出显著的希望.
- 进一步利用人工智能的研究可以支持临床决策,并推进对ADHD的理解.
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