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机器学习用于从手术内皮质电图中检测 (非) 性组织.

Sem Hoogteijling1, Eline V Schaft2, Evi H M Dirks3

  • 1Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE, P.O. box 85500, 3508 GA Utrecht, The Netherlands; Stichting Epilepsie Instellingen Nederland (SEIN), The Netherlands; Technical Medicine, University of Twente, Enschede, The Netherlands.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
|September 12, 2024
PubMed
概括

机器学习 (ML) 使用光谱内科外电皮质图 (ioECoG) 功能可以帮助区分组织,特别是在瘤病例中. 这种方法补充但不取代专家对ioECoG数据的临床阅读.

关键词:
生物标志物 生物标志物的手术手术,的手术.可解释的人工智能焦点性 - 焦点性内脑电图 (EEG) 是一种脑内脑电图.夺取自由,夺取自己的自由.

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科学领域:

  • 神经科学是一个神经科学.
  • 医疗技术 医疗技术 医学技术
  • 人工智能在医学中的应用

背景情况:

  • 临床视觉手术内皮质电图 (ioECoG) 读数对于定位组织和改善手术结果至关重要.
  • 机器学习 (ML) 增强ioECoG解释和确定关键预测特征的潜力仍然是积极研究的领域.

研究的目的:

  • 调查ML是否可以补充手术临床ioECoG读数.
  • 在ioECoG分析中确定患者子组如何影响ML表现.
  • 为了确定ML模型使用的最重要的ioECoG光谱特征.

主要方法:

  • 在71名患者 (培训组) 上使用14种光谱特征训练了额外树类分类器 (ETC),在手术后获得了恩格尔1A结果.
  • 将ioECoG通道分类为切除或非切除的组织.
  • 将ETC性能与20名患者的试验组中的临床ioECoG读数进行比较,使用可解释AI (xAI) 识别关键特征.

主要成果:

  • 在20名试验患者中,ETC在14名患者中显示了与临床读数相当或优于临床读数的性能.
  • 在瘤亚组中,ETC取得了最高的表现 (AUC:0.84).
  • 可解释的AI确定了相对的theta,alpha和快速波纹功率作为切除组织的预测因素,以及非切除组织的相对β和马功率.

结论:

  • 微妙的光谱IOECoG变化,人类眼睛无法感知,可以帮助区分健康和病态组织.
  • 结合光谱ioECoG特征的ML模型可以作为临床ioECoG解释的宝贵补充,而不是替代.
  • 在支持脑瘤患者的手术决策方面,ML方法特别有前途.