基于多模式方法的神经和精神健康障碍的分类:综合性综述
Hivi I Dino1, Masoud M Hassan2
1Department of Computer Science, College of Science, University of Zakho, Zakho, Kurdistan Region, Iraq.
Neuroscience and biobehavioral reviews
|October 6, 2025
概括
使用多式联络数据进行深度学习,改善了神经和心理健康障碍的分类. 本综述综合了更准确诊断的进展,挑战和未来方向.
科学领域:
- 神经科学和人工智能 人工智能
- 计算机精神病学和神经病学
背景情况:
- 神经和精神健康障碍对全球健康构成重大挑战.
- 传统的诊断方法往往无法捕捉到这些疾病的复杂性.
- 基于深度学习的多式联络方法通过整合各种数据类型来提供更全面的理解.
研究的目的:
- 为神经和精神健康障碍提供基于深度学习的多模式分类的全面审查.
- 分析最近的进展,核聚变战略和现场挑战.
- 引导未来的研究,以改进诊断工具.
主要方法:
- 系统审查机器学习和深度学习文献.
- 分析多式联运数据集成技术 (神经成像,生物信号,临床数据).
- 对十种主要的神经和精神疾病的分类性能进行了检查.
主要成果:
- 与单模式方法相比,多模式深度学习显著提高了神经和心理健康障碍的分类准确性.
- 确定了各种条件的关键模式和有效的融合策略.
- 突出挑战包括数据不平衡,模型可解释性和数据集要求.
结论:
- 基于深度学习的多式联络方法对推进复杂大脑疾病的诊断有很大的前景.
- 解决数据整合挑战对于临床翻译至关重要.
- 未来的研究应该专注于先进的融合技术和大规模的高质量数据集.
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