使用机器学习方法诊断双极性障碍的概述:临床机会和挑战
Walter Antonio Campos-Ugaz1, Jessica Paola Palacios Garay2, Oriana Rivera-Lozada2
1Universidad Privada Norbert Wiener, Lima, Perú.
Iranian journal of psychiatry
|June 29, 2023
概括
机器学习显示出使用脑电图 (EEG) 和磁共振成像 (MRI) 数据诊断双极性障碍 (BD) 的前景. 虽然深度学习可以达到很高的准确性,但临床应用需要进一步的研究.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 精神病学是一个精神病学.
背景情况:
- 机器学习越来越多地用于精神疾病诊断.
- 脑电图 (EEG) 和磁共振成像 (MRI) 是这些方法的关键数据来源.
- 双极性障碍 (BD) 的诊断仍然是一个重大的临床挑战.
研究的目的:
- 提供对用于双相情感障碍诊断的机器学习方法的最新概述.
- 审查使用EEG和MRI数据进行BD检测的现有研究.
- 评估AI在BD诊断中的当前状态和未来潜力.
主要方法:
- 进行了非系统的文献综述.
- 搜索包括PubMed,科学网络和谷歌学者.
- 研究侧重于机器学习,以区分使用EEG和MRI数据的BD与对照.
主要成果:
- 审查了26项研究 (10项EEG,16项MRI).
- EEG研究报告了~90%的准确性;MRI研究报告了~80%的准确性与传统的ML.
- 深度学习方法的准确度超过95%.
结论:
- 应用到EEG和MRI的机器学习显示了BD诊断的概念证明.
- 目前的结果是有希望的,但需要谨慎的解释.
- 在临床实施之前,需要取得重大进展.
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