使用人工智能的可穿戴设备进行数字表型识别,以鉴定精神疾病和遗传关联
Jason J Liu1, Beatrice Borsari1, Yunyang Li2
1Program in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT 06511, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA.
Cell
|December 20, 2024
概括
穿戴式传感器捕捉详细的行为, 使人工智能能够更好地识别青少年的精神障碍. 这种方法也增强了基因研究,
科学领域:
- 神经科学
- 遗传学
- 数字健康
背景情况:
- 精神疾病是由复杂的遗传和环境相互作用引起的.
- 准确的行为特征在精神病学研究中至关重要但具有挑战性.
- 穿戴式传感器为数量,公正的行为数据收集提供了全新的方法.
研究的目的:
- 利用可穿戴传感器数据和人工智能 (AI) 来改善青少年精神疾病的分类.
- 将来自可穿戴数据的数字表型整合到全基因组关联研究 (GWAS) 中,以加强遗传发现.
- 通过持续的行为表型识别,识别与精神疾病相关的新遗传位置和基因.
主要方法:
- 来自青少年大脑认知发展 (ABCD) 研究的可穿戴传感器和遗传数据的分析.
- 使用超过250个可穿戴设备衍生特征 (数字表型) 的可解释AI框架的开发和应用.
- 实施包含数字表型的单变体和多变体全基因组关联研究 (GWAS).
主要成果:
- 与以前的方法相比,人工智能框架在分类患有精神疾病的青少年方面取得了更高的准确性.
- 确定了16个重要的基因位点和37个与精神病相关的基因,包括ELFN1和ADORA3.
- 证明连续的,可穿戴的特征在GWAS中提供了比传统的病例控制方法更大的检测能力.
结论:
- 可穿戴技术和人工智能可以客观而准确地分类青少年的精神障碍.
- 从可穿戴数据中获得的数字表型显著增强了遗传关联研究的力量.
- 这种综合方法揭示了青少年行为与精神疾病遗传基础之间的新联系.
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