精神分裂症的检测和分类:过去十年的系统审查
Arghyasree Saha1, Seungmin Park2, Zong Woo Geem3
1Department of Information Technology, Jadavpur University, Jadavpur University Second Campus, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Salt Lake City, Kolkata-700106, West Bengal, India.
Diagnostics (Basel, Switzerland)
|December 17, 2024
概括
人工智能 (AI) 通过分析复杂数据在精神分裂症 (SZ) 检测方面表现有前途. 这篇评论强调了人工智能.
科学领域:
- 使用先进的算法来分析医疗保健中复杂的大规模数据集.
- 模仿人类认知过程,用于医学分析中的自动决策.
背景情况:
- 精神分裂症 (SZ) 是一种慢性精神障碍,影响全球数百万人.
- 具有幻觉,偏执和认知障碍的特征,影响日常功能.
- 强调在精神卫生保健中急需先进的诊断工具.
研究的目的:
- 系统地审查人工智能 (AI) 在精神分裂症 (SZ) 检测和分类中的应用.
- 在各种神经成像模式中检查机器学习 (ML) 和深度学习 (DL) 方法.
- 综合当前的进展,并确定AI驱动的SZ诊断中的挑战.
主要方法:
- 按照PRISMA 2020指南进行系统审查,涵盖2015-2024年的研究.
- 包括来自多个数据库的同行评审研究.
- 使用脑电图 (EEG),sMRI和fMRI数据对ML和DL技术的评估.
主要成果:
- 在SZ诊断的AI方面取得了重大进展,特别是ML和DL在特征提取和分类中的有效性.
- 人工智能在多模式数据集成中的潜力,以提高诊断准确度.
- 确定了挑战:数据集的局限性,预处理的可变性,以及对模型解释性的需求.
结论:
- 对基于人工智能的SZ预后方法进行全面评估,详细说明其优点和局限性.
- 识别研究缺口和人工智能在SZ检测和诊断中的未来方向.
- 强调人工智能在增强心理健康诊断方面不断发展的作用.
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